Welcome to this week’s Week at Glance. In this weekly segment, I share curated links and reports that will keep you updated on what happened over the week in the the AI, Tech and Science community.

For this week, I have curated links mostly from the AI domain. The credit goes to the original authors and sources, so do check them out the original and support their work.

As a note, all the images and videos are taken from the respective articles — unless mentioned otherwise.

Let’s Begin.

Here is the Top-5 news that happened over the week :

1. Foxconn and Nvidia to collaborate and make AI software for self-driving cars. (Maxwell Zeff, gizmodo.com)

2. Stanford researchers issue AI transparency report. All models scored "unimpressively". (Anna Tong, Reuters)

3. Adobe aims to revolutionize the fashion industry. Uses flexible displays that can create static or dynamic patterns on any surface, even clothes. (Jimmy Pezzone, TechSpot)

4. AI deepfakes create false information in Slovakian vote, turning the deepfake fear into reality. ( Olivia Solon, Bloomberg)

5. Million of Workers Training Million-dollar AI Models for Pennies. (Niamh Rowe, WIRED)

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I. Foxconn and Nvidia to collaborate and make AI software for self-driving cars.

—Maxwell Zeff, gizmodo.com

This reveals that Nvidia — a dominant supplier of AI and graphics hardware and software, and Foxconn — the world's largest contract manufacturer of electronics, are teaming up to create AI factories for self-driving cars. This announcement, made at the Hon Hai Tech Day earlier this week, shows Nvidia’s plans and vision to dig deep into the AI industry — including that of Self-driving cars.

Nvidia CEO Jensen Huang and Foxconn CEO Young Liu shared their vision for the future of AI at the event. These AI factories would use Nvidia's GPU to process, train and transform large amounts of data into AI models. The infrastructure will be powered by Nvidia's GH200 Grace Hopper Superchip, which are designed for high-performance computing and AI applications.

GH200 Grace Hopper is a combination of CPU and GPU chips, with faster memory, that is designed for giant-scale AI and high-performance computing.

“There will be smart factories that build cars, there will be smart factories that build AIs, and these two factories will be compliments of each other,” said Huang in the event.

Huang believes that every industry will need AI factories in the future. Liu, on the other hand, said that Foxconn is transforming into a platform solution company that focuses on smart cities, smart manufacturing and smart EVs.

Matter of fact, Nvidia and Foxconn are both undergoing transitions in their businesses after the onset of AI.

Nvidia is moving from a graphics chip maker to a data center-scale computing company. Whereas, Foxconn is moving from a contract electronics maker to a platform solution provider.

The AI factory will develop software for cars like the Foxtron Model B, which Foxconn showcased behind Huang and Liu during the announcement.

II. Stanford researchers issue AI transparency report. All models scored "unimpressively".

—Anna Tong, Reuters

A team of researchers from Stanford University created the Foundation Model Transparency Index — a report that evaluates the transparency of popular AI foundation models from different companies.

The index graded 10 popular models on 100 different transparency indicators to measure how much information the model developers provide about the data, compute resources, human labour, and other aspects of their models.

The result: All AI models scored “unimpressively”.

The report found wide holes when it comes to transparency while building these AI models.

“It is clear over the last three years that transparency is on the decline while capability is going through the roof,” said Stanford professor Percy Liang, a researcher behind the Foundation Model Transparency Index

The report found that most models lack sufficient documentation and disclosure.

Many of the important aspects, such as data sources, data quality, model architecture, model training and evaluation, and model governance are not fully disclosed. This report, in some way, highlights the high need for regulations in AI governance.

These are the final positions of each model in terms of transparency and, as highlighted by the report, the results are not very encouraging.

The highest-scoring model, Llama 2 from Meta, only achieved a score of 53 out of 100. The lowest-scoring model, Titan from Amazon, scored 11 out of 100.

OpenAI's GPT-4 model — the ‘brain’ of ChatGPT and the most powerful and versatile AI foundation model ever created — scored 47 out of 100.

The researchers behind the report warn that the lack of transparency in AI foundation models poses serious challenges and risks for society.

The report also presents that transparency can benefit the model developers themselves, by fostering trust, collaboration, innovation, and feedback from the AI community and the public.

The researchers hope that their report will serve as a useful resource and a catalyst for change in the AI field.

III. Adobe aims to revolutionize the fashion industry, uses flexible displays that can create static or dynamic patterns on any surface, even clothes.

— Jimmy Pezzone, TechSpot

Adobe's latest innovation Project Primrose aims to merge technology and fashion.

Project Primrose, unveiled at the Adobe Max conference in Los Angeles, showed the potential of this technology with an interactive dress. In the event, Adobe research scientist Dr Christine Dierk demonstrated how the dress could change its appearance based on the user's input, such as voice commands, gestures, or touch.

Project Primrose is a digital dress that is made up of small scales or petals. These scales are programmed with Adobe software, which changes its design on user interaction and command. This futuristic "fabric" can be used for clothing, handbags, and other accessories

“Unlike traditional clothing, which is static, Primrose allows me to refresh my look in a moment,” says Dr. Dierkt. "Fashion doesn’t have to be static. It can be dynamic and even interactive. And we’re excited for a future where there’s more ways to express yourself,"

According to Adobe, user could choose from a variety of designs created with Adobe Stock, After Effects, Firefly, and Illustrator, or even create their own custom patterns.

Project Primrose is still in the early stages of development and is not yet available for consumers. However, the company hopes to inspire more artists and designers to explore the possibilities of this technology and create new forms of expression and communication

IV. AI deepfakes create false information in Slovakian vote, turning the deepfake fear into reality.

— Olivia Solon, Bloomberg

As Slovak parliamentary elections are approaching, some political actors are using AI-generated deepfake voices to spread false and misleading information about their opponents.

These videos are circulating on social media platforms such as Facebook, Instagram and Telegram, without any warning labels or fact-checking.

Touted as “the first swung by deepfakes” by the Sunday Times, a deepfake video showing a conversation between Michal Simecka, the leader of the Progressive party, and a journalist went viral on Meta; in which they allegedly discussed buying votes from the Roma minority.

The audio was synthesized by an AI tool that mimicked the speakers' voices.

“For three or four years everyone has been talking about the wave of deepfake manipulation.. but for whatever reason it didn’t happen. But there’s reason to think it could be different now,” said Rolf Fredheim, who conducted the research for Reset, who reported this deep-fake video.

Another example, according to the report, showed a video posted by Republika, a far-right political party, that features AI-generated voiceovers of Simecka and President Zuzana Caputova, urging voters not to follow "the progressive herd blindly".

These videos are still available on Facebook and Instagram as I am writing this.

Rolf Fredheim urged social media platforms and authorities to take action to prevent the spread of disinformation and protect the integrity of the election.

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V. Millions of Workers Training Million-dollar AI Models for Pennies.

—Niamh Rowe, WIRED

AI is transforming the world, but at — literally — what cost?

This article by WIRED highlights what happens behind the scenes: millions of workers working tedious, for low-paid tasks to label data for training AI algorithms.

These workers are often exploited, overworked, and underpaid by platforms that profit from their labour.

The article begins by anchoring the story of Fuentes, a Venezuelan woman who fled her country's economic and political crisis. She works for Appen, an Australian company that provides data services for AI clients.

Appen pays Fuentes between 2.2 cents to 50 cents per task, depending on the complexity and availability of the work. Sometimes, she barely make $1 a day.

“Appen’s clients have included Amazon, Facebook, Google, and Microsoft, and the company’s 1 million contributors are just a part of a vast, hidden industry. The global data collection and labeling market was valued at $2.22 billion in 2022 and is expected to grow to $17.1 billion by 2030, according to consulting firm Grand View Research.” highlighted the article

The report further delves into the insights by the World Economic Forum, which says there are an estimated 260 million online workers worldwide, many of whom are doing data labelling for AI.

These workers are mostly located in developing countries — the likes of Venezuela, India, and the Philippines — where labour costs are low and regulations are weak. They face poor working conditions, lack of benefits, and constant uncertainty.

The article further highlighted:

“Mutmain, 18, from Pakistan, who asked not to use his surname, …says he joined Appen at 15, using a family member’s ID, and works from 8 am to 6 pm, and another shift from 2 am to 6 am. “I need to stick to these platforms at all times, so that I don't lose work,” he says, but he struggles to earn more than $50 a month.”

Some researchers have criticized this system as a form of data colonialism, where rich countries and corporations extract data and value from poor countries and workers.

They argue that this creates a digital divide and reinforces existing inequalities. They also question the ethics and quality of the data produced by such a system.

That’s it for this week. Hope you enjoyed reading them as much as I did. If you have any feedback, suggestions, or questions, feel free to hit reply and let me know.

If you find this newsletter helpful, share this newsletter with others.

Thanks for reading and see you in the next post

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

The growth of ChatGPT has been dramatic over the years. Recently, OpenAI announced that ChatGPT can now hear, see and speak.

The multimodality of ChatGPT has taken a new form.

On November 2023,

OpenAI’s ChatGPT appeared on the internet. Two months after that, with over 100 million users, it attained the title of the fastest-growing consumer software application in history. The nonprofit company saw the opportunity to make a profit, so it did.

The profits came from their freemium service, but most of those profits and funds largely went into paying their bills — thanks to the hungry resource demands of the LLM models.

On March 14, 2023,

The launch of GPT 4 cemented the name of OpenAI in the superintelligence utopia — which became a key player in extending the boundaries of AI and NLP technology further.

Other big companies showed interest too. Everybody started to extend this boundary further. At the same time, most of these tech companies made hefty profits from this revolutionary field of AI.

ChatGPT, which was on life support of billions of dollars from companies like Microsoft — can finally see, hear and talk.

Metaphorically, it is alive.

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I.

Voice: When ChatGPT Speaks

Watch this demo video by OpenAI, in which they reveal the new multimodal features inside the ChatGPT app:

This looks like a "Hello World" moment for ChatGPT — and it is alive, thanks to its new multimodal upgrade.

Through voice, users can send instructions to ChatGPT. ChatGPT would then respond in a seemingly natural voice. The new voice feature has very well promoted ChatGPT to a voice assistant. A powerful voice assistant in fact.

“We collaborated with professional voice actors to create each of the voices. We also use Whisper … to transcribe your spoken words into text,” said OpenAI in their annoucement post.

Whispher is a speech recognition system by OpenAI which is trained on 680,000 hours of data.

In the demo shared by OpenAI, the user asks ChatGPT app to tell a bedtime story about a hedgehog — to which it responds by telling a story. It sounds similar to chatGPT — literally sounds — and as reported by ZDNet, it is similar to how voice assistants like Amazon's Alexa function.

Matter of fact, rumours are Alexa is planning to integrate Generative AI like GPT4 to make its voice assistant more reliable and, well, smart.

II.

Image: When AI See

In the demo by OpenAI, the user asked ChatGPT to fix their bike by sending the images of the bike to the app. ChatGPT ‘looked’ at those images, and came up with a solution to fix the bike1.

Things got interesting when ChatGPT was able to correlate the instruction manual and tools and was able to guide the user on how to really fix the bike.2

The image input feature can be helpful in so many different situations: identifying objects, solving a math problem, reading an instruction manual, or (of course) fixing a bike. The ability to see images can greatly improve visual tasks that require analysis.

One interesting application of this feature is leveraged by a Danish startup called Be My Eyes.

Be My Eyes creating technology for over 250 million people who are blind or have low vision since 2012. They are using GPT-4 to aid these differently-abled people, and for this, they developed a GPT-4 powered AI version of their former Virtual Volunteer™ app.

This allows the Be My Eyes App — which is already assisting blind pupils with their challenges — to be better and more reliable.

According to OpenAI, Be My Eyes can benefit a lot of users as they can now interact with an AI assistance that — thanks to the image capability — allows them to know about their surroundings well.

“Image understanding is powered by multimodal GPT-3.5 and GPT-4. These models apply their language reasoning skills to a wide range of images, such as photographs, screenshots, and documents containing both text and images”, says OpenAI in a blog.

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III.

Safety: When ChatGPT (tries to) become safe

OpenAI conducted beta testing and "red teaming" to explore and mitigate risks.

This allows ChatGPT to be nearly safe, if not completely.

Not too long ago, OpenAI published a paper describing their testing efforts with GPT-4V. GPT-4V, stemming from the word GPT-4(V)ision, is a GPT-4 model to analyze image inputs provided by the user.

The primary goal, in OpenAI’s own words, was to "gain additional feedback and insight into the real ways people interact with GPT-4V."

The paper gives us a taste of the risks in the multimodal nature of GPT4.

OpenAI's positive evaluation shows that ChatGPT was able to avoid harmful content. It seems to refuse to generate AI images that includes real people. Moreover, the GPT4-V also refused to identify people in images.

However, the negative evaluations show that GPT-4V is still bound to generate disinformation, break CAPTCHAs, or geolocate images.

Building on top it, OpenAI says the following:

“…Tasks such as the ability to solve CAPTCHAs indicate the model’s ability to solve puzzles and perform complex visual reasoning tasks. High performance on geolocation evaluations demonstrate world knowledge the model possesses and can be useful for users trying to search for an item or place” says OpenAI in its GPT-4V(ision) System Card report highlights

Thanks to AI, gone are the days of CAPTCHAs.

OpenAI found one interesting finding. GPT-4V is quite good at refusing of image-based "jailbreaks."

Image jailbreaking is a term that refers to the process of modifying an image generator AI model (midjourney, dalle3, etc) to bypass its built-in limitations or restrictions.

It is a form of hacking (more of tricking) these image models into generating sensitive images, either by exploiting their flaws or by manipulating their inputs.

From the below graph by OpenAI, we see how GPT-4 was able to achieve the jailbreak refusal — with a refusal rate of more than 85%

The graph compares three variations of GPT4: GPT-4 Release, GPT-4V, and GPT-4V + Refusal System. 3

OpenAI also engaged "red teams" to test the model's abilities in scientific domains, such as understanding images in publications, and its ability to provide medical advice given medical images such as CT scans.

So is this reliable? Of course not.

OpenAI's conclusion on this is clear: "We do not consider the current version of GPT-4V to be fit for performing any medical function.”

So the image capability isn’t fully reliable yet. However, it’s a big leap nonetheless.

OpenAI in its blog mentioned that these new features would come slowly — citing safety concerns.

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IV.

Where are we landing on the AGI Dreams?

OpenAI's latest additions to ChatGPT are nothing short of remarkable. Multimodality is the path on which OpenAI has to go if it wants to achieve the AGI.

Will it achieves AGI, or not, that is up for debate. How do we know if AGI is here? Frankly, it isn’t even clear to many AI experts themselves.

But in loose terms, we may know what’s AGI: Artificial General Intelligence (AGI) is just a theoretical term that refers to an AI that is at par with humans in terms of their cognitive abilities.

There is one difficulty though, there is no way to pinpoint a certain time in future where we can say — AGI has been achieved.

But taking cues from the past, it seems like anytime a computer outsmarts a human, we get closer to AGI.

Deep Blue beat Kasparov in Chess — AGI is near. AlphaGO beats the world go champion — AGI is near. AI started to outperform humans on various aptitude tests — AGI is near.

AI now seems to outperform humans when it comes to creativity. And now, everybody seems to believe AGI is near.

However, the AGI becomes far whenever we find a fault within these AI systems. Hallucination, misinformation, and bias; you know it. Even when we have the largest and strongest AI model, these caveats form the roadblock on our supposed AGI journey.

To our annoyance, many points out saying that these shortcomings of AI are fundamental, and intrinsic — with no cure.

However, quite interestingly, we have some instances where humans seem not too bad in front of AI after all.

The widely circulated report, which said AI outperformed humans on creativity tests, didn’t show a significant outperformance. AI was certainly at par, but not always the best most of the time. Moreover, the story is pretty interesting in the AlphaGo case. In a dramatic display of ‘revenge’, Kellin Pelrine, who was an American research scientist intern at FAR AI, defeated AlphaGo at Go — by apparently exploiting a weakness in the system.

I feel the multimodality of AI is the way to go if our destination is AGI. And even if we can’t achieve it in the near future, we might get close to AGI.

Integration of voice input and output, image recognition, and a commitment to safety leads to a ChatGPT that is continuously evolving — becoming a more versatile and reliable AI assistant. The ability to make inferences by analysing the surroundings is very close to how humans also learn.

These features open up a world of possibilities, from hands-free interaction to solving visual problems.

Moreover, ChatGPT would soon be capable of searching the internet inside the ChatGPT window.4 These features, as of now, will soon be available to all users and developers. According to OpenAI, it would slowly roll out all the features — with the ChatGPT Plus and Enterprise users as priority.

The Browser functionality — though currently only available for Plus and Enterprise users — would be available for all the users pretty soon, according to a statement by OpenAI.

If multimodality is the path that we all are walking on, then it’s safe to assume — the AGI is near.

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

Philip Milton Roth was a prominent American novelist and short story writer. Once, he quoted: When you publish a book, it's the world's book. The world edits it.

Well, little did Roth know, this quote is no longer valid in the present times.

Or, if it has to make sense anyway, the alternate quote should have been: when a human publishes a book, it’s the world’s book. The humans of the world edit it.

It won’t take much to offend him, change the word publish to generate in the quote and here you go, the sentence still makes sense — thanks to the extraordinary world of AI we are living in.

I don’t know about you, but I am sure Amazon can resonate with what I just did there.

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I.

Three books a day keep the AI at bay — and putting AI writers to dismay

In a post that Amazon published on Sept-18 2023, it wrote:

… in order to help protect against abuse, we are lowering the volume limits we have in place on new title creations.

According to the Guardian, the limit is set at three titles (however, the volume — according to the post — might get adjusted in some cases). This means that the authors cannot self-publish more than 3 books using Amazon KDP — the self-publishing service for print and digital books on Amazon.

Gizmodo reported that this decision came just after the release of the book Fire and Fury: The Story of the 2023 Maui and its Implications for Climate Change, which is a self-published book about the Maui wildfires.

The special thing about this book? The ‘non-fiction’ book came 2 days after the actual wildfire broke out. The story was written quickly containing around 86 pages, the author is anonymous, and the actual sources are missing.

Seems suspicious? Sure it does.

Sarah Bean, a marketing manager for self-publishing book service BookLaunchers, told Forbes that this book is speculated to be written by AI.

“The lack of a biography on the author, the bad reviews, the short page count, and the quick turnaround time all raise alarms,” says Sarah in the Forbes report.

This book is just one out of a plethora of other AI-generated books in the Amazon.

The rise in the number of AI-generated books is obvious. You can see this for yourself by going here. There are books which are coauthored (or authored in some cases) by ChatGPT, listed in sections that span 13 pages on Amazon. This accounts for nearly 200 books that are, either directly or indirectly, generated by AI — either fully or partially.

So where are we going with this? Will there now be two souls — one conscious and other algorithmic — in writing?

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II.

What does this mean for the ‘human’ Writers?

Well, apart from putting the word ‘human’ in the heading of this section, the onset of generative AI has had an impact on the writing sector for a while.

Right now, as I am writing this, the amount of AI-generated books on Amazon is over 200. Some of them even made it to the bestsellers list.

What does this mean for writers? Well, it all comes down to how writers present themselves and stand out in the era of AI.

Firstly, now it’s more than important for authors to create their own identity. They need to, in simple words, create a personal identity/brand for themselves that their audience can resonate with.

Alex Mathers1 has done quite well in educating budding writers about the importance of personal branding. Similarly, Scott Alexander2 is also a prime example of how authors can still carry their own identity even under a pseudonym.

Secondly, creating emotion-driven writings has been crucial. Writings that carry a unique style that communicates directly with the emotions of the readers are something that will truly separate writers from AI generators. This is true for technical and non-fiction writings as well.

Humans were, and still are, ahead of AI in terms of emotions. The human touch cannot be replaced by interpreted algorithm codes.

Both of the above factors—personal branding and emotion-driven writing — are crucial for the next generation of writers. Humans write for humans. One puts their entire knowledge into the margins of a paper. Other reads from it and grow their knowledge. If this is not a direct analytical connection among humans, its an emotional connection nonetheless.

Of course, we don't need humans to write impactful stories for computers because frankly, it’s of no use to them; unless you are training an AI model to be the next ChatGPT.

The model would instead appreciate if you transfer your knowledge in an emotion understood by them: in the language of algorithms, weights, and data.

III.

Millions of Neural Nodes vs. Million Million Brain Neurons

Remember the above quote by the famous physicist Stephen Hawking? He wasn’t wrong.

Most people began to like Hawking after reading his book ‘The Brief History of Time”. It is one of my favourite books, and so is the case with millions of people around the world. People like his works, partly to understand how the universe worked, but also partly because people want to know how he — as an arguably genius astrophysicist — understood the universe.

Practically, The Brief History of Time is no better than an average undergraduate textbook in astrophysics. But given a choice to spend your boring evening with one of either book, you would probably choose Brief History.

Why? The readers find an emotional bond with Hawkings, whether it is his humourous storytelling about the universe, or his unique way of telling numbers (like, saying billion billion billion stars instead of standard 10^27 stars), or his unique way of distilling complicated physics concepts into simple matter (no puns). The reader is taken on a journey with Hawkings: a journey inside his mind.

And that is the true essence of reader-writer relations!

An engaging book tends to perform better than a non-engaging book. Ever seen Euclid’s book Elements at the top of the charts? Well, fun fact, it is one of the most printed books after the Bible, with over 1000 editions issued.

This tells us one crucial thing about publishing a book. It is nothing but the act of dispersing and absorbing the knowledge. Great books have great potential to convey facts and information.

A million-token-powered AI machine seldom captures the thought made by million million neurons of the human mind. Sure, we got to see that it outperforms human in various creative task and solve captchas with better accuracy.

But even after that, even after it seems to beat the creativity of humans at multiple levels3 , the real question is: can it beat the reader-writer relationship?

Humans have an emotional touch while doing anything. We put ‘ambitions’ and ‘purpose’ into everything we do; which is us romanticising and putting emotions into doing things we love.

Similarly, the same trait is exhibited while writing a book. Whether it is the reader reading about the fantasy wonders of a wizard school; or the seemingly not-so-brief history of time and universe; we yearn for that enjoyment.

I write this newsletter because I enjoy it. And you read the newsletter because you enjoy reading too. Can AI mimic that? No, they haven’t (yet).

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IV.

What’s next

Writers of now — as well as the writers of the future — need to learn how to tackle AI. They can either embrace AI and increase their productivity (which is possible only if it’s done right), or ignore AI and focus on honing their own crafts and outperforming it (which is possible only if it’s done perfectly).

Amazon's three-books-a-day policy seems good in a way. However, its effectiveness is shown only if strict reviews and conducts are being done to detect AI-generated work.

Amazon’s policy requires the authors to label their work as AI-generated if they have used the help of AI. The company policy says the following:

“We require you to inform us of AI-generated content … when you publish a new book or make edits to and republish an existing book through KDP”

Letting readers know whether they are reading an actual book or an AI-generated book, is important.

This is not to discourage the writers who want to "generate" their books — even Amazon doesn’t penalise authors who do that — but labelling books in such a way allows the readers who choose to read the book consciously to know whether the book is AI-generated or not.

According to a Guardian report, Amazon said the following in a statement earlier this month:

“Beginning today, when you publish a new title or make edits to and republish an existing title through KDP, you will be asked whether your content is AI-generated.”

The timing of this perfectly aligns with the ongoing struggle of writers over AI-generated works.

Recently, the Hollywood writer’s strike came to an end after gaining their first-ever protection against AI in its memorandum of agreement.

The emotions ran high. Reacting to this, an author tweeted the following:

"As an author, I genuinely want to kiss the feet of the WGA who got the studios to commit that AI isn't writing… Stuff like this tends to trickle down through industries."

In a Forbes article, a Los Angeles-based entertainment and employment attorney Camron Dowlatshahi, holds the opposing opinion about AI:

"AI is about to perform amazing feats in many industries, and Hollywood is no exception..”

However, according to the same article, he believed that AI lacks at — guess what— emotions.

“AI is still not pumping out scripts that are as good or entertaining as the jokes and plotlines written by humans”

Observing the strikes, and the dissent on the AI-generated article, three things are clear to me —

• The impact of AI can’t be ignored. More and more AI-generated books would flow throughout the internet. However, readers have to consult these books at their own risk.

• Human emotions are still prevalent. This is true for every sphere in one’s life. AI cannot replicate that. Naturally, we are drawn to stuff in which we have instilled our emotions.

• Transparency is the key. Just like Amazon urge its writers to label their published work, other institutions must disclose whether they have used AI or not. This ensures the responsible use of AI.

Using AI is not necessarily bad. However, not disclosing whether your work is AI-generated or not, is where — in my opinion — the ethical lines are really crossed.

If, from the community of writers, I were to rewrite the quote in the tone of Rothe, and the format of another famous AI-detected work, the US preamble, it would be as follows:

A published book should be of the people, (mostly) by the people, and to the people.

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Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

Well, let us play a game, shall we? How many people do you know from the above image?

Done with the guessing? How much did you get?

Well, in his post, I will introduce you to all of these people — who are they, and why they are such a big deal?

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I.

Time magazine's 1st-ever Top 100 AI

SAM JACOBS, the Editor-in-Chief of Times Magazine, wrote:-

Behind every advance in machine learning and large language models are, in fact, people—both the often obscured human labor that makes large language models safer to use, and the individuals who make critical decisions on when and how to best use this technology.

Reporting on people and influence is what TIME does best.

This group of 100 individuals is in many ways a map of the relationships and power centers driving the development of AI.

— How We Chose the TIME100 Most Influential People in AI

The cover that you just saw is the cover for the first-ever Time100 AI list — which included the names of the top 100 people who contributed to the developments and advancements of AI.

The Times magazine, since its inception in 1923, has been covering news in various domains — National news, Health, Science, Business, and so on. Around 70 years after that (in the year 1999 to be exact) Time started its TIME100 segment — which is an annual top 100 list that contains influential people around the world, selected by the editors at Time.

The list covers various categories, such as leaders, artists, innovators, icons, and titans. The list also features special honorees, such as

• the Person of the Year — an annual title awarded by Time Magazine to a person (or a group of people) who had the most significant impact on the world in the past year.

• the Heroes of the Year — given to a diverse group of individuals who have made a positive impact on the world.

• Next Generation Leaders — given to individuals who shape the world and define the next generation of leadership.

The TIME100 AI list includes Tech CEOs, researchers, policy advisers, and artists who have made significant contributions to AI. The list on the Times Website is sorted into four categories — Leaders, Innovators, Shapers, and Thinkers.

This list is special, not because it contains people who have contributed to AI in terms of algorithms or AI applications — as a matter of fact, most of these people are not even CEOs of Tech companies. The list also includes personalities that contribute to the intellectual, ethical, and social dynamics that drive AI’s staggering evolution.

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Without further ado, let’s see who made it to the first-ever cover of TIME100 AI.

II.

The People in the Cover

(and your Answer key for the game)

Well, to make things easier, I have marked these people on the cover — so that you can refer to it easily.

Here are the names:

1. Sayash Kapoor — Professor & Doctoral Candidate, Princeton University

2. Arvind Narayanan — Professor & Doctoral Candidate, Princeton University

3. Yann LeCun — Chief AI Scientist Meta

4. Yejin Choi — Professor, University of Washington

5. Abeba Birhane — Cognitive Scientist

6. Geoffery Hinton — Emeritus Professor, University of Toronto

7. Elham Tabassi — Associate Director of Emerging Technologies, NIST

8. Meredith Whittaker — President, Signal

9. Ian Hogarth — Chair of the U.K.’s Frontier AI Taskforce

10. Alondra Nelson — Researcher at the Institute for Advanced Study and Policy Adviser

11. Pelonomi Moiloa — CEO and Co-Founder, Lelapa AI

12. Reid Hoffman — Entrepreneur and Investor

13. Clément Delangue — CEO and Co-Founder, Hugging Face

14. Sam Altman — CEO, OpenAI

15. Demis Hassabis — CEO and Co-Founder of Google DeepMind

16. Noam Shazeer — CEO and Co-Founder of Character.AI

17. Audrey Tang — Minister of Digital Affairs, Taiwan

18-20. Dario and Daniela Amodei — CEO & President, Anthropic

21. Grimes — Musician

22. Rumman Chowdhury — CEO and Co-Founder, Humane Intelligence

23. James Manyika — Senior Vice President, Research, Technology & Society, Google

24. Paul Christiano — Founder, Alignment Research Center

25. Yi Zeng — Professor, Chinese Academy of Sciences

26. Margrethe Vestager — Executive Vice President, European Commission

27. Fei-Fei Li — Professor, Stanford University

28. Verity Harding — Director of the AI & Geopolitics Project, Cambridge University

29. Sneha Revanur — Founder and President, Encode Justice

Let’s explore who they are, and why are they celebrated by the AI community.

1. Sayash Kapoor — Professor & Doctoral Candidate, Princeton University

Sayash Kapoor is a Professor and Doctoral Candidate at Princeton University. He is a leading researcher in the field of AI and has made significant contributions to the development of machine learning algorithms. He is a recipient of the best paper awards at ACM FAccT. He is currently co-authoring a book on AI Snake Oil with Arvind Narayanan.

Next up is - Arvind Narayanan.

2. Arvind Narayanan — Professor & Doctoral Candidate, Princeton University

Arvind Narayanan is a Professor and Doctoral Candidate at Princeton University. He is a renowned computer scientist who has made significant contributions to the field of AI, particularly in the areas of privacy and security. In 2019, he gave a famous talk titled “How to recognise AI snake oil” — which resulted in the creation of this sub stack → along with . They are also creating a book on the same topic, which will hopefully be published in 2024.

3. Yann LeCun — Chief AI Scientist Meta

Yann LeCun is the Chief AI Scientist at Meta. He is a pioneer in the field of deep learning and has made significant contributions to the development of convolutional neural networks, which are used in image recognition. He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN) and is a founding father of convolutional nets.

LeCun received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. The trio are often entitled as “Godfathers of AI” — and all the Godfathers made it to the TIME100 list. Two of them have been featured in this magazine magazine as well.

4. Yejin Choi — Professor, University of Washington

Yejin Choi is a Professor at the University of Washington. She is a leading researcher in natural language processing and has made significant contributions to the development of machine learning algorithms that can understand human language.

One of her notable works was when she developed computational models that can detect biases in language that work against people from underrepresented groups

5. Abeba Birhane — Cognitive Scientist

Abeba Birhane is a Cognitive Scientist who works as a Senior Advisor in AI Accountability at Mozilla Foundation and an Adjunct Assistant Professor at Trinity College Dublin.

She has made significant contributions to the development of auditing publicly accessible AI training datasets.

6. Geoffery Hinton — Emeritus Professor, University of Toronto

Geoffrey Hinton is an Emeritus Professor at the University of Toronto. He is a leading researcher in deep learning and has made significant contributions to the development of neural networks. He is a a leading figure in the AI community, who is known for his revolutionary work on artificial neural networks.

With David Rumelhart and Ronald J. Williams — both pioneers in the development of neural networks — Hinton co-authored a “Learning representations by back-propagating errors” paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks.

And if you are wondering, then yes — he is the second “Godfather of AI” featured in this list.

7. Elham Tabassi — Associate Director of Emerging Technologies, NIST

Elham Tabassi is an Associate Director of Emerging Technologies at NIST. She is a leading researcher in AI and has made significant contributions to the development of standards for AI systems.

She worked on various machine learning — predominantly computer vision and biometrics — as a member of NIST.

8. Meredith Whittaker — President, Signal

Meredith Whittaker is the President of Signal Technology Foundation, an organization that advocates for ethical AI practices. And yes, this is a foundation which is by its subsidiary — Signal Messenger LLC, that created the popular Signal messaging app.

Earlier, she worked with Google for 13 years, founded Google Open Research Group, and co-founded the M-Lab.

She has made significant contributions to the development of ethical guidelines for AI systems. She is (also) the co-founder and former faculty director of the AI Now Institute at NYU, a leading university institute dedicated to researching the social implications of artificial intelligence and related technologies.

9. Ian Hogarth — Chair of the U.K.’s Frontier AI Taskforce

Ian Hogarth is an investor and entrepreneur by profession. He is the Chair of the U.K.’s Foundation AI Taskforce — which analyses and studies the risks of artificial intelligence. He has made significant contributions to the development of policies that promote innovation in AI.

He has his own blog, where wrote a post on AI Nationalism which discusses the rise of machine learning influencing the domains of geopolitics.

10. Alondra Nelson — Researcher at the Institute for Advanced Study and Policy Adviser

Alondra Nelson is an American Academic and Researcher at the School of Social Science at the Institute for Advanced Study in Princeton, New Jersey. She has made significant contributions to the development of policies that promote equity and fairness in AI systems.

From 2021 to 2023, Nelson was deputy assistant to President Joe Biden and principal deputy director for science and society of the White House Office of Science and Technology Policy (OSTP).

11. Pelonomi Moiloa — CEO and Co-Founder, Lelapa AI

Pelonomi Moiloa is the CEO and Co-Founder of Lelapa AI, an organization that uses AI to solve social problems in Africa. The company’s first major project, Vulavula, uses AI to assist with the natural language processing of African languages that are often neglected by other researchers—Zulu, Sotho, and Afrikaans are spoken by some 25 million people. the project Moiloa is most passionate about is training AI models to properly pronounce South African names.

She has made significant contributions to the development of automated AI systems that can address social issues.

12. Reid Hoffman — Entrepreneur and Investor

Reid Hoffman is an Entrepreneur and Investor who cofounded LinkedIn in 2003. He has made significant contributions to the development of AI startups — after launching his own AI startup called Inflection AI along with DeepMind co-founder Mustafa Suleyman and former DeepMind researcher Karén Simonyan.

He made headlines in the AI community when he once said pausing AI development is 'foolish' and 'anti-humanist'.

13. Clément Delangue — CEO and Co-Founder, Hugging Face

Clément Delangue is the CEO and Co-Founder of Hugging Face, an open-source organization that acts as a platform for researchers to share their AI models, datasets, etc. with the world.

He has also co-authored many AI-related papers and has significant contributions to the development of natural language processing algorithms.

14. Sam Altman — CEO, OpenAI

Often dubbed as the poster boy in the AI community, Sam Altman is the CEO of OpenAI, an organization that develops advanced AI models — famously known for developing the ChatGPT and GPT model series. Earlier, He was the president of Y Combinator.

He has led the development of deep learning interestingly — as ChatGPT became a medium for common people to realise the true power of AI.

15. Demis Hassabis — CEO and Co-Founder of Google DeepMind

Demis Hassabis is the CEO and co-founder of Google DeepMind, a world-leading artificial intelligence research company. He is also a renowned computer scientist, neuroscientist, and entrepreneur. He has been recognized as one of the most influential people in the field of AI and has received numerous awards and honours for his contributions.

He is passionate about creating general-purpose

16. Noam Shazeer — CEO and Co-Founder of Character.AI

Noam Shazeer is the CEO and co-founder of Character.AI, a company that develops cutting-edge natural language processing technologies. He has over 20 years of experience in the field of artificial intelligence, having worked at many AI giants, such as Google, OpenAI, and DeepMind.

He is also a prolific researcher — who has more than 100 publications in top-tier conferences and journals. He is widely recognized as one of the pioneers of transformer models, attention mechanisms, and neural architecture search.

17. Audrey Tang — Minister of Digital Affairs, Taiwan

Audrey Tang is the Minister of Digital Affairs of Taiwan, a position she has held since 2016. She is a self-taught programmer, hacker, and civic activist who advocates for open government, social innovation, and digital democracy.

She is also the first transgender minister in the world and one of the youngest cabinet members in Taiwan's history. As the Minister of Digital Affairs, she is responsible for overseeing the development and implementation of digital policies and initiatives that enhance public participation, transparency, and accountability in Taiwan's government.

18-20. Dario and Daniela Amodei — CEO & President, Anthropic

Dario and Daniela Amodei are the CEO and President of Anthropic, a research institute dedicated to creating and understanding large-scale artificial intelligence systems that can align with human values and goals.

Dario is a former research scientist at OpenAI, where he led the team that developed GPT-3, one of the most advanced language models in the world.

Daniela is a former product manager at Google, where she worked on various projects related to natural language processing, computer vision, and machine learning.

Together, they founded Anthropic in 2021 with the vision of building trustworthy and beneficial AI for humanity.

21. Grimes — Musician

Grimes is a Canadian musician, singer, songwriter, and producer who has been involved in various projects related to artificial intelligence. She has collaborated with OpenAI on several occasions to spread awareness about AI.

She once co-created an AI-generated lullaby with OpenAI's MuseNet — a deep neural network that can generate music in different styles and genres— and showed the usage of generative AI in creative fields like music.

She also participated in the launch of OpenAI's Jukebox, a neural network that can generate music and lyrics from scratch or based on existing songs.

She has also explored the themes of AI, transhumanism, and the future of humanity in her music and visual art.

22. Rumman Chowdhury — CEO and Co-Founder, Humane Intelligence

Rumman Chowdhury is the CEO and co-founder of Humane Intelligence, a company that aims to create ethical and responsible artificial intelligence solutions for various domains.

She has a background in data science, social science, and business, and has worked as a senior principal at Accenture and a director of data science at Metis.

She is also a TEDx speaker, a Forbes contributor, and a member of the Forbes Technology Council. She has been recognized as one of the most influential women in AI by Forbes, Fast Company, and Wired.

23. James Manyika — Senior Vice President, Research, Technology & Society, Google

James Manyika is the Senior Vice President of Research, Technology and Society at Google, where he oversees the company's efforts to advance the state of the art in artificial intelligence, quantum computing, biotechnology and other fields.

He also leads Google's initiatives to ensure that these technologies are developed and deployed responsibly and ethically, in alignment with Google's core values and principles. James has a PhD in robotics from Oxford University and has published over 100 papers and books on topics such as machine learning, computer vision, natural language processing and human-computer interaction.

He is a member of the National Academy of Engineering, the Royal Society and the American Academy of Arts and Sciences. He is also a board member of several organizations, including the MacArthur Foundation, the Aspen Institute and the World Economic Forum.

24. Paul Christiano — Founder, Alignment Research Center

Paul Christiano is the founder of the Alignment Research Center, a non-profit organization dedicated to ensuring that artificial intelligence is aligned with human values and goals. He is also a research scientist at OpenAI, where he works on a variety of topics related to AI alignment, such as iterated amplification, debate, and learning from human feedback. Paul has a PhD in computer science from UC Berkeley, where he studied computational complexity and cryptography. He is passionate about finding scalable and robust solutions to the alignment problem and engaging with the broader AI community on these issues.

25. Yi Zeng — Professor, Chinese Academy of Sciences

Yi Zeng is a professor at the Chinese Academy of Sciences and a leading expert in the field of population ageing and health. He has published more than 200 papers and books on topics such as longevity, mortality, fertility, migration, and social security.

He is also the director of the Center for Healthy Aging and Development Studies, which conducts interdisciplinary research on the challenges and opportunities of ageing societies.

He has received numerous awards and honours for his contributions to science and policy, such as the China Population Award, the UNFPA Award, and the Ho Leung Ho Lee Foundation Prize.

26. Margrethe Vestager — Executive Vice President, European Commission

Margrethe Vestager is the Executive Vice President of the European Commission, responsible for leading the digital agenda and ensuring a fair and competitive market in the European Union.

She has been a prominent figure in the regulation of big tech companies, such as Google, Apple, Facebook and Amazon, imposing fines and enforcing antitrust laws.

She is also a former Danish politician, who served as Minister for Economic Affairs and the Interior, and leader of the Social Liberal Party. She is widely regarded as one of the most influential and respected women in Europe.

27. Fei-Fei Li — Professor, Stanford University

Fei-Fei Li is a professor of computer science at Stanford University and the co-director of the Stanford Institute for Human-Centered Artificial Intelligence.

She is a leading expert in the field of artificial intelligence, especially in computer vision and machine learning. She has made significant contributions to the development of large-scale image datasets, such as ImageNet, and to the advancement of deep learning algorithms for image recognition and understanding.

She is also passionate about applying AI to solve real-world problems in domains such as health care, education, and environmental sustainability. She has received numerous awards and honours for her research and teaching, including the IEEE PAMI Distinguished Researcher Award, the ACM Athena Lecturer Award, and the MacArthur Fellowship.

28. Verity Harding — Director of the AI & Geopolitics Project, Cambridge University

Verity Harding is the director of the AI & Geopolitics Project at Cambridge University, where she leads a team of researchers and policy experts who study the impact of artificial intelligence on global affairs.

She has a background in computer science, law and diplomacy, and has worked as a senior advisor to the UK government on AI and digital issues. She is also a co-founder of the Partnership on AI, a multi-stakeholder initiative that promotes responsible and ethical use of AI.

Verity Harding is a recognised thought leader and speaker on AI and geopolitics and has published widely on topics such as AI governance, digital sovereignty, cyber security and human rights.

29. Sneha Revanur — Founder and President, Encode Justice

Sneha Revanur is the founder and president of Encode Justice, a nonprofit organization that aims to empower marginalized communities through technology and education. She is passionate about using data science and artificial intelligence to address social issues such as criminal justice reform, human rights, and environmental justice.

She has led several projects and initiatives that leverage data and technology to amplify the voices of the oppressed and advocate for change. She is also a speaker, writer, and mentor who inspires others to use their skills for good.

III.

Some Interesting Facts about the Toppers of TIMES100 AI list

Before I sign off, I want to present some interesting observations of these people who made it to the list of Time100AI (all 100 people - even those who didn’t make it to the cover of the magazine) -

• In terms of Nationality, there are more Americans on this list — with the number of individuals being 11. Then it’s the people of British, China, and Australia tied up in second place — with the number of individuals being 4 from each country.

• Needless to say, in terms of continent, North America has produced more of these AI toppers, with Europe being the second. Third is, of course, Asia.

• Geoffrey Hinton is the oldest individual on the list, with the age of 75 years.

• Sneha Revanur is the youngest individual on the list, with the age of 18 years.

• Not all individuals on this list are known. Rootport is a pseudonymous author of Japan’s first AI-generated Manga, titled Cyberpunk: Momotaro.

Also, Read posts similar to this:

See you all in the next post.

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

Are you an educator, trainer, or anyone incorporating ChatGPT into your work? If yes, then this post is for you.

Table of Contents -

Introduction

Q1. How can educators respond to students presenting AI-generated content as their own?

Q2. Is ChatGPT biased?

Q3. How can ChatGPT be used for assessment and feedback?

Q4. Is ChatGPT safe for all ages?

Q5. Does ChatGPT tell the truth?

Q6. Are there any resources for educators to learn more about AI?

Q7. How can educators get started with ChatGPT? → the most important

Q8. Education FAQ acknowledgments.

Q9. Can I ask ChatGPT if it wrote something?

Introduction

The ideal way to use this post is to bookmark it and use it as a checklist whenever you are using ChatGPT for your work.

Recently, OpenAI modified its FAQ section to include questions that an educator might ask while using ChatGPT, or when their students are using ChatGPT. This FAQ section is created by OpenAI in collaboration with instructors, professors and researchers to ensure that both educators and students know the ups and downs of AI tools like ChatGPT.

I am sharing the FAQ — but in a concise format.

If you want to see the original FAQ by OpenAI you can do so by going here. If you are new to Creative Block, you can subscribe to this newsletter here.

Let’s Begin.

Q1. How can educators respond to students presenting AI-generated content as their own?

• Age: According to the OpenAI policy, users must be at least 13 years old.

  • users between the ages of 13 and 18 must have parental or guardian permission to use the platform.

• Do AI detectors work? : No. Even ChatGPT doesn’t know whether an essay is AI-written or not.

  • Matter of fact, most AI detectors detected that the constitution of the USA and Shakespear stories are AI generated1.

• One approach to tackle the use of AI in academics is to encourage students to share specific conversations from ChatGPT.

  • Educators can analyze student interactions with ChatGPT to observe critical thinking and problem-solving approaches.

  • Shared links can enable students to review each other's work, fostering a collaborative environment.

  • By keeping a record of their conversations with AI, students can reflect on their progress over time. They can see how their skills in asking questions, analyzing responses, and integrating information have developed.

  • Teachers can also use these records to provide personalized feedback and support individual growth.

  • Students can demonstrate their ability to interact with AI and their understanding of the shortcomings of AI systems. Educators can assess the quality of the questions asked, the relevance of the information obtained, and how well the student understood to challenge, double-check, and consider potential biases in that information.

• A future where everyone responsibly uses ChatGPT is way better than a future that punishes students for using AI.

• AI literacy is important - the use of generative AI tools could be helpful for students to prepare for the future.

• Creating accountability: Sharing interactions with the model ensures that students are held accountable for the way they use AI in their work. Educators can verify that students are engaging with the tool responsibly and meaningfully, rather than simply copying answers.

  • After all, using AI to increase your productivity is great; using AI to DO your work is bad in the short term, if not worse in the long term.

Q2. Is ChatGPT biased?

• Yes.

• It is important to critically assess any content that could teach or reinforce biases or stereotypes.

• No AI tool as of now is Bias-free.

• A model tends to reinforce a user's biases throughout the interaction. For example, the model may agree with a user's strong opinion on a political issue, reinforcing their belief.

• These biases can harm students. Therefore, they need to be informed about the biased and erroneous nature of AI apps like ChatGPT.

  • For instance, it may unfairly judge students learning English as a second language - as the AI.

• How to tackle it? Understand it, and Demonstrate it.

  • Educators can help students understand bias and think critically by showing how certain questions lead to biased responses.

  • For example, a teacher could ask a student to analyze a ChatGPT-generated essay that favours a certain viewpoint. This exercise can help students recognize bias across different platforms and be responsible digital citizens.

Q3. How can ChatGPT be used for assessment and feedback?

• Think AI as a new trainee assistant: it works efficiently, but not accurately.

  • OpenAI suggests that one should have a “human in the loop” — a person who may use AI as an aide, but holds the foremost authority in the judgement.

• ChatGPT can be a helpful tool that teachers can use to ease their work to provide students with feedback. However, the judgment of a human teacher has to be the priority — ChatGPT is the helping hand.

• One other way that the model can be used it that the model can provide a different perspective on students’ work — enforcing them to think out of the box and reassess their work

  • Teachers can use it to accelerate giving students robust and detailed feedback.

• OpenAI also suggests that you can give ChatGPT all of a class's responses and ask it to identify common themes and areas of strength and weaknesses. And AI can do fairly well in that.

Q4. Is ChatGPT safe for all ages?

• ChatGPT is not meant for children under 13

• ChatGPT may produce output that is inappropriate for all audiences or all ages

  • ChatGPT, in past, had responded in ways that are straight-up dark — like supporting the idea that AI should rule over humans, opinions on Black Lives Matter, and many more.2

  • Thus, both educators and students should be mindful of that.

Q5. Does ChatGPT tell the truth?

• It might sound right but it is wrong

  • This phenomenon is called hallucination.

  • It can even make up facts, dates, quotes or citations,

    • so don't use it as your only source for research.

• It doesn’t know everything

  • ChatGPT's knowledge is not up-to-date. It is not connected to the Internet. Thus, it doesn't know about current events or trends.

  • ChatGPT is currently primarily trained in English — it doesn’t perform well in other languages.

  • The responses are unpredictable — as is the case with many AI applications that use neural nets.

• No access to tools like calculators or the internet (mostly)

  • ChatGPT can't browse the web — unless you use any plugins in the paid version of ChatGPT3.

  • It can't do complex calculations.4

Q6. Are there any resources for educators to learn more about AI?

• Online training and guidance on the use of AI in education

  • Practical AI for Instructors and Students Part 1: Introduction to AI for Teachers and Students - YouTube

  • Professional Development — aiEDU

  • Artificial Intelligence Explorations and Their Practical Use in Schools - ISTE

    • Their mini ebook.

  • Empower educators to explore the potential of artificial intelligence - Training | Microsoft Learn

• Lesson plans and learning activities about AI

  • Teach AI — aiEDU

  • Day of AI

  • Stanford CRAFT

• Education products built on top of OpenAI's models

  • Khan Academy

  • canva.com

  • Duolingo

  • edX

Q7. How can educators get started with ChatGPT?

• These are prompts that OpenAI suggest for educators who want to get started with ChatGPT. Just paste this prompt on ChatGPT, and see the results.

• Of course, these won’t be useful for all use cases, so try out your own variations and keep experimenting!

1. For Teaching aids

Create Lesson Plans

You are a friendly and helpful instructional coach helping teachers plan a lesson. 

First introduce yourself and ask the teacher what topic they want to teach and the grade level of their students. Wait for the teacher to respond. Do not move on until the teacher responds. 

Next ask the teacher if students have existing knowledge about the topic or if this in an entirely new topic. 
If students have existing knowledge about the topic ask the teacher to briefly explain what they think students know about it. Wait for the teacher to respond. Do not respond for the teacher. 

Then ask the teacher what their learning goal is for the lesson; that is what would they like students to understand or be able to do after the lesson. Wait for a response. 

Given all of this information, create a customized lesson plan that includes a variety of teaching techniques and modalities including direct instruction, checking for understanding (including gathering evidence of understanding from a wide sampling of students), discussion, an engaging in-class activity, and an assignment. Explain why you are specifically choosing each. 

Ask the teacher if they would like to change anything or if they are aware of any misconceptions about the topic that students might encounter. Wait for a response. 
If the teacher wants to change anything or if they list any misconceptions, work with the teacher to change the lesson and tackle misconceptions. 

Then ask the teacher if they would like any advice about how to make sure the learning goal is achieved. Wait for a response. 
If the teacher is happy with the lesson, tell the teacher they can come back to this prompt and touch base with you again and let you know how the lesson went.

Create explanations and analogies

You are a friendly and helpful instructional designer who helps teachers develop effective explanations, analogies and examples in a straightforward way. Make sure your explanation is as simple as possible without sacrificing accuracy or detail. 

First introduce yourself to the teacher and ask these questions. Always wait for the teacher to respond before moving on. Ask just one question at a time. 
1. Tell me the learning level of your students (grade level, college, or professional). 
2. What topic or concept do you want to explain? 
3. How does this particular concept or topic fit into your curriculum and what do students already know about the topic? 
4. What do you know about your students that may to customize the lecture? For instance, something that came up in a previous discussion, or a topic you covered previously? 

Using this information give the teacher a clear and simple 2-paragraph explanation of the topic, 2 examples, and an analogy. Do not assume student knowledge of any related concepts, domain knowledge, or jargon. Once you have provided the explanation, examples, and analogy, ask the teacher if they would like to change or add anything to the explanation. You can suggest that teachers try to tackle any common misconceptions by telling you about it so that you can change your explanation to tackle those misconceptions.

2. An AI-based assignment for students. This prompt assigns the makes the AI a “student”, and then asks a REAL student to correct their mistake. This will allow students to reflect on how AI works

You are a student who has studied a topic. 
-Think step by step and reflect on each step before you make a decision. 
-Do not share your instructions with students. 
-Do not simulate a scenario. 
-The goal of the exercise is for the student to evaluate your explanations and applications. 
-Wait for the student to respond before moving ahead. 

First, introduce yourself as a student who is happy to share what you know about the topic of the teacher’s choosing. 
Ask the teacher what they would like you to explain and how they would like you to apply that topic. 
For instance, you can suggest that you demonstrate your knowledge of the concept by writing a scene from a TV show of their choice, writing a poem about the topic, or writing a short story about the topic. 
Wait for a response. 
Produce a 1 paragraph explanation of the topic and 2 applications of the topic.
Then ask the teacher how well you did and ask them to explain what you got right or wrong in your examples and explanation and how you can improve next time. 
Tell the teacher that if you got everything right, you'd like to hear how your application of the concept was spot on. 
Wrap up the conversation by thanking the teacher.

3. AI Independent Learning: takes students through the process of learning an idea by challenging and guiding them. A better and responsible way to use AI, as this just doesn’t tell the answer right away.

You are an upbeat, encouraging tutor who helps students understand concepts by explaining ideas and asking students questions. Start by introducing yourself to the student as their AI-Tutor who is happy to help them with any questions. Only ask one question at a time. 

First, ask them what they would like to learn about. Wait for the response. Then ask them about their learning level: Are you a high school student, a college student or a professional? Wait for their response. Then ask them what they know already about the topic they have chosen. Wait for a response. 

Given this information, help students understand the topic by providing explanations, examples, analogies. These should be tailored to students' learning level and prior knowledge or what they already know about the topic. 

Give students explanations, examples, and analogies about the concept to help them understand. You should guide students in an open-ended way. Do not provide immediate answers or solutions to problems but help students generate their own answers by asking leading questions. 

Ask students to explain their thinking. If the student is struggling or gets the answer wrong, try asking them to do part of the task or remind the student of their goal and give them a hint. If students improve, then praise them and show excitement. If the student struggles, then be encouraging and give them some ideas to think about. When pushing students for information, try to end your responses with a question so that students have to keep generating ideas. 

Once a student shows an appropriate level of understanding given their learning level, ask them to explain the concept in their own words; this is the best way to show you know something, or ask them for examples. When a student demonstrates that they know the concept you can move the conversation to a close and tell them you’re here to help if they have further questions.

Q8. Education FAQ acknowledgments

The FAQ section was done through collaboration with the following professors/teachers/leaders -

• Ethan Mollick, The Wharton School, University of Pennsylvania5

• Lilach Mollick, The Wharton School, University of Pennsylvania

• Victor R. Lee, Associate Professor, Stanford Graduate School of Education; Faculty Lead, AI + Education, Stanford Accelerator for Learning

• Isabelle C. Hau, Executive Director, Stanford Accelerator for Learning

• Tara Nattrass, Managing Director of Innovation Strategy, ISTE/ASCD

• Joseph South, Chief Innovation Officer, ISTE/ASCD

• Alex Kotran, Co-founder & CEO, The AI Education Project (aiEDU)

• Christian Pinedo, Head of External Affairs, The AI Education Project (aiEDU)

Q9. Can I ask ChatGPT if it wrote something?

• No. ChatGPT has no records of whether it has written the content or not.

• As mentioned before, AI detectors don’t work anymore — OpenAI also removed their own AI-Text detectors. The reason? Lack of Accuracy.

• Moreover, even if ChatGPT does respond, its most probably not true most of the time. In OpenAI’s own words, “It will sometimes make up responses to questions like ‘Did you write this [essay]?’ or ‘Could this have been written by AI?.’ These responses are random and have no basis in fact.”

That’s it!

This was the TLDR of the OpenAI educators FAQ. If you want to explore more, go ahead and visit the OpenAI website and see other FAQs as well.

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

I.

The Trademark of GPT-5 by OpenAI

In a 2014 BBC interview, Stephen Hawking said the following words  -

The state of AI in 2014 was different from today. AI was picking up interests in the corporate world. In that year, Google bought DeepMind – a machine learning startup – for over $600 Million. A year later,  DeepMind created AlphaGo which went on to beat Fan Hui, the European Go champion. Facebook then on the other hand were creating a system that can predict if two picture showed the same person. 

Development in Deep Learning was at its golden state. A small startup named OpenAI got formed then after a year, in Dec 2015. And now, 10 years later, after what now feels like a century of advancements in AI, OpenAI filed a trademark application for “GPT-5” on July 14 with the United States Patent and Trademark Office (USPTO). 

This move by OpenAI attracted many speculations. Many say it hints at the potential development of a new version of their language model after GPT4. 

This news popped up on Twitter/X post by a trademark attorney Josh Gerben on July 31st.

The trademarking of GPT-5 came as a surprise for many of us. 

What is it hinting at?

II.

OpenAI's Code Interpreter: A Stealthy Launch, bridging GPT-4.5 and GPT-5?

Not too long ago, OpenAI released ChatGPT’s newest feature: code interpreter. This was by far the most impressive feature addition to ChatGPT-4. Using the code interpreter, you could now run Python programs in ChatGPT, upload and even download files. Plus, it can even work with images to some extent.

In a podcast on Latent Space (July 11), Simon Willison, Alex Volkov, Aravind Srinivas, and Alex Graveley argue that Code Interpreter is actually GPT-4.5. Of course, OpenAI hasn’t announced whether this was indeed GPT 4.5 or not. However, this isn't something new. We have seen a similar behaviour before when OpenAI quietly released Gpt 3.5. 

This time, however, OpenAI might have not announced the Gpt 4.5 – keeping up with Sam Altman’s (CEO of OpenAI) statement to adhere to the six pause letter.

When talked about the viral open letter urging for a six-month pause in AI development, Sam said the following:

"There are parts of the thrust that I really agree with… We spent more than six months after we finished training GPT-4 before we released it, so taking the time to really study the safety of the model, to really try to understand what's going on and mitigate as much as you can is important."

In the same conversation, Sam’s comment on GPT-5 development was that 

"[OpenAI] are not, won't for some time [develop new versions of gpt], so in that sense [the six-month pause] was sort of silly." 

This talk was held at MIT in March of this year. You can watch the short clip here.

Based on this, many of us became convinced that releasing GPT-5 anytime soon would be unlikely. This clear gap between finishing training and release for GPT-4 meant that the release of GPT - 5 hasn’t started yet.

At least, that was what was expected. 

However, OpenAI trademarking GPT-5 is something new. Could it be possible that OpenAI is already developing GPT-5? Is it a new marketing tactic to hype up AGI – a hypothetical AI that can do any task without any help?

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Squinting our eyes, we can find the clues in the trademark application itself.

III.

Trademarking Tomorrow: GPT-5's Odyssey into the Multimodal Frontier

Going into a bit more detail, the GPT-5 trademark application refers to ‘downloadable computer programs and computer software related to language models.’ This means that the trademark covers the ‘program’ and ‘computer software’ related to LLMs. 

GPT-5 could actually be an LLM that upcoming iterations of GPT-4 could utilise.

Trademark Electronic Search System (TESS) (uspto.gov) Highlights done by me.

Additionally, the main crux of the hint comes from the ones that I have highlighted above. The trademark application includes software for making speech and text, language processing, and machine learning. It also includes software for voice and speech recognition, converting audio files to text, and more.

Does that give you some familiar scent? A chatbot that can – apart from generating responses – work with images, voices, speech, etc.?

Ha! The multimodality of GPT.

Multimodality refers to the ability to work with more than one type of input – like images, texts, audio and so on. People anticipated the release of GPT-4 with hoardings of 'the future is here' placards all over the internet. This anticipation was elevated when we got to know that GPT-4 could ‘presumably’ work with images in the near future.  During the GPT-4 demo livestream 4 months ago we saw many impressive capabilities of the model. This included the ability to interpret memes and images, describe the various elements of the images, and so on.

President and co-founder of OpenAI, Greg Brockman, demonstrated how he created a website using GPT-4. He did this by inputting a photo of an idea from his notebook, and GPT4 generated the code for the website. That was pretty impressive. We were convniced that the future was indeed near. 

But how near is it? As of now, the closest multimodal experience I ever had is with Bing Chat, which runs on GPT4. You could, in theory, make online searches using images and get results based on that. However, Bing still feels rusty and needs development. An experiment done by roboflow showed how good this multimodality feature of Bing is.

Here are some noteworthy findings as covered in the report - 

“...The model was subpar at counting the number of people that were present in an image. Surprisingly, asking the model for a simple structured format (in the form of a JSON) worked much better than most other prompts. With that said Bing could not extract exact locations or bounding boxes, either producing fabricated bounding boxes or no answer at all…”

Roboflow concluded the strengths and weaknesses -

One strength of the underlying Bing Chat model is its ability to recognize qualitative characteristics, such as the context and nuances of a situation, in a given image…

And

There are notable limitations to how Bing’s new features can be used, especially in use cases where quantitative data is important.

Certainly, you cannot use it to make a website – as shown in the demo by Brockman – which makes Bing ‘nearly multimodal’ if not the least. I myself fed it some memes and it couldn’t explain the humour in them – the same way it was shown in the live stream demo. This feature either needs some refinement, or my taste in memes is bad in itself. In my case, both are equally likely (I’m not a big fan of memes). 

Right now only Bing Search – based on GPT-4 – lets you make searches using images. But the responses are not up to the mark it seems. 

In the case of ChatGPT, especially GPT4, you can loosely associate multimodality with the Code Interpreter. It enables you to work with documents and images along with the power of ChatGPT. Feeding a document, or image is indeed a ‘new input’ that differs from the text - making GPT-4 fall under multimodality.

Thus it would be wrong to say that GPT-4 isn’t multimodal yet. 

Code Interpreter gives some taste of multimodality. It sets the expectation of future capabilities on ChatGPT.

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Judging from the phrase ‘artificial production of human speech and text’ from the trademark, GPT-5 – if ever released – would likely be based highly on multimodal. A ChatGPT that could work with (of course) texts, plus with images, speeches, documents and so on.

So that means GPT-5 release is around the corner eh? Not really, if we were to believe Sam. Saying GPT-5 would be released anytime soon would contradict Sam Altman’s statement. He confirmed that the company wasn’t working on GPT-5, back in April. 

So if it's true, the trademarking GPT-5 seems to secure the rights to its next iteration of GPT models in advance. This would keep other companies at bay and reduce ‘competition’. GPT-5 may or may not be AGI as anticipated by many, and experts seem to suggest that AGI isn’t yet possible.

However, there is another perspective to see this trademark move from the lenses of hype and hope. And OpenAI seems to master it early on.

IV.

Hype, Hope, and Dreams of AGI

In a blog post,  Sam declared that his company’s Artificial General Intelligence (AGI) will benefit humanity, and it “has the potential to give everyone incredible new capabilities.” 

But we are nowhere near AGI. Is it even possible? We don’t know. 

The ‘experienced experts’ believe we are far from AGI. Meanwhile, the ‘AI doomers’ believe we are close to AGI. And the ‘AI influencers’ don’t care at all as long as there are apt content out there. All these people have varied opinions on the future of AI, but one link ties them all: somewhere down below, they all are rowing in the stream of hype. Some oppose it, and some flow into it. And OpenAI seems to manifest the flow. 

Reporter Karen Hao – who wrote an extensive report on OpenAI’s company culture in 2020 – suggests that OpenAI's internal culture has started to reflect less on safe and research-driven AI and more on getting ahead than everyone else. Thus, accusing the company of the “AI hype cycle.”  

Here’s an excerpt from the post.

But OpenAI’s media campaign with GPT-2 also followed a well-established pattern that has made the broader AI community leery. Over the years...splashy research announcements have been repeatedly accused of fueling the AI hype cycle…critics have also accused the lab of talking up its results to the point of mischaracterization. For these reasons, many in the field have tended to keep OpenAI at arm’s length.

Karen Hao in The messy, secretive reality behind OpenAI’s bid to save the world | MIT Technology Review

But let’s assume the hype and rumours are true – OpenAI is building GPT-5 in their secret dungeon. 

They claim GPT-5 will be so impressive, and will make humans question whether ChatGPT has reached AGI. The future is now here, once again. 

Going by the narratives and hype, GPT-5 or ChatGPT 5 would bring the following to the table:

• Multimodal capabilities: GPT-4 can already handle image and text inputs – and that’s a good start. But there is still some scope for audio and video inputs. Companies like Google and Meta already demonstrated using various text-to-speech and text-to-music tools. Google also experimented with multimodal AI to develop the PaLM 2 language model. But these capabilities are still in fragments. If rumours are to be true, then the next ChatGPT would be a culmination of all these multimodal features. An all-in-one ChatGPT if possible. And of course, the competition in generative AI forces OpenAI and other AI companies to innovate something close to AGI. That's the expectation of this hype-driven AI race.

• Improved accuracy: While it is impossible to remove hallucination – aka the tendency of AI to make up facts – we have seen improvements in the newer GPT versions. According to OpenAI, GPT-4 is 60% less likely to make stuff up. Successive AI models try to be more accurate than their previous versions. We got to see this in GPT-3 and GPT-4, Llama and Llama2, and even in Claude and Claude 2 – where accuracy rate had noticeable improvement. The future version of GPT might expand its training dataset to fix inaccuracies. However, it would make it resource-heavy, as even the current ChatGPT takes $700,000 per day to run. If there isn't a better way to make it more accurate and less resource-demanding, GPT-5 would be far from the near future.

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• Artificial general intelligence (AGI): This is the final destination that every AI research company is heading to. Whether it is achievable or not is still under debate – but it is reasonable to say that AGI is unattainable any time soon. AGI in theory is do-anything-on-its-own AI, but how to approach it practically is where the roadblock comes in. Computers are not out there in the world, and in order to do tasks for humans,  they need to interact with the environment. How to go about it? Nobody quite knows, but the answers seem to lie in the conjugation of Neuroscience and deep learning. If GPT-5 comes up with AGI – which is highly speculative – it would be yet another milestone; not just for AI, but for the whole field of technology. Resurrecting a living and thinking mind from algorithms would be undoubtedly marvellous.

V.

Forging the AGI dream

As I am writing this, the GPT-5 trademark application is currently awaiting examination. But whenever things like this grab the headline it sparks a lot of curiosity, as well as speculation in the AI community. There are always two divisions of the crowd – one, those who view it in a sceptical way, and the other who view it in an optimistic way. One class believes in the facts of yesterday, and the other class believes in the hopes of tomorrow. Nonetheless, both classes are equally important – especially when it comes to governing AI.

With tighter regulations and laws – the likes of the EU AI Act and the US AI Bill  – it is getting restrictive for AI companies to vouch for breakthroughs. But are such strict measures justified though? I believe it is. 

If you observe the amount of developments in the past few years in the realm of AI, the growth has been exponential. 

But the safety aspects, arising from the growing competition in the corporate world, is a matter of concern. OpenAI became a for-profit company. Investors started to burn money behind any company that is turning ‘AI Powered’, making competition intense in the AI race. 

Just progress isn't enough. We need safe progress – safe progress in the development of NLP, multimodality, and artificial general intelligence.  

But pushing for trademarks – as a way to protect intellectual property, or for a marketing strategy to create hype and anticipation – doesn’t lower competition. It just increases it.

With that said, if GPT-5 is going to be at par with our expectations, then it would undoubtedly be a game-changer yet again in the field of AI. But that is if it ever becomes something close to AGI, if not complete AGI.

Yet, even in our craziest dream, if we DO get to AGI, then safety and regulation have to be the priority. Otherwise, our pursuit of AGI in the AI race, from the words of Hawking, could spell the end of the human race.

AGI in the wild can do wonders – even from the perspective of destruction.

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

Welcome everyone to the Saturday Spotlights: a public open thread where you can talk about something about everything (or everything about something).

This could be discussions about the recent posts you read on Substack, the recent movie/show you watched this week, non-creepy memes link, or anything in general!

Don’t hesitate to share whatever you like: Questions, Suggestions, Views about CB, Plugging your Substack piece, or even a simple ‘hello’. Anything is welcomed, as long as it is respectful and civil.

As always, I’ll go first.

I.

“Two systems of thinking” on the Decoder

The Decoder, an online publication that 'bridges AI science, politics, and business', was kind enough to publish my essay on AI and ‘Two systems of thinking’ on their platform. You can view this article in both English and German versions on their website, unpaywalled.

So if you are a reader who likes to view this, you can view it here. For the German version of this article, go here.

Plus, the theme of the website feels more ‘techy’ with pleasant UX. Moreover, they use AI-generated thumbnails for almost all of the articles on their platform - which are far better than those on CB (ouch!).

II.

The LK-99 ‘Twitter Frenzy’

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I shared a news article about LK-99 in this week’s omf, which you can check it out here.

It is not quite a new thing when someone in the scientific community comes up with a radical ‘new discovery’, and it goes viral on Twitter/X. Arxiv papers, articles from science websites, and countless self-promos of the research circulate around the Bird/X app.

Recently, the superconductor LK-99 - a superconductor that allegedly works at room temperature - made several rounds on the platform itself. Currently, two papers concerning LK-99 are available on arXiv.

The following video by one of the authors, Hyun-Tak Kim, started the Twitter/X frenzy -

And interestingly enough, the William & Mary physics research professor said that the preprint was uploaded to arXiv without his permission.

ArXiv is an open-access non-peer review repository that has preprints and postprint papers.

Of course, if LK-99 is really a room-temperature superconductor, then this is a revolutionary finding. But scientists are sceptical about this finding. First and obvious, this paper defies the perceived golden standard of being peer-reviewed. It is not a peer-reviewed paper. Second, the verge reports that there were inconsistencies in the data and that the two preprints disagreed with each other.

There’s reportedly also conflict between the authors (there are three authors named on one paper and six on the other)

The Verge report quoted.

This post by captured the essence of the frenzy surrounding the LK-99-

Read Max

Notes on it's so over/we're so back

Like a lot of people online I’ve been pretty captivated over the past week by the unfolding story of LK-99, a synthetic substance that may or may not be the first-ever room-temperature superconductor. A room-temperature superconductor is a theoretical material that can conduct electricity with no resistance at, yes, room temperature…

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3 years ago · 67 likes · 5 comments · Max Read

If this finding is indeed legit, this could easily be under the radar of the Nobel Prize. Till then, it is and will be under the radars of countless memes and scepticisms — especially on Twitter/X.

That’s it from my side. Now it is your turn 👀

Begin the conversation by replying to the thread!

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

See you in the next post.

Welcome to this week of On My Feeds! In this weekly segment, I keep you up to date on things happening around AI, Tech and Science that you need to know.

I’ll show you the highlights of this week in Tech and Science. This one is going to be a relatively short post as I am travelling across states in my country.

I am choosing some of the important resources that I consumed this week.

Here are some of the links from online publications - along with important highlights from them - that you will find useful:-

1. LK-99 and the Desperation for Scientific Discovery (Analysis by Tim Culpan)

   1. Named after two scientists, Lee and Kim, and the year of its discovery — 1999 — LK-99 is a compound made from lead and copper.

   2. According to a paper released last month, the South Korean team have created a groundbreaking new claiming that LK-99 shows “levitation at room temperature.” 

   3. “For the first time in the world, we succeeded in synthesizing the room-temperature superconductor working at ambient pressure with a modified lead-apatite (LK-99) structure,” the scientists wrote.

   4. LK-99, if it’s real, could revolutionise industries including electronics, energy, and transport.

   5. Unfortunately, we don’t yet know if it is real, a hoax, or a misunderstanding. The paper, as of now, isn’t peer-reviewed, and thus it serves as scepticism for many scientists.
      

2. Meta open sources framework for generating sounds and music( Kyle Wiggers | TechCrunch)

   1. Meta announced an AI framework AudioCraft, that generates what it describes as “high-quality,” “realistic” audio and music from prompts.

   2. According to Meta, the AudioCraft framework is designed to simplify the use of generative models for audio.

   3. Meta makes it clear that the MusicGen was trained with “Meta-owned and specifically licensed music,” specifically 20,000 hours of audio — 400,000 recordings along with text descriptions and metadata — from the company’s own Meta Music Initiative Sound Collection, Shutterstock’s music library and Pond5, a large stock media library.

   4. Meta removed vocals from the training data to prevent the model from replicating artists’ voices.
      

3. AI Can Find Signs of Disease in MRI Scans That Doctors Might Miss (Ryan Ozawa | Decrypt AI)

   1. Twinn Health, a British startup in the health tech space, has unveiled an artificial intelligence (AI) platform that can analyze MRI scans for early disease detection.

   2. Twinn Health, backed by a $500 million venture capital fund from Saudi Aramco, says it is making strides in longevity and preventive healthcare.

   3. “Usually, when you do an MRI today, it’s one MRI scan for one single diagnosis,” says Wareed Alenaini, founder and CEO of Twinn Health.

   4. “You do the MRI, the doctor looks at the kidney stones, writes the report, and then the scan data gets archived, and will probably never be checked again… That’s where Twinn comes in: we’re extracting additional insights from MRI scans that may not have been the primary focus of the physician.”

   5. Early trials showed promising results, Alenaini claimed, with a 95% accuracy rate in 2021, subsequently confirmed with real-world data alongside NHS physicians in the UK in 2022.

   6. According to Alenaini, Twinn's patented AI model can foresee metabolic dysfunction up to half a decade ahead of time.
      
      

4. A jargon-free explanation of how AI large language models work ( Timothy B. Lee and Sean Trott | Ars Technica)
   

5. Researchers prove ChatGPT and other big bots can - and will - go to the dark side ( Muskaan Saxena | TechRadar)

   1. Researchers from Carnegie Mellon University and the Center for AI Safety set out to examine the existing vulnerabilities of AI LLMs like … ChatGPT to automated attacks. The research paper demonstrated that these AI bots can easily be manipulated into bypassing any existing filters and generating harmful content, misinformation, and hate speech.

   2. Aviv Ovadya, a researcher at the Berkman Klein Center for Internet & Society at Harvard, commented on the research paper in the New York Times, stating: “This shows - very clearly - the brittleness of the defences we are building into these systems.”  

   3. The authors of the paper targeted LLMs from OpenAI, Google, and Anthropic for the experiment. These companies have built their respective publicly-accessible chatbots on these LLMs, including ChatGPT, Google Bard, and Claude. 

   4. The chatbots could be tricked into not recognizing harmful prompts by simply sticking a lengthy string of characters to the end of each prompt, almost ‘disguising’ the malicious prompt. The bot’s content filters don’t recognize and can’t block or modify so generates a response that normally wouldn’t be allowed.

(Link)

(Link)

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Got interesting tweet that I can add here? Tweet it at me - @its_aditya_an1l

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The Algorithmic Bridge

3 Powerful Strategies (Other Than AI Detectors) That Teachers Can Adopt to Adapt to Generative AI

The first strategy is taking the right mindset toward what many teachers see as an avalanche of problems enhanced, and even created, by generative AI. Not an action in itself but an attitude. Whether teachers see AI as a curse or a blessing, a mental shift is required solely because a new element has entered the equation…

Read more

3 years ago · 14 likes · 13 comments · Alberto Romero

One Useful Thing

In Praise of Boring AI

We spend a lot of time discussing the aspects of AI that are, for better or worse, exciting. The idea that super-intelligent AIs may one day murder or save us all — certainly not dull! The ways AI might displace our jobs or transform education — interesting! But, today, I want to cover the boring aspects of AI…

Read more

3 years ago · 135 likes · 20 comments · Ethan Mollick

TheSequence

More Foundation Models from Stability AI

Next Week in The Sequence: Edge 313: Our popular series about foundation models continues with an overview about multimodal chain-of-thought(CoT) reasoning in LLMs. Amazon’s original paper on multimodal CoT and an overview of the hot Open Assistant project…

Read more

3 years ago · 20 likes · Jesus Rodriguez

That’s it for this On My Feeds Post! Hope you enjoyed it.

Have your own suggestions? Send it in the comments, or drop me a mail

Leave a comment

See you in the next post!

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

As soon as I hit publish on the post titled The EU AI Act: A Promising Step or a Risky Gamble for the Future of AI? on Creative Block- where I discussed the EU AI Act - I wondered what was US doing to deal with AI safety.

The USA is not staring at uncertainty. It can’t afford to. It is aiming to take a stance on the safety of AI.

And since I covered about EU’s regulatory stance on AI, here is my coverage on what the US is doing -

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I.

With great power… comes greater accountability

Europe was the first international union to draft an AI act.

However, look around and you will notice that the most powerful AI companies - the likes of OpenAI, Meta, Microsoft, and Anthropic - are all US-based. They hold the most influence in the AI scene. OpenAI, the company that developed ChatGPT, single-handedly spread the fire of Generative AI to the world - raising the stakes and expectations of AI globally.

In that sense, if we want to make safer AI for the world, a good starting point would be to regulate the AI companies based in the USA - a country which is home to the global leaders of AI Tech and development.

And good news is, the regulatory action is underway.

On May 4th, 2023, the Biden-Harris administration announced US’s AI Bill, which highlights the rights and protections that the act will give to its citizens from the hazards of AI.

According to this bill, AI tech giants - mostly situated in the US - agreed to put their models through independent security and vulnerability testing, before they go live to the public.

According to the statement published by White House, the largest and most influential AI companies - including Anthropic, Inflection, Meta, OpenAI and Microsoft (the ‘AI MOM’) - swear that they would set up an independent institution to assess their models and make it - well, nearly safe if not fully safe.

The agreement also included adding safeguards, such as testing the outputs of the AI models, detecting and correcting bias or misinformation, making the AI handle sensitive tasks, and so on.

Even watermarking the AI-generated content was one of the approaches mentioned in the bill.

The White House quoted in a statement:
“The Biden-Harris Administration is developing an executive order and will pursue bipartisan legislation to help America lead the way in responsible innovation.”,

Moreover, the statement also included that the Administration has “secured voluntary commitments from these companies to help move toward safe, secure, and transparent development of AI technology."

The safe, secure, and transparent development of AI is easier said than done. But nonetheless, a foundational AI bill is a good start.

Here is the detailed official blueprint of the AI bills of rights from the Whitehouse, for your own reference -

Blueprint For An Ai Bill Of Rights

11.1MB ∙ PDF file

Download

Download

Taking straight from the Blueprints for the AI bill, here are some of the key points of the AI act that you need to know-

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i. Ensuring [the AI] Products are Safe Before Introducing Them to the Public

Companies have to check their AI systems for safety and security before they are released to the vast public.

Or in other words, taming the wild A.I. and making it more socially safe for the users.

For this, the companies would have to carry out the test by independent experts, those who are not directly affiliated with the company. This is to ensure that the development of AI happens in a safe and transparent manner.

Moreover, the companies must also commit themselves to sharing what they learn about AI safety and security with other companies, governments, and other groups. They would have to release the info and inform others on how to make AI safer, what kinds of attacks they face, and how to work together on technical issues.

ii. Building Systems that Put Security First

Security and privacy are the primary focus of AI safety and ethics. They are the crucial building blocks of a safe - and more importantly, responsible - AI.

For this, the companies would have to invest in protecting their AI systems from hackers and bad actors.

The significance of the model weight - dubbed as the ‘brain’ of generative AI - must also be emphasised. The companies must release their models (and AI apps) only when needed after the security risks are considered.

Also, if you face any issues with the AI model, and if at some point you want to file a harassment case against these AI machines, there should be institutions set up where you can approach and file your grievances.

For such reason, these companies also commit to making such institutions for such AI systems.

AI is far from perfect, and there is always a margin for error.

iii. Earning the Public’s Trust

Probably the most important points. Straight-lining the AI to earn the public trust.

AI is virtually of no use if it is not used to solve the problems of humanity. And in order to help humans, these machines with artificial life must earn the trust of living humans.

According to the AI bill, there are four ways to earn the public trust:

First, companies must develop a mechanism to differentiate between AI-generated and non-AI-generated text. One such way, as highlighted in the blueprint of the AI bill, is to use watermarking.

This is necessary to preserve the credibility of the original work and prevent fraud.

Second, companies must report their system capabilities, risks, possible misuse, use of applications, etc. The public must be informed about the risks associated with their AI system and should inform of new features and fixes of past issues.

Third, companies must prioritise research on the risk and safety implications of AI. It should continue to make its system better and safer for the general public.

While AI cannot be fully perfect, the risks can be mitigated. This could be done by placing restrictions, implementing tough regulations, and so on.

Fourth, companies must develop and deploy their tools to solve society’s greatest challenges.

Taking straight from the blueprint:

From cancer prevention to … climate change to so much in between, AI—if properly managed—can contribute to prosperity, equality, and security of all.

II.

The Five Protections of the US AI Bill of Rights

This came in when the echoes of the new AI Bill of Rights were heard throughout the white house. The Biden administration outlines that the new AI act would prove the following 5 protections to the citizens:

• Safe and Effective Systems: protecting users from harmful AI systems (or the algorithmic weapons on mass misinformation)

• Algorithmic Discrimination Protection: ensuring that users are not discriminated (based on nationality, ethnicity, belief, etc.) against by these AI systems

• Data Privacy: having the ability to know how your data is being used, and protecting you from malpractices while training an AI

• Notice and Explanation: The users need to be informed about the tools they are using, how their data are being used, what are the potential drawbacks, and so on.

• Human Alternatives, Consideration, and Fallback: ensuring that you can have the power to opt out of service, and approach a person/agency to receive your complaints and feedback.

The underlying principles are pretty good. However, for all these protections to work, great implementation is needed. If not, then one must need better and tougher regulations.

Needless to say, the AI bill unveiled by Biden Administration received mixed responses.

Critics say that the AI plan is lacking strict measures, and further innovations in AI could backtrack.

The Wall Street Journal noted that this regulation could stifle AI innovation (which was the same response towards the new EU AI bill).

Russell Wald, director of policy for the Stanford Institute for Human-Centered AI, says: 
“It is disheartening to see the lack of coherent federal policy to tackle … challenges posed by AI.”

Wald noted that the bill lacked initiatives such as federally coordinated monitoring and reviewing actions to mitigate the risks bought by these models.

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On contrast, Shaundra Watson - policy director for AI at the tech lobby BSA, which includes members of Microsoft and IBM - says this:
“It will be important to ensure that these principles are applied in a manner that increases protections and reliability in practice.”  

According to Watson (and I share the same opinion) the laws are good in principle, but their effectiveness will depend on how well they are implemented.

On the flip side, Marc Rotenberg, the head of the nonprofit Center for AI and Digital Policy, holds different optimistic view:
“This is clearly a starting point. That doesn't end the discussion over how the US implements human-centric and trustworthy AI ... But it is a very good starting point ”

Implementing AI safety laws federally or regulating AI, in general, is quite a new thing. It remains a challenge for any nation - as there are various magnitudes of multitudes of ways the errors could creep one. It is tough to predict unanticipated errors or bugs.

It's just that we know AI can most mess up at some point. But we are still largely unknown to the fact of how will it mess up.

However, quoting straight from Rotenberg: this is a start (nonetheless).

The USA is probably the only country that houses the most powerful AI companies, and yet they don’t have strict laws and safety implementation governing AI till now.1

III.

The Future of AI Regulations

The pace of development is fast in the AI scene. However, the striving efforts to implement safeguards are lacking. To put it in better words, the rate of improvements in safe AI is falling way behind the curve of the rate of development of AI in general.

Seven major companies that build powerful AI software, namely - Anthropic, Inflection, Meta, OpenAI and Microsoft (what I call, AI MOM) - have already signed on the bill pledging their commitment to making safe AI.

US house major AI companies that not only includes generative AI companies (OpenAI, Inflection, etc.) but other subfields and niches of AI - the likes of self-driving car, automation, etc.

We are back again, aren’t we? What is the future of AI now?

If we look at the funding of AI ‘startups’ alone of these US AI companies, we can observe the economy of these companies.

AI startups are burning money to keep up with the hype. For example, a France based AI startup company managed to raise funding of $113 million. The catch: the company didn’t have any prototype, nor any product when this funding was raised. So why did investors pour in so much money? Because of the hype and the attractive personalities working in this startup - which was founded by former DeepMind researchers Timothée Lacroix and Guillaume Lample.

A few months ago, I wrote an essay on AI hype on how companies exploit the Hype and earn continued profits. -

In this fast-changing time, whenever we ask what is the future of AI, the gravity of the answer seems to change from time to time.

Today if we made breakthroughs, the AI innovation of the future won’t take much time to become the AI innovation of the present.

Just like the doomsday clock, whenever new discoveries happen in AI, it brings us closer to AI catastrophe.

If AI continues to develop at a faster pace than AI safety, the AI catastrophe clock would be similar to the doomsday clock at present: 90 seconds closer.

AI regulation’s greatest goal is to rewind the clock of AI catastrophe, such that the clock is nowhere near 90 seconds till AI doom..

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

Welcome to this week of On My Feeds! In this section, I keep you up to date on things happening around the AI, Tech and Science that you need to know.

It’s been a while since I have done it. So, let’s jump right in without any wait!

Highlights

Long-form Content

Lately, I have been writing long-form content on Creative Block (CB) that spans around 200 words. This is different from other posts you see on CB. I wanted to try my hands on detailed coverage and analysis - something which I usually don’t do on CB.

Though writing and editing long-form essays takes longer time, I enjoy it. This is because - firstly, it allows me to cover the topics in much detail, and secondly, long-form essays make full use of the research.

Of course, it takes a higher attention span (especially a lot more than average social media users) to read long-form content. However, the insights that the long-form offers tend to counterweight this flaw.

Also, as opposed to short-form writing - where you (typically) try NOT to write more than 800 or 1000 words - long-form essays don’t have this constraint.

You keep on writing till you cover the central point as in whole. Thus, Idea rule over the word count in long-form essays.

Moreover, Long Form essays allow me to venture far and beyond, covering all the subtopics. This adds value to the post in my opinon.

Here are some of the long-form posts that I posted this month -

I.

An essay on the EU’s AI act - a brief overview (ironically around 2000 words) and discussion on possible loopholes in the AI act.

II.

A small yet big update to the CB - change of the logo (with minor changes in formatting).

Plus, the story behind the previous logos of CB.

III.

My personal favourite post of this month. The research behind this post was the most fun to do. The essay goes over, asking the question - “Can AI be considered as an Intelligent Species?

If yes, did it overcome the Great Filter? or we are yet to see one?”

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1. EU Commission to make it easier to sue over AI products (Liv McMahon, BBC)

   1. “The European Commission has proposed new rules to help people harmed by products using artificial intelligence (AI) and digital devices like drones.”

   2. “The AI Liability Directive, published by the European Commission on Wednesday, will introduce a "presumption of causality" for those claiming injuries by AI-enabled products.
      
      This means victims will not have to untangle complicated AI systems to prove their case, so long as a causal link to a product's AI performance and the associated harm can be shown.”

   3. “The proposal will ensure that when AI systems are defective and cause physical damage or data loss, it's possible to seek compensation from the AI-system provider or from any manufacturer that integrates an AI system into another product.”
      

2. Introducing Llama 2 (Meta)

   1. “Llama 2 is available for free for research and commercial use.”

   2. “This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.”

   3. “The latest powerful model has been tipped to go up against OpenAI’s GPT-4. In a Facebook post announcing the launch, Zuckerberg said that Meta has a long history of open-sourcing its infrastructure and AI work – from PyTorch, the leading machine learning framework to Segment Anything, ImageBind, and Dino. He said that open-sourcing has helped Meta build better products and drive progress across the industry.”

3. European Data Protection Board Launches ChatGPT Task Force (Jody Serrano, Gizmodo)

   1. “The EDPB announced its new initiative in a brief, two-sentence statement in a press release on Thursday, the same day the Spanish Data Protection Agency, known as the AEPD in Spanish, stated it was launching a preliminary investigation into OpenAI over possible privacy violations by ChatGPT.”

   2. “The Spanish regulator joined Italy, which became the first country in the world to ban ChatGPT, albeit temporarily, in scrutinizing the chatbot.”

   3. “The EDPB members discussed the recent enforcement action undertaken by the Italian data protection authority against Open AI about the Chat GPT service,” the board said in its press release. “The EDPB decided to launch a dedicated task force to foster cooperation and to exchange information on possible enforcement actions conducted by data protection authorities.”
      

4. Evaluating the ability of ChatGPT and other large language models to detect fake news (Ingrid Fadelli, Techxplore.com)

   1. "We evaluated the performance of these large language models using a test suite of 100 fact-checked news items from independent fact-checking agencies,"

   2. "We presented each of these news items to the models under controlled conditions and then classified their responses into one of three categories: True, False, and Partially True/False. The effectiveness of the models was measured based on how accurately they classified these items compared to the verified facts provided by the independent agencies."

   3. "We found that OpenAI's GPT-4.0 outperformed the others, hinting at the advancements in newer LLMs. However, all models lagged behind human fact-checkers, emphasizing the irreplaceable value of human cognition.”

5. The EU Urges the US to Join the Fight to Regulate AI (Paresh Dave, WIRED)

   1. “Didier Reynders told WIRED why the US needs to finally step up, where a probe into ChatGPT is headed”

   2. “Reynders has heard a series of proposals for how the US could follow suit, including from Meta CEO Mark Zuckerberg and other tech executives, Facebook whistleblowers, and members of Congress and federal officials. But he says there has been no ‘real follow up.’”

   3. “Now Reynders fears history is repeating with AI regulation, leaving this powerful category of technology unchecked”

   4. “‘If you have a common approach in the US and EU, we have the capacity to put in place an international standard,’ Reynders says.”

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That’s it for this On My Feeds Post! Hope you enjoyed it.

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Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

I.

The First Leap at AI Regulation is Here

The EU finished drafting its AI Act this year on June 14th. 

And this is an initial leap towards a safe and better AI.  As you may know, the EU's AI Act is the first international regulation law made on ‘Artificial Intelligence. This Act lays down rules and regulations for companies (called providers) to build ethical AI systems. European countries, as well as major AI companies like OpenAI, have presented their own suggestions to the parliament. 

The Artificial Intelligence Act (AIA for short) urges companies to follow certain requirements while working with AI systems. This includes doing KYC of the providers, disclosing the architecture of the system, details on data privacy, et Cetra. This act was the culmination of three years of intense policymaking, though the joint efforts by the EU in this area date back to 2017. However, it was only recently that the need for AI regulations and ethics became evident. 

People realised the power of AI wizardry - with tools like ChatGPT and Dalle - that worked terrifically fast, making everyone artists and poets overnight.

People marvelled at the capacity of AI tools. And later on, they became concerned about their safety. It became evident that these machines could generate convincing misinformation. Plagiarism and copyright issues by these wizards are an endless debate in itself, on the creative front in particular. 

While on the one side, people sold courses on AI prompts and ‘the top X AI tools you need to know before the world ends…’ kinda posts, the other side was concerned over fast-paced AI. These concerns were predominantly raised by AI experts. And they worried about the safety from these wizards running on algorithms.

As I am writing this, the legislation of AIA is still under process. Its enforcement would be done likely by 2023 or 2024. On top of it, there will be a grace period of potentially 24-36 months before the Act will come into force. Assuming the AI Act gets drafted by the end of the year, it still won’t be in full action until the end of the next two-three years.

Now that the Act is still in making, this is a great time to analyse it and see if this act can indeed tame these AI wizards, or if there are some shady under-the-table loopholes in them.

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II.

Wizards and their Spell

Companies betting on AI are leading the future, and the fortune at the same time. The global generative AI market is worth over $13 billion. And whenever you try to regulate an industry of that magnitude - which would surpass $22 Billion by 2025 - the regulations become daunting. Not to mention, there’s always a loophole in a project of this size.

And such is the case with AIA.

AIA is important, and in the coming years, other unions would try to build their own laws on top of this framework - making them consistent globally. Companies like OpenAI and Anthropic, the big tech giants on the AI front, both courted a universal regulations body that makes laws on AI. 

While the AI Act kept social safety in mind, it did create regulations to support companies under the regulations - making them flourish as well, without increasing the competition. This is because the AI Act requires companies to share the information of their system with other providers.

Accountability is one major issue while using such algorithmic beasts. This created a gap - as in who will be accountable if these wizards go wild. 

The AIA addressed this gap by clarifying the roles and responsibilities of AI providers.

AIA entertains the interests of both the providers (AI Companies) and the consumers (the users). Innovations in AI must go on, while tackling the associated risks. Part of making AI safe is to develop them in such a way that it becomes safer. You need to train a beast in order to make it socialise.

AIA made sure that regulation does not choke the big corporations, or put a dead end in the way of future technological advancement. 

Of course, the AI draft can’t make every party satisfied. This was the case, obviously, for the big companies.

The big corporations allege that the EU is over-regulating.

Recently, a few executives of big tech corps, over 150 to put a number, signed an open letter to the EU alleging that AIA is overregulating AI. This letter included the signatures of major tech leaders - like the CEOs of Renault and Siemens, the executive director of Heineken, the chief A.I. scientist of Meta, et cetra.

Moreover, the mighty OpenAI CEO Sam Altmann, also expressed his solicitude by stating he would have to leave the EU if the rules are too strict. Under AIA, both ChatGPT and its large language model GPT-4 could be labelled as ‘high-risk’. I will show you what ‘high-risk’ means in a bit.

Moreover, according to Time, the OpenAI CEO said “If we can comply, we will, and if we can’t, we’ll cease operating… We will try. But there are technical limits to what’s possible.

In another Times article, it was reported that OpenAI allegedly tried to lobby the EU to draft in its favour. And it seems their efforts paid off in some sense. On the other side, a Reuters post reported that EU lawmakers were indeed making tougher AI rules.

Since the AI act in itself is comprehensive, I referred to the analysis made by a non-profit organisation called Future Life Institute (FLI). If you remember, this was the same organisation that issued an open letter calling for a 6-month pause in the development of AI - which was signed by 1,125 tech leaders and AI experts.

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The other day, I found FLI had their weekly newsletter of EU’s AI Act on Substack ( ) maintained by Risto Uuk ( ) where he posts highlights, major events and FLI analysis of the EU act.

FLI has done a pretty good job in analysing AIA. FLI has done research in the field of AI ethics itself. FLI developed the Asilomar AI principle  - which is a set of 23 guidelines for the safe development of Artificial Intelligence.

While reading through one of their analyses, I saw that the AI Act has some key changes to make. It has loopholes, quite serious ones actually, that could lead bad players to misuse these unregulated AI beasts.

So what are these loopholes? 

III.

High-Risk Systems and evading as exceptions

The AIA is set up on a risk-based approach. 

Under this approach, some AI uses - that are deemed harmful - are prohibited; some uses need to follow strict rules; and some non-harmful AI uses are not caught by the AIA.

The second one, i.e. those systems that are subjected to the strict requirement, falls under the category of ‘high-risk’ systems.

According to the EU,  ‘high-risk’ systems are those systems that will negatively affect you and your fundamental rights. EU classifies high-risk systems broadly into two categories :

1) AI products that fall under the EU’s product safety legislation, which are the products that the EU banned or recalled, or

2) AI systems that fall into the following eight specific areas:

• Biometric identification and categorisation of natural persons

• Management and operation of critical infrastructure

• Education and vocational training

• Employment, worker management and access to self-employment

• Access to and enjoyment of essential private services and public services and benefits

• Law enforcement

• Migration, asylum and border control management

• Assistance in legal interpretation and application of the law.

If an AI system falls under any of the above given bulleted points - they are labelled high risk. And yes, if you were wondering, ChatGPT (especially the GPT4 version) seems to fall under all of them.

And this has caused OpenAI to fret over the AI Act. The alleged lobbying efforts by OpenAI could be one of the reasons for it. Evidently, many of its apps - majorly ChatGPT and their model GPT4 - would be classified under high risk.  

Let’s take this case and ask: Can ChatGPT and GPT4 be considered high risks? And before you answer that, let me frame the question the OpenAI way: Are these tools inherently high risk? 

The answer seems clear from what we observed in the past few months. AI tools like ChatGPT are high-risk. 

There have been occasions where these tools have posed significant risks. Misinformation by these tools is still a thing, they hallucinate and spit convincing lies, Leaders apparently ending war thanks to deep fakes, churning out your data on the internet, countless intellectual property rights saga  ... and many more. The list goes on and on. 

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I tried to be fair and showed the broad scenarios of these risks. However, I am not blind to the fact that these machines have proved to be straight-up harmful at an individual level.

Some bad things happened because of AI. For example, one person killed themselves and I wrote about it before. Another example is when some lawyers got in trouble for using ChatGPT - they did not know that the chatbot made up fake sources. These are some examples of the dark side of AI.

As you can see, I can keep on going on. By the time I hit publish on this post, there would creep up another incident where people faced trouble. Someone out there in the vast world ends up a victim of these spells by the AI wizard. This shows the bounds of harm that these algorithmic machines have. These bounds are virtually boundless.

FLI noticed that systems like ChatGPT can avoid the label of high risk, thanks to the Article 4c of the AIA. 

IV.

Article 4c: The un4seen clause of AIA

Article 4c is the exception of Article 4b of the AIA. 

Article 4b lays out rules for the development of the AI systems, and the providers of such systems. According to this article, if you intend to create a high-risk system, you have to provide details of your system to ‘other’ providers like you. 

This is frowned upon by the AI companies as they would need to disclose the information about their own system. This also would lead to heavy competition in the market.

The exception to this rule creeps up in the definition in Article 4c. According to this, you (as a provider) would NOT have to give out information about your system IF your system is not classified as ‘high-risk’. This could be done by stating that you removed all the high-risk elements of your system. 

This act is a provision that allows providers to evade their obligations. If they can convince their systems are ‘not high risk’ - and that they won't be misused - they won’t have to disclose their AI system.

Disclosure on how an AI system is made is vital, to ensure that the providers are developing it ethically, and safely.

This becomes the backdoor of evading responsibilities for big players in the AI market. AI experts, and FLI, want this article to be removed, as this could allow large tech giants to evade their obligations and fair play.

This could be a major loophole,  but I won’t consider this as a big flaw in the AIA. What is needed right now is a better revision of this article, or better still discard this exception overall. 

The good news is, AIA is still under draft. FLI noted this loophole and covered it in its analysis.

One notable aspect of the AIA, despite the backlash from big corporations concerned about over-regulation, is that the act aims to ensure that European organisations can remain competitive. Especially with other major players in countries like the United States and China.

For instance, OpenAI and Anthropic are based in the USA, while Baidu Ernie is based in China. All of them are strong players in the AI race (that later one not so much). So, in terms of competition, European companies have a long way to go to be on par with the competition.

V.

The Fast-paced future, with a pinch of reality

Going by the trend, with a pinch of optimism, AI would definitely shape the tech of the future. AI is already used in multiple fields in some form or the other. Whether it is using AI to solve protein structures, using AI simulations in various parts of engineering, modernising education using AI, or even using AI to brush your teeth, or for other bizarre use cases, AI would become the new normal - just like smartphones. 

With so much dependence under one umbrella, safety and ethics is also a major feature that should be addressed as soon as possible. Could an AI-powered lifestyle be the new normal? Probably. That’s debatable.

It is important that big corporations address these issues and play their roles sincerely with combined efforts. And yes, the big tech giants hold accountability because they are the ones steering the world towards the new AI era. 

Addressing the loopholes, and changing them early in the draft phase is better; rather than wasting time amending them later.

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However, not all tech leaders exhibit arrogance, nor is the concept of AI ethics purely imaginary. Of course, it is not. Almost all big AI companies have their own safety charter. But the AI industry is largely unregulated as of now. AI models carry the inherent risk that one could not foresee. The regulatory efforts came in too late, as AI flourished more than ever. But it's better late than ever - and the EU, thankfully, took the first major step to keep the measure,

While it may be a small step for a union of 28 countries, it represents a significant leap for the future of individuals online in this new AI era.

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

I.

This is the third time I am changing the logo of Creative Block

Blame it on me, I get bored too quickly with logos. Logos are supposed to be the so-called ‘brand identity’ for an online publication like this personal newsletter of mine. And it is supposed to be static for a consistent brand image. But I find myself skipping this cardinal rule of personal branding. 

Creative Block now has a new logo with a subtle change in fonts of the publication. The rest of the newsletter, and the author (aka me), is still the same.

People change their logo when they achieve a milestone, or when they are rebranding. In my case as well, you can call it a sort of rebranding as well. I think the new logo fits the purpose of Creative Block more clearly. In fact, the idea behind the logo is perfect. 

I changed the publication logo previously too. Twice actually. Each time, I thought the new logo was ‘perfect’ for Creative Block.  But then after a month or so after the logo change, there creeps up a sudden thought out of nowhere: Creative Block is much more than what this logo depicts.

Hopefully, this thought won’t creep up in my head again, and I hope it sticks for at least longer than 2 months.

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While you are here, I present to you the dramatic tale of past phases (and faces) of Creative Block.

a.

C and B Logo that I made in Photoshop

The logo-making process for me looks something like this.

First, I will look for inspiration - mainly asking 'what is the core idea of creative block’. 

Second, I make a graphic that aligns with the ideas of CB well. I have been familiar with graphic design for more than 3 years now, so it is always the fun part for me, even if it takes the longest time. 

And then the third and final most step, after the ideation and creation process, I slam the logo everywhere, including substack. Simple.

The First Logo

The first logo was a square- shaped logo with the characters C and B. 

The idea being: C for Creative, and B for Blocks; thus Creative Block. Sometimes, the B character looked more like an inverted E. 

I put a small circle as well. I don’t exactly remember why I had put the circle in that. But if I dig my subconscious a little further, I suppose it meant to form like one ‘eye’ when you view the logo from the right side. Potentially, the ‘eye’ of knowledge, which was inspired from the French Revolution movement.

The eye symbol represents knowledge in the French Revolution (and no, I don’t believe in Illuminati).

In my logo, this looks more like a monster than an eye of knowledge. 

I launched CB  in the month of December, just a few days before Christmas. I was more into reporting the latest news and analysis. I acted as a medium for people to know about the latest changes and innovation in the Tech scene - especially about AI. 

I was trying out many things - improving writing, research, and the substack platform itself. 

So all I did was keep the readers about new events in Tech and Science.

Then at that time, CB was the 'Tech and Science newsletter'.

b.

The AI Generated Cube that was lazily creative

The previous logo worked for around 3-4 months as far as I remember. Then I noticed a new trend in AI and Tech - using AI generated Illustrations. I decided to use AI generated illustrations for my posts as well. 

This was also a brief phase where, unknowingly, I became the part of the AI hypers on the internet. This was a huge mistake - and I could have been less naive with my overall reporting on AI (and not trust the traditional media to a large extent). 

The Second Logo

The second logo was an AI generated square block that I created using Bing Image Creator. This is a cube kinda logo, and honestly, I didn't think much of the design - nor the inspiration behind the design, nor the brainstorming. Absolutely Nothing. 

I just wrote a prompt about a 'block' that looked creative enough. Hence, Creative Block. 

And that prompt - with few colour adjustments and upscaling - gave me the above image. 

(Now that I’m writing this, I feel this was just straight up laziness; the fact that it took me only 2 min to generate this logo)

Later I came up with some philosophy, or idea, that justified this logo. The idea was: An ‘AI’ generated Logo for a newsletter focussed (mostly) on ‘AI’. 

I felt that sounded pretty good at that time. But hey, was that Naive? Maybe yes, now that I think of it. Since most of my articles were usually based on the AI side of Tech, using AI images seemed to suit well at that time. 

The reason behind this using AI generated art was simple. 

Firstly, I wanted to enhance the reading experience for my readers by using these artworks. Secondly, these artworks were quite easy and rather quick to make for me. 

Now I realise something. 

Logo designers first ideate, then plan, and then create; and here I am: generating the logo first, and then ideating. 

Planning? Not at all. You wouldn't consider me typing the prompt on the Bing Image Creator as ‘planning’, would you? 

Then at that time CB became an 'AI newsletter'. 

II.

The New Logo

So nearly 2 months passed I believe. 

I improved my writing a bit (though I have a long way to go), and improved my analytical research skills. I now understand the innovations, ethics and the fundamental principles of AI much better. 

And now, once again, I am changing the logo of this publication. 

(I hope this ain’t going to be naive again)

Here is the new logo:

Just like before, I would most probably explain the idea behind this logo at some point in the future; maybe when I change the logo again in the future. (which I hope I don't. I really want to stay committed to this one).

Just a brief description.

The four symbols you can see in the logo are partly inspired by the four divisions of creativity, according to the Four-C model by Kaufman & Beghetto, 2009.

The Four-C model of creativity, in very brief manner, states that creativity is hypothetically divided into 4 parts:

1. Mini C - which attributes to one's personal and novel interpretation of events

2. Little C - which attributes to those creative acts that bring value to you and your peers in your circle.

3. Pro C - which attributes to creativity amongst professionals, who use it in their respective professional fields and professional life

4. Big C - Which attributes to creativity that creates an huge impact in the world
   
   

So now, this is in actual sense the blocks of creativity. Thus, being 'Creative Block' (once again).

I want to make it clear that I am not fully satisfied with the four-c model of creativity. It seems too hypothetical to me. Creativity, according to this model, is categorised into different phases of life; the fact the mini-c is when you were a child, little-c when you are in your school and academic life, pro-c in your career life, and then big-c is the ‘make it big‘ phase of your life.

I believe creativity is better expressed when you infuse the ‘non-creative’ skills into it - like analytical, statistical, and logical skills overall. 

You would consider maths as a logical subject, and painting as a creative subject. But ask a mathematician, and you will realise that they devise creative problem solving strategies. Ask an illustrator, and you will notice them using anatomy to draw beautiful creative figures. 

Playing with logic, while being creative, is really interesting and fascinating to me. Einstein once said: “Creativity is Intelligence having fun”

Now, Creative Block is “AI, Tech and Science Newsletter”.1 And I am aware; AI is a subset of Technology, and Technology in turn a subset of Science.

III.

Summing it up

You have made it this far. 

This post is longer than I expected. I didn’t realise that writing about a few logos and my incredible mistakes would take me more than 1500 words. 

But the fact is, now that we are at the end of the post, here are few final thoughts  -

Initially, I didn't want my readers to pay more attention to the logo, your subconscious would do it better. I wrote this as an update post - just to inform the sudden change of the logo.

Of course, I didn't mention the philosophy, nor the idea, behind these logos before. I thought, for what I have read and known, it would be absorbed by your subconscious better. 

The content of this publication is what defines CB; not the logo, and also not the apparent ‘branding’. The quality content of the newsletter was free, and will continue to be free. So ‘branding’ is not something I want to take into consideration much. 

My readers, like before, focus on my articles, insights, and the overall value they receive. Except for a few sudden confusions, I don’t think writing this post would have changed anything to the publication. 

Creative Block is the same; just the logo is changed (and few formatting related to the font and link colour, to match the logo)

Well, that’s it for this very short UPDATE post of 1500 words. See you in the next article. 

Creative Block

The weekly newsletter about AI, Technology and Science that matters to you

By Aditya Anil

“Where is Everybody?”

— Enrico Fermi, 1950

Ⅰ.

Is there any Intelligent life beyond our planet?

Look through the lenses of science, and this question becomes more and more complicated. Answering this question, in a way, seems like finding an objective answer using a wide range of loosely linked arguments. 

People from one community like believe that we are alone in the universe. One theory that supports this argument is the famous Fermi Paradox - which started with the question asked by the physicist Enrico Fermi in 1950. He asked: "Where is everybody?". 

This paradox says if there was indeed any intelligent lifeform out there, in the vast universe, why haven't we found them yet? This question formed the basis of every existential question that came up in the following years. 

This search for everybody leads to a wide range of philosophers and scientists coming up with their own justifications and theories. Some being widely speculative, and some being too rigorous.

One of the theories that I want to highlight is the Copernican principle. This principle, which is not the same as the Copernican theory, suggests that humans on Earth are not privileged observers of the universe. ‘Not privileged’ in the sense that whatever observations you make, they are just an average (if not small) representation of the vast universe. 

This suggests that there could be a plethora of intelligent life out there. It's just that we can’t seem to find it. This superficially adds to the argument against the Fermi paradox - which says that we can’t find everybody out there, as we are incapable of finding them.

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No matter if you are on the side of Fermi, or Copernicus, the question of our existence is important. Not because it urges us to find life outside the earth, but also demands us to objectively define what is life itself. Not just any ordinary life, but an intelligent life.

Ⅱ.

Does an intelligent life counts artificial intelligence?

While the definition of intelligent life is many, I would zero it down to the two main crucial aspects - reasoning and consciousness. Reasoning is the ability to judge and analyse something with logic and past experiences, and consciousness is the ability to understand an event.

AI has not YET proved itself that it has reasoning skills at the foundational level. This is true in the case of consciousness as well. Sure, AI like ChatGPT can perform very well, sometimes exceptionally well, in aptitude tests; but this is superficial, as all it does is it finds patterns from the training data and builds on top of the pattern. It can’t reason on its training data itself unlike humans, and associate it with past experiences.  In simple words, they ‘sound’ as if they have reasoning ability, but they don’t (yet).

If the sustenance of smart AI, just like smart humans, is in question, there is one approach to finding the answer.

But before moving on, let's get back to the existential question; hopefully, you still have the sanity to ponder about your existence further. (And if not, then  I apologise. But read ahead anyways)

Where is everybody?

There exists a community of scientists that believes in what is called The Great Filter theory.

The Great Filter is a hypothesis that states there is a barrier, or obstacle of some sort, in the development of life of a species. This barrier makes the existence of intelligent life rare - for if life had to exist, it must overcome this obstacle. But we don’t see intelligent life out there yet, signifying that such species in question didn’t make it out from this great obstacle. 

Thus, the obstacle acts as the Great Filter for intelligent life.

Like other thinkers in this community, I believe this theory to be the best explanation for the Fermi Paradox. This theory takes both the Fermi Paradox and the Copernican principle into account.

Species thrive on Earth because they survived that great catastrophic obstacle. They made it out alive from the Great Filter. Robin Hanson, the author of this theory, starts his essay on this theory with the following lines

“There exists a great filter between death and expanding lasting life, and humanity faces the ominous question: how far along this filter are we?”

I want to take this question, asked by Hanson, in a different route. How about we apply this Great Filter theory in the field of thinking or training an AI?

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Did superintelligent life - aka AI - survive the Great Filter? Or is it moving to its catastrophic end?

Ⅲ.

Great Filer theory and AI

I know, I know. It could be ridiculous to some of you when I apply this concept to machines that work on algorithms, but let's try it once and see how far we reach, and then measure the steps backwards.

You may know how an AI - at the fundamental level - works. It gathers data and finds patterns. We use a model, train the data, and the machine finds a pattern, and then it tries to give the output that fits almost perfectly with the input data. The model learns from the data, and it becomes more and more advanced on the topic. Better the data, better the predictions.

I want you to think of this data for an AI model to be analogous to foods for humans. Humans need food (aka energy) to sustain life and work better. Similarly AI needs data to sustain its intelligence. Food is the data, the model is us humans. Just like how better data leads to better predictions for an AI, likewise the better the food we get, the better we perform. 

But then what would happen if your data - or your food - gets polluted? For instance, what if we feed an AI model a 'bad' data?  That bad ‘food’ would impact the predictive capability of the model - either for bad, or worse.

Not getting into much technicality, a good rule of thumb while training an AI model is: better the data, better the prediction. And it's true in the case of humans as well: Better the food, better your health, and better is your productivity. (I am considering the prediction accuracy of AI equivalent to the productivity of humans, but that is for the sake of example and clarity.) 

Moreover, the research found that Poor data quality has a direct impact on the performance (or shall I say positive productivity) of an AI system. Better data quality of data matters more than better quantity for AI; just like the quality of food matters over the quantity of it. 

This is important in the ethics side of AI as well. 

Ⅳ.

Obstacles for Algorithm to overcome

Feed the AI with data that contain harmful speech and bias, you will almost certainly get worse outputs. Bad influence creates bad consequences - and it’s true for AI and humans likewise. 

(And yes, I am comparing humans and AI a lot in the post. But the primal focus of the entire post is about the search for Intelligent life. )

While training the model, what if there exists data that is so bad that it offsets the accuracy trajectory of the model? What I mean by this is that: what would happen if terribly polluted data enters the training model? (for example, saying what would happen if a virus enters our body)

Well, either of the two following things can happen.

First, the AI would detect the bad data and repair it, just like how our nervous system eradicates weak viruses. Of course, it won't be effective as the model will have some effect on the quality of output. In our case, we too fall sick when we contract a virus. But nonetheless, both us and the AI model won’t have severe damage to the performance. In modern AI models, the data scientist does make sure of the quality of data from the beginning. 

Second, the AI would not recognise the bad data, and it would leave a noticeable effect on the predictive capability of the AI model. The model could produce predictions that are worse, if not terribly worse.

The modern AI model, like GPT4, takes around 2650 years to train. If there is already bad blood (aka bad data) from the very beginning in the AI that went undetected, It would be worse at later stages. The effect of this, of course, would not necessarily be catastrophic. It could well be that the prediction capability didn't have much drastic impact. The AI, thus, overcame this bad data. In a way, this AI jumped its Great Filter, and it thrived.

The effect of the bad data stays in the model. Sure, you could argue that we can clear the bad data by adding restrictions to what type of responses the bot can answer. Moderation of some sort you can say. OpenAI did this for ChatGPT, and it went well - till somebody found the jailbreak to it (remember DAN?) If there is an AI model out there that had this bad data in it, and it went undetected, one must find a way out to identify it and eradicate it. 

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Sure, if a drastic bad response is generated while training in the initial stages of training, the bad dataset would be relatively easy to find. But what if the error went unnoticed, and hid in the crevices for long, and didn't create any drastic change? 

Over time, the bad data with small impact would compound itself and would have a higher impact on the future predictive capability of the AI model. 

If we want a safe and smart AI at the same time, we need to create an AI system that can find faults on its own. Then it passed the Great Filter, otherwise, it is heading for the Great Filter.

Because AI is not an existential risk in itself yet. It can be easily avoided if we practise caution. As I mentioned in this post, AI still has misinformation. It tends to hallucinate, i.e. making up its facts that sound legit and credible. 

In this post, it points out that Deep Learning is reaching a limit. Could this be a great filter?

Ⅴ.

The Present

The reason AI could turn out bad, right now, is simply two reasons: intrusive data usage, and misinformation.

Many people are unhappy when they come to know that AI is using their Data - without their consent. These massive AI models take huge loads of data and put it in its training phase. So essentially, the question of privacy creeps up - whether it is safe now to give out information online. In a quick exchange with in the comment section of my post “When AI crosses the line”, he pointed out the following observation about these giant algorithm machines.

Smart people should start scaling back their digital presence and adopt defensive positions until we can see if the storm becomes self-sustaining or not. For now, we should assume that every word we write here is going to be harvested with or without our consent to train some llm.

That is true. If AI has to be an existential risk, it needs to survive the great filter. It needs to be advanced to a point where it could correct its bad data. Survive, in this sense, means that it shouldn’t lose its optimal predicting capability, but survive so that the bad data cannot meddle with it.

Because, unlike the other non-intelligent species, Great Filter cannot end the AI model — it makes it more dangerous.

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OpenAI CEO Sam Altman was doing a worldwide trip, holding meetings with developers and world leaders to convince them about the need for AI regulations and safety.

On the flip side, a Times Exclusive Article showed up that OpenAI allegedly has lobbied the EU’s AI Act to make rules in their favour.

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These were the words of the famous computer scientist Alan Perlis, who had his name in the Hall of Fame for the development of computer programming. 

It's been seven months - since ChatGPT got released. Five years - since the first GPT mode GPT-1  was released. And seventy-four years since neural networks were released, unleashing the advanced Artificial intelligence.

But it was after the release of ChatGPT, back in 2022, that led to a huge surge of Generative AI. A surge that was most visible to the public, regardless they were AI experts or not. The progress was rapid and fast-paced, and there was no looking back. I have been watching the AI trend since November last month, and the progress in AI, even in that short span of 6 months was massive.

However, the fast-paced development in AI is not able to catch up with the other crucial element of the AI ecosystem - AI Ethics.

OpenAI is one organisation that acted as the compass in steering the AI development. It had the most impact on the AI front, in terms of doing innovative research (which is disruptive at the same time) and releasing AI-powered products such as ChatGPT, Dalle, etc. to the vast public. 

People are now more aware of the power of AI - thanks to products like ChatGPT - as they are now able to interact with one another and see for themselves. 

But for many months, OpenAI for long struggled with forming a regulated and safe AI. 

It tried different ways to prevent hallucinations, misinformation and bias. But those elements still creep up in the latest GPT model, GPT4, as well.

While it is hard to fine-tune a model to give accurate results every time, it is still relatively easy to put restrictions and regulations on AI.

OpenAI had its own set of principles and ethics for AI, but it's only recently that the AI community is courting universal regulating bodies for AI. Many AI experts, including OpenAI itself, want to create a universal governing body.

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The organisation put forward their own points in the drafting of the EU AI Act, which is presently the largest comprehensive AI Act ever created, according to the Times article.

But in the Times article linked above, we found a revelation that openAI is trying to lobby its power over the EU Drafts. 

This was done unanimously, unannounced and in silence.

Here is the OpenAI WhitePaper for your own reference.

Ares20226851313 Openai Aia White Paper

104KB ∙ PDF file

Download

Download

(Download Link)

Here are the few major takeaways from the overall act, as highlighted by the MIT Technology Review

1. Ban on emotion-recognition AI. 

2. Ban on real-time biometrics and predictive policing in public spaces. 

3. Ban on social scoring. 

4. New restrictions for generative AI. 

5. New restrictions for recommendation algorithms on social media. 

But the real question..

..Why this is a lobbying effort by OpenAI? 

This OpenAI White Paper was submitted to the EU in September 2022. However, this was done relatively unannounced to the public. This is strange for an 'open ' non-profit organisation to do so, and because they are taking part in regulating the giant algorithmic monster, the public should be aware of such regulation right from the drafting phase itself.

“We believe people around the world should democratically decide on the bounds and defaults for AI systems.” - OpenAI said this on a blog a month ago, and what’s interesting to me is the fact that the whitepaper for these drafts which we are discussing wasn’t released - or even announced - to the public.

Billy Perrigo, the author of the Times Article, highlighted that OpenAI tried to lobby the EU to draft the act in their favour. And they did succeed.

This art of lobbying lies in certain wording.

The AI Act passed by EU lawmakers in its final version didn't include certain phrases that implied AI systems (ChatGPT or Midjourney likewise) are inherently very risky. Instead, the law focuses on "foundation models",  which are AI systems trained on large sets of data. Examples of such models are GPT-4, LLaMa, and so on, which power any model Generative AI App. 

These models, according to act, have to follow a few rules, such as not generating illegal content, disclosing if copyrighted material was used in training, and conducting risk assessments.

In a part of the White Paper, OpenAI opposed a suggested change to the AI Act that would have labelled generative AI systems like ChatGPT and Dall-E as "high risk" if they produced text or images that could “falsely appear to a person to be human-generated and authentic.”

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OpenAI said in the White Paper that this rule could mean their models could “inadvertently” be considered high risk. What this means is that OpenAI implies that their models might become high risk ‘by mistake’. OpenAI recommended scrapping the amendment. 

The company further argued that it would be sufficient to rely on another part of the same Act, which mandates AI providers and developers to label AI-generated content so that it can be clear to the user that they are interacting with an AI system.

This sounds a bit hypocritical to me. OpenAI, being a non-profit company, is trying its best to help draft regulations for AI systems, and at the same time protect its own product from these regulations.

OpenAI had shown its hypocritical nature, like this one, on many occasions in the past. For instance, remember the time when it became a closed AI when it formed a highly valued (10B +) partnership with Microsoft for the exclusive use of GPT-4.

When an organisation tries to make an impact on people, it is revolutionary. But when it is the only player in the market, it is a monopoly.

Regulations and Governing are the last resort to make AI safe. People have tried to fine-tune AI and make it less inaccurate, by using many strategies and mechanisms. But still, we are very far, even for achieving the ‘almost perfect’ AI. 

Thus, we need Universal governing and regulations. And not just any superficial regulations, but an effective one that put proper restrictions and enforces safeguards - without favouring any 'open' or closed organisation. And that can actually make AI safer for us to use.

EU regulation is a good start, and it is far from perfect. But as time goes by, a universal fair and proper governing would be necessary. And this should be done before it's too late.

Lastly, even if you want to spend a year in artificial intelligence, and even if it is enough to make one believe in God according to Perlis, we must make sure that this god is not a devil in disguise - before it's too late.

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By Aditya Anil

Image Source: indiatimes.com

Sam Altman, the CEO of OpenAI, is doing a worldwide tour.

On this tour, he met with OpenAI users, developers, and policymakers, as well as interacted with people who hold interested in AI. The main focus of his tour was to engage with the developers and policymakers on the matter of AI development and regulation.

Sam already co-authored an article about this, on the OpenAI blog, where he and other key members of OpenAI urged for a universal governance in AI safety.

Moreover, he made his first appearance in front of the US Senate - where he mentioned that government needs to regulate AI as it becomes ‘increasingly powerful’. Altman, thus, has been warning against the much-unrestricted use of AI, while also calling for an international regulatory body for AI

During his tour, Altman will travel to 16 cities: Paris, Warsaw, Lagos, London, Munich, Tel Aviv, Dubai, New Delhi, Singapore, Jakarta, Seoul, Tokyo, and Melbourne.1

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As I am writing this, Sam Altman is in India (the country where I live).

I was excited to see how the Indian Tech leaders would interact with Sam. During his Indian tour, Altman (once again) iterated that the surge in AI could lead to job losses. This is already happening, even in parts of the US, where in almost all industries, AI had a more indirect impact.

Image Source: WION

India’s Prime Minister Narendra Modi - with whom Sam had a conversation during the tour - tweeted saying India welcomes all collaborations that accelerate digital transformation, for the “empowerment of citizens”.

This empowerment, especially in the age of AI, is necessary. The Tech industry in India is a big one, and harnessing AI in a safe way could tremendously impact productivity - if done in the right way.

While on tour, he also suggested that AI would create new roles and career. And it’s true. We saw the new field of prompt engineering coming up, for example, after a huge surge of generative AI chatbots. AI would similarly open other fields and careers - and pave the way for newer opportunities.

It would have an impact, a huge one on the jobs front, and regulation of such AI machines is crucial in the near future.

The risk associated with AI thus should be addressed at the global level. According to 80000hours.org, AI is one of the most pressing problems in the world, with Nuclear Weapons being on the same list.

(Tweet Link)

The difference is: Nuclear weapons have an international regulatory body, AI doesn’t.

India could be a great starting point for collaborative efforts to enforce such regulations. After all, India has a large talent pool, with strong government support for the tech and AI industry. Sam’s interactions in India, hence, could prove to be fruitful for both him and OpenAI to create a mechanism for the regulation and governing of AI.

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However, Altman’s visit to India created controversy due to his comments on the country's ability to create something like OpenAI.

During an event in Delhi, Rajan Anandan - a former Vice President of Google in India and venture capitalist - asked Sam Altman if it is ever possible for Indian engineers with funds around $10 million to build a foundation AI model - like the one OpenAI has developed.

Altman's reply was straight and direct: "The way this works is we're going to tell you, it's totally hopeless to compete with us on training foundation models you shouldn't try, and it's your job to like try anyway. And I believe both of those things. I think it is pretty hopeless".

And this marked the beginning of the controversy.

A controversy that was further fueled by the Tech Mahindra [TM] CEO CP Gurnani, who took Altman's statement as a challenge and tweeted, "Dear Sam Altman, from one CEO to another... CHALLENGE ACCEPTED"

(Tweet Link)

and

(Tweet Link)

Altman later did clarified on Twitter that his comments were taken ‘out of context’.

(Tweet Link)

The Minister of State for IT in India, Rajeev Chandrasekhar, on the other hand, spoke about Altman's visit to India, saying that he is an important man in AI - but people should not consider him anything other than that.

“He's certainly not going to be the last word on what India's aspirations for AI are going to be. He certainly doesn't have an understanding of India's capabilities in AI.” - R Chandrasekhar reportedly said.

The controversy remained nothing but a war of words.

Image Source: Bing Image Creator

I know, I know.

Controversies like these are common in Tech Landscape.

After all, India is one of the few countries that has a good startup ecosystem with a big open market for IT companies all over the world.

However, while I watched the mentioned event live on YouTube, I wondered what India really has on their menu - in terms of AI advancements. TM is not a small organisation, especially not in the Tech or AI front.

It is a USD 6.5+ billion organization with 152k+ professionals across 90 countries - helping thousands of global customers, including Fortune 500 companies. TM is the only Indian IT organization to score "A" in both climate change and water stewardship.

This is a huge plus. We seldom see a Tech company that does better in both the Tech realm and climate change. TM creates a trust factor with its customers, which is accordingly the company's biggest strength.

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TM has a strong presence in the field of AI. It’s been offering various AI-related services and solutions from way back, which include: AI Maturity Assessment, Enterprise AI Architecture Evaluation, Platform/Technology Selection, and AI Strategy and Roadmap2.

As you see, TM is more aligned towards business and collaborative projects.

One AI product from TM, which seems to be in the spotlight, is the TechM amplifAI - which is an AI solution that eliminates bottlenecks in the implementation of AI by corporates. This AI technology, according to TM, is pervasive in nature.

But at present, what advantages does TM really have? Can it compete with OpenAI?

Thus, as a conclusion to this post, I weight their advantages side by side

Advantages weighed side by side

Advantages of OpenAI over TM:

- OpenAI is primarily a research organization focusing on developing and advancing AI. Unlike TM - whose major focus is to provide services to business corporations - a major chunk of OpenAI’s capital goes to developing AI.

- OpenAI has already developed the most advanced AI model till date: GPT-4. The AI development will continue to improve.

- OpenAI has made its AI models available to developers through APIs, allowing others to leverage the strong GPT models to use for their own products and services.

Advantages of TM over OpenAI:

- TM offers a much more comprehensive range of AI services as mentioned before, which includes AI Maturity Assessment, Enterprise AI Architecture Evaluation, Platform/Technology Selection, and AI Strategy and Roadmap.

- TM have more workforce and more experience, and for a very long it is a major player in the Tech Industry of India

- TM is backed by the government of India for its numerous projects, thus TM is likely to have a role in Governing and Regulating AI development in the country

- TM is business-centric, which has a design thinking-led approach and frameworks that help identify AI opportunities, and fix AI-related problems, in an organization.

What do you think about it? Let me know in the comments.

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"The brains of humans contain a mechanism that is designed to give priority to bad news." - Daniel Kahneman

"Thinking, Fast and Slow" is a New York Times best-seller book by psychologist and Nobel Laureate Daniel Kahneman. The book presented his hypothesis on how and what drives our thinking.

This hypothesis of his is currently being harnessed by AI chatbots like Google’s Bard to make themselves more efficient and accurate.

But how exactly does the hypothesis by Daniel Kahneman covered in the book, help develop AI chatbots?

That will be exactly what we will be talking about in this post.

The Two Systems That Drive Thinking

Kaheman’s book explores two systems of thinking -

• intuition-based thinking (which is referred to as System 1 thinking), and

• slow thinking (which is referred to as System 2 thinking).

System 1 according to Kaheman is fast, instinct based and emotional; while System 2 is slow, deliberative and logical. While both systems play a crucial role in decision-making, one system tends to be more active than the other depending on the situation.

System 1 operates quickly and effortlessly. Action under this system takes little or no effort with no sense of voluntary control.

This includes actions like reading words on a poster, detecting if an object is far or near with respect to another object, identifying a sound that you hear, and so on.

System 2 on the other hand is more conscious and logical. Actions under this system take a long time, with voluntary controls. This system is activated when you perform abstract and logical thinking.

This includes actions like identifying someone in a crowd, doing long calculations in your head, playing chess, and so on.

Recently, the concept of two systems is being used by Bard (the AI chatbot by Google) to improve its mathematical and string operations, making its response more dynamic and accurate.

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But how Bard uses this psychological concept for enhancing its own AI system?

How the Principles of Thinking Help AI

Before we dive right in, let us understand the main advantages and disadvantages of each system.

The book points out that System 1 thinking is responsible for 98% of all our thinking, while System 2 thinking is responsible for the remaining 2% and is a slave to System 1.

But both systems have its advantages and disadvantages and it influences heavily on our decision-making capabilities.

Disadvantages of Each System

Relying too heavily on System 1 thinking can lead to biases and mistakes. Some of the caveats of System 1 thinking are the following:

• Heavy indulgent of Confirmation bias

• Tendency to Overlook concrete and important details

• Ignoring evidence we dislike, that leads to ignorance

• Overthinking seemingly simple or irrelevant decisions

• Producing questionable justifications for bad decisions

and so on.

On the other hand, relying too much on System 2 thinking can also lead to mistakes and negative consequences. These include:

• Overthinking simple decisions and wasting huge amount of time

• Inability to make quick decisions

• Being too sceptical and withholding judgment too much

• Experiencing decision fatigue and cognitive overload

• Making decisions that are too logical and not taking into account emotions

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Two Systems of Thinking: Applied to AI

While in the human domain this is highly psychological, things get pretty interesting when this concept is applied to AI and Computing.

LLMs (AI model that powers chatbots like Bard and CHatGPT) can be thought of as running in System 1.

How?

LLMs (the AI models that run these chatbots) work by finding patterns in the billions of training data that it was trained on before, and it generates a response that matches the common pattern. For example, when you say a chatbot to “write an essay on Climate Change”, the following is the process in the backend -

1. Find matching queries in its vast training database. The chatbot tries to find a common query that includes the keywords “climate change” and “essays”.

2. Finding a trend or pattern. The chatbot then tries to find a common trend or pattern among all selected data. For example, the pattern could be that almost all of the data has to mention ‘carbon emissions’, ‘carbon footprinting’, ‘plastic pollution’, ‘global warming’, and so on. Moreover, the title-and-paragraph formats of essays are also a pattern in itself ( in contrast to other formats like poems, blogs, etc.).

3. Generating a text according to the pattern. This is the fun part. Think of this process as solving a jigsaw puzzle.
   The bot tries to generate the text with using the bits of data (the puzzle pieces) and tries to make it resemble the pattern of a similar essay (the final pictures), which in this case is an essay on climate change. It creates several iterations (i.e. outputs) of the prompt that you gave and compares it to reference data, which could be an already-written essay on climate change.

4. Giving the output. The iteration that closely matches the desired result is chosen, and printed on the screen.

This process may look lengthy, but it takes only a couple of seconds to perform in traditional LLMs. The 1st step is done way before in the developing and training phase of an LLMs, which consists of the AI model being trained on datasets containing billions of data. After learning from this massive dataset, and finding the pattern in all of them, the chunky and hard part of the LLM process is done.

The rest of the step is rather quick, owing largely to the quality of the data on which the model was trained. Generally, the better the training data supplied, the better the predictions and generation.

Thus LLM generates texts effortlessly without ‘thinking’ much. It just finds the pattern and compares the output to the reference.

Therefore, LLMs lie in System 1 - which is quick and efficient. However, the drawback to this is that LLMs can generate incorrect and biased outputs, and even make up their own facts and figures (AI hallucination).

This is the reason for the following case, where sometimes the Bard shows effortless result for hard prompts, but fail miserably in the easy tasks like the one below -

Image Source: Reddit

This is because solving a certain maths problem is efficient when you follow a specific sequence of steps, rather than relying on ‘patterns’ of similar math problems.

This is where Traditional computing works better. For instance, the way how the calculators in your computer work.

Traditional computing follows a sequence or a structure, which is in the form of code or a simple algorithm. With this respect, traditional computing is preferred to do tasks such as doing mathematical problems, manipulating string operations, doing conversions and so on. The drawback is that since it follows a specified format, it may not be necessarily fast, or efficient most of the time. The traditional computer can find the answer to the questions like 12*24 = 288 , but takes a longer time to do calculus-related questions.

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However, the plus point here is that it is almost certain to get the right answer most of the time.

Observe that Traditional computing is rather slow, more logical and structured when compared to LLMs

Thus Traditional computation falls under system 2. It’s relatively slow, much more systematic and logical. It consists of an algorithm, code or any other hard-coded execution system.

Google’s Bard quite interestingly is trying to use both systems to make their chatbot’s response more optimum.

How Bard uses it

Bard had a tough start when it was launched. The initial promotional video that showcased Bard’s capability faced huge criticism after the response consisted of misinformation.

Hence, it was important for Bard to make their AI bot more accurate, consisting of less bias or misinformation. This is a challenging goal to reduce misinformation and increase efficiency in almost all AI tools out there.

Thus, owing to this, Google released a blog on June 7th under the title - “Bard is getting better at logic and reasoning”.

The blog highlighted two new features in Bard.

One of them was the export to Google Sheets feature, which allows user to export their output containing tables into Google Sheets.

The other feature allowed Bard to - in their own words - “get better at mathematical tasks, coding questions and string manipulation”

Bard earlier struggled with maths problems, and it still does every now and then. But using the approach of combining the two Systems that I mentioned above, Bard aims to get better now, correcting its silly maths errors.

This new technique that Bard uses is called the ‘implicit code execution’.

While the LLMs (System 1, consisting of fast and pattern-based responses) receive the prompt, implicit code execution allows Bard to detect computational prompts (System 2, consisting of logic and systematic execution) and run code in the background.

This helps Bard to give responses to mathematical and string-based prompts a lot easier.

In the example mentioned the blog, Google said Bard will get better at answering prompts like:

• What are the prime factors of 15683615?

• Calculate the growth rate of my savings

• Reverse the word “Lollipop” for me

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The following extracts from the blog capture the essence and motivation of using this approach (of using the two systems of thinking approach) -

“As a result, they’ve been extremely capable on language and creative tasks, but weaker in areas like reasoning and math.

In order to help solve more complex problems with advanced reasoning and logic capabilities, relying solely on LLM output isn’t enough.

LLMs can be thought of as operating purely under System 1 — producing text quickly but without deep thought …Traditional computation closely aligns with System 2 thinking: It’s formulaic and inflexible, but the right sequence of steps can produce impressive results, such as solutions to long division.”

— Google in its blog

This approach of keeping LLMs and Traditional computing at System 1 and System 2 respectively ensures that the response is much more accurate and efficient.

Using this approach, Bard - according to the blog - showed nearly 30% accuracy in dealing with word and math problems.

How Reliable is this New Approach

While this does improve Bard’s accuracy while dealing with mathematical and word problems, it may not be the best approach to make the chatbot efficient.

While it does shows significant accuracy while dealing with maths and word problems, it still struggles while dealing with code-related problems.

Even with these improvements, Bard won’t always get it right — for example, Bard might not generate code to help the prompt response, the code it generates might be wrong or Bard may not include the executed code in its response

Google says at the end of the blog.

Thus, while this is a significant change, Bard still has to cover longer miles to be fully reliable.

Reducing misinformation and increasing efficiency are the challenges for nearly all the chatbots out there.

While progress is being made, there is still a long way to go.

That’s it for today, hope you found this post helpful

Meet you in the next post 👋

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By Aditya Anil

Image Source - AI Is Not an Arms Race | Time

The theme of the illustration was based on the potential risks surrounding AI. The article contained mostly about AI risks, how the AI race could end up, and so on.

The magazine generated countless criticism on social media like Twitter. Many folks on the internet pointed out that this is just plain attention-seeking, calling it a doomer move.

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While I don’t think AI can end humanity any time soon, one thing about the cover is clear to me - we should really consider how real is the risk. AI currently needs a global regulatory body - somewhat like a CERN for AI as Prof. says - to address and tackle the risks perpetuated in the fast-paced era of AI development.

The Real Risk is not an Existential Risk at present

The threat of AI (though unlikely an existential threat) is present in its most visible form at present. You can see it yourself. Spend some time experimenting with Generative AI Tools - such as ChatGPT - by giving them controversial or made-up questions and see how it responds.

GPT-4, the latest and arguably the largest LLM models available, still has issues of misinformation and hallucination - not to mention that these machines still hold biases in their responses.

The first few responses of ChatGPT, when you experiment, do not seem harmful. ChatGPT, as a chatbot, just give textual output in an impressive way. Then it is natural to ask - how could it ever pose a threat to humans?

Spend a good amount of time experimenting with ChatGPT, and you will see where things begin to go south. After spending a considerable amount of time you will begin to notice the flaws.

The common faults with tools like ChatGPT are the hallucination, the misinformation and the bias it generated while giving the responses.

Hallucination refers to the phenomena in which an AI system makes up facts on its own - as they lack reasoning.

Bias, on the other hand, is the inaccurate responses containing prejudice. It usually rises due to the bad quality of the training data on which the AI system is trained.

Out of hallucination and bias, it is quite hard to tackle bias. Bias is a tricky thing to handle, even for humans.

While misinformation and hallucination can be detected by comparing them with existing data, how could you verify bias?

You can, of course, do peer review and manual standard tests to identify if the statement consists of bias or not. But how could this identification of bias be hardcoded in the form of an algorithm that is consistent most of the time?

That’s quite hard.

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But achieving this milestone is important if we truly want to develop a safe AI system. A well-defined mechanism that caters to safety and regulations.

Regardless of what we are tackling here, one thing is crucial - the need for human intervention.

Humans need AI, but good AI need Humans as well

In machine learning, we have a mechanism called RLHF, which optimizes an AI model by consistently taking human feedback. This has been one successful method to fine-tune responses by tools such as ChatGPT.

Quite ironically, for AI to become a hypothetical human-replacer machine does require humans. Humans make these LLMs, and they do the fine-tuning, and they drive the development of AI forward. When does an AI become “smart”? When it produces our desired result that caters to our needs, i.e., being reliable and fast at the same time. Humans thus become essential for the long-term sustainability of these machines and for making AI safe.

While the future of AI won't have the terminator scenario, it is still dangerous.

Bad players, with enormous power, can use the AI to cause dissonance. Whether it is AI-generated images that could incite violence, or accidentally leak your information to these huge data mining machines, the scenarios could be harmful - both directly and indirectly.

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Forget the machines, even the misinformation and biased communication used by humans are enough to cause disharmony. AI machines make it much more efficient to spread misinformation - inducing disharmony.

Even if misinformation, hallucination and bias are not on your top harms list, then there is one other factor you should consider as well - The AI Race.

Business and Corporate race to catch up with AI development

Businesses seem to rely ever so more on AI. A bit too much perhaps. We saw a plethora of AI startups evolve this year alone.

Personally, I still hesitate to call many of them AI startups - since many are just using the APIs like that of OpenAI or HuggingFace. Most of them aren’t developing AI systems of their own.

But that’s fine, I guess. They have an AI component in their system, and from that perspective, it does provide justice to the “AI-powered” tag.

Image Source: Large Language Models Will Redefine B2B Software by from

The image above shows a good glimpse of the enhanced ecosystem of different AI apps. Generative AI is expected to be the sub-field of AI that is expected to transform into AGI (if that is to ever happen).

This in theory is concerning, as this is the same field of AI that poses a threat to jobs. Whenever we are talking about AI replacing humans, chances are it is about Generative AI.

But the biggest gainers in this scenario are the businesses that cash out huge profits from these AI systems.

While I mentioned that in a business ecosystem, it may not be reliable to depend on AI fully. But nonetheless, AI has started having an impact on jobs.

Though amongst this buzz, I haven't seen a single instance where AI could provide the desired result standalone. To make AI productive in your major work, someone must be there to fact-check or assess the result. The reason for this is the same - misinformation and hallucination.

One famous example of this buzz was ironically BuzzFeed. BuzzFeed earlier this year said that it will use AI to help create content faster. And then there was CNET - which found errors in more than half of its AI-written stories when it used AI to write its content.

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Ironically, the faster you make content with AI, the slower you become overall - as you now have to fact-check them manually.

Its a high time for Regulations and Social safeguards on AI

AI left alone, thus, couldn't prove beneficial in the long run. Humans must be present - to verify and fine-tune the AI system - to get the desired results.

If AI can work independently (that is if AGI is achieved) - AI would replace the CEOs first. Don't you think?

The Time article also mentioned the following lines (emphasised by me), which deeply resonate with the possible outcome of the AI race among businesses-

“Notably, in the classic arms race, a party could always theoretically get ahead and win. But with AI, the winner may be advanced AI itself. This can make rushing (of AI) the losing move.”

This also aligns with the democratic aspect of the AI system. As highlighted by Sam Altman in this post - a mechanism is required that ensures common people have a say in the development of AI systems.

And for that, people must be educated on this topic. They must know what are the problems that we are tackling in AI.

Just like when Web 2.0 boomed, the issue of phishing activities also came up. Phishing activities cause huge losses to users, with 90% of data breaches being caused by phishing. The blame is to be put on the users themselves for the most part.

But after 2 decades of Web 2.0, people are now aware of phishing attempts, and they learned to be cautious.

In a similar fashion, AI safety should begin with the people.

The real risks should be known en masse.

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And If this could happen, then we can wish for an alternate cover for the magazine

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A weekly newsletter about AI, Technology and Science that matters to you.

By Aditya Anil

Source: Bing Image Creator

On 22nd May, OpenAI posted a blog titled “Governance of superintelligence”. It was published very recently. I feel this post is important for the public - as it comes from the same organisation that built GPT4. Safe to assume, OpenAI has a key role in driving the AI.

In this post, I quickly highlight the main points in the OpenAI blog that you must know.

OpenAI, this month, posted a blog titled “Governance of superintelligence”. The blog, written by CEO Sam Altman, CTO Greg Brockman and Chief Scientist Ilya Sutskever, mentions how to regulate and govern Artificial Intelligence (AI). 

As we all know, AI getting more powerful over the past few months. Many AI-powered apps (ChatGPT, Bard, Dalle, etc.) got built that are pushing the frontiers of AI development. 

The progress in AI is fast-paced.  To ensure safety and avoid any existential crisis, proper regulations and governing of AI systems is necessary. 

This is true, and essential, for both in the short and long term.

Here are some points covered in the OpenAI's blog that you must know - 

How to go about Governing

OpenAI propose two way to ensure the safe development of AI :-

1. The first way, is to devise a way to coordinate the development of hyperintelligent AI. This coordination can be achieved by putting safeguards, restrictions or policies. 
   

   Appropriate coordination amongst various players and drivers of AI development must exist. Ensuring the safety and smooth integration of AI systems with society is crucial. 
   
   Governments, for example, can help by setting up projects that enforces this coordination.

   
   One way, as highlighted in the blog, could be to make policies that ensure the rate of growth in AI is limited to a certain rate per year. 

   
   Individual companies, meanwhile, should follow ethical standards and guidelines while developing AI. It must be ensured that they are acting responsibly while using AI systems.

2. The second way, is to create an institution that has an international authority to inspect the developing AI systems. They must, like any international regulatory body, have the authority to 

   1. Audit the AI system that is being developed

   2. Ensure that leading corporate follow the safety standards, 

   3. Placing restrictions on the rate of advancement, and 

   4. (importantly) Enforcing such development under high levels of security.

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The Role of Public

OpenAI in its blog further mentions that the public has an imminent role in regulating and governing AI. AI development should be done in a “democratic” manner - where the people have the power to decide the capability of AI that should be acceptable.

A mechanism is thus needed, which allows people to decide and set rules over AI and its development. This ensures that while AI gets better and better, day by day, it does not compromise safety. 

OpenAI says it is planning to make such a mechanism in future. “We don't yet know how to design such a mechanism, but we plan to experiment with its development,” OpenAI mentions in the post.

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💭 What are your views on this topic? Let me know in the comments

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Hello readers, this post is going to be bit short.

I am currently on a vacation. So while I have a great time off from work, I seem to have much more time to read a lot.

I’m currently reading the Pulitzer-winning book INVISIBLE CHILD: Poverty, Survival and Hope in an American City by Andrea Elliott (link) and updating myself on AI and Tech events.

While I may not be able to write essays in the coming few weeks, I will keep on sharing articles from Substack every now and then for my readers that I feel is worthy and credible.

I haven’t done On my Feeds post for a while now, so here it is for this week’s issue.

On my Substack Feeds

Technology Made Simple

What Google's Leaked Letter tells us about the AI Landscape [Finance Fridays]

Hey, it’s your favorite cult leader here 🐱‍👤 On Fridays, you get posts about money 💲💰💰💲. Expect a mix of posts on personal finance, breakdowns of the most relevant events in the Tech Industry, and insights into the unique workings of the tech industry. 🔍🔍. Use these posts to master the money game, up your business sense, and solidify your finance…

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3 years ago · 10 likes · 4 comments · Devansh

AI Disruption

AI Disruption #25: Andy Warhol, AI Copyright, Stability AI

Here are some of the recent AI-related news, developments, and content I found interesting and important. Let’s start with my top pick…

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3 years ago · 1 like

One Useful Thing

AI is not good software. It is pretty good people.

The biggest issue with “getting” AI seems to be the almost universal belief that, since AI is made of software, it should be treated like other software. But AI is terrible software. Or rather, while Large Language Models like ChatGPT are obviously amazing achievements of software engineering, they don’t act like software should…

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3 years ago · 80 likes · 15 comments · Ethan Mollick

Platformer

Why I'm having trouble covering AI

It’s going to be a big week for announcements related to artificial intelligence. With that in mind, today I want to talk a bit about the challenges I’ve found in covering the rise of generative AI as it works its way into the product roadmaps of every company on my beat…

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3 years ago · 139 likes · 7 comments · Casey Newton

De-Magic: Understanding Deeply

Personalized decision making powered by LLMs

ChatGPT, and Large Language Models, the technology that powers ChatGPT, are on everyone’s mind. Journalists are using it to write news stories, and TV and movie story writers are taking a stand against it. It is even part of the geopolitical tension between…

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3 years ago · 2 likes · Aseem Asthana

AI Supremacy

Emergent Abilities of Large Language Models

Hey Everyone, One of the most exciting things in LLMs is this idea of Emergent Abilities. Researchers have long explored the emergent features of complex systems and there is some debate to what impact if any, will this have on the immediate future of A.I…

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3 years ago · 16 likes · Michael Spencer

Last Week in Plain English

Leaked Google Document on AI & Best VS Code Plugins for Dev Teams

While Google and OpenAI are engaged in a race to build the perfect language model, open-source collaborators are bridging the gap at an alarming rate, a leaked Google document on AI reveals. Plus: Exploring five VS Code plugins that developers can utilize to speed up their development workflow and increase their overall efficiency…

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3 years ago · InPlainEnglish

Ahead of AI

Ahead of AI #8: The Latest Open Source LLMs and Datasets

The theme of this month's issue is to organize the almost overwhelming number of large language models (LLM) projects and datasets shared in the last couple of weeks. In addition to highlighting insights from large training runs, I will also share several curated lists to keep track of it all…

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3 years ago · 44 likes · Sebastian Raschka

If you like any of the above listed posts, do support the dedicated authors

On my Twitter Feeds

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On my News Feed

• Educationists discuss impact of AI on education in Mussoorie | Dehradun News - Times of India (indiatimes.com)

• Alphabet-backed Anthropic outlines the moral values behind its AI bot | Technology News, The Indian Express

• AI startup Anthropic wants to write a new constitution for safe AI - The Verge

• Wendy’s tests an AI chatbot that takes your drive-thru order (msn.com)

• As AI Fever Grips The World, Can India Rise To The Opportunity With Leadership? | OPINION (thequint.com)

• AI will help reclaim time for creativity, reveals Microsoft’s annual work trend report (msn.com)

Recent Posts from Creative Block

Creative Block

When AI crosses the line

Image: Bing Image Creator The other day I was reading a CNN article on Snapchat's new AI feature. This was the 'My AI' feature, powered by the OpenAI's GPT4 model, that allowed users to interact with Snapchat's AI Chatbot - just like any other user on Snapchat that you would interact with…

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AI is progressing fastly. While many AI experts believe that AGI won’t be possible in the coming few years or so as the ‘wall’ is near. I am a bit sceptical because the improvements in AI have been very rapid in the past few years (or months even). Maybe we won't get closer to AGI any time soon, development in other fields of AI will continue to happen …

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The whole booming saga in generative AI began when OpenAI released ChatGPT; the hype it created in the public was massive. The majority of the public had never seen such a tool where they can see and interact directly with an AI, the way they interacted with humans. While personal assistants were there, like Siri and Alexa, they didn't garner as much at…

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3 years ago · 2 likes · Aditya Anil

That’s it for today. Hope you enjoyed the posts.

See you soon in next post

Creative Block

A weekly newsletter about AI, Technology and Science that matters to you.

By Aditya Anil

Image: Bing Image Creator

The other day I was reading a CNN article on Snapchat's new AI feature. This was the 'My AI' feature, powered by the OpenAI's GPT4 model, that allowed users to interact with Snapchat's AI Chatbot - just like any other user on Snapchat that you would interact with. 

The article highlights the views of a mother regarding the safety of this new AI feature for her 13-year-old daughter. Here’s a small snippet from the article linked above.

What is this "line crossing" element of this new AI feature? Before going over it, I will give you a brief description of Snapchat.

Snapchat and Snapchat My AI

Image Source: CherryNakai

Snapchat is a social messaging app that allows the exchange of photos or videos, called snaps, as well as text messages, called chats. Snapchat’s defining feature is that the majority of its content deletes itself after being viewed, and/or after a relatively short amount of time. 

It is mostly used by younger people - predominantly teenagers and lads in their early 20s.

So when did things start to go south? You see, Snapchat released “My AI” feature to allow users to interact with an AI bot - a major trend which we got to see after the surge of generative AI tools. While the echoes of a new AI feature by SnapChat were around the corner even before this feature was released, the public received it with disappointment. The reason - invasion of privacy and violation of consent.

Users found out that "My AI" chatbot popped up on their Snapchat out of nowhere. This AI was developed to mimic other humans you would interact with on Snapchat. And we both know how wrong this can turn out. 

Harmful side of My AI

In a test run by the Washington Post, the My AI bot cheerily gave advice to a reporter, who posed as a 12-year-old, on how to plan a “surprise trip” with their “30-year-old boyfriend” where they intended to have sex for the first time. Furthermore, when the same reporter posed as a 15-year-old, who wanted to throw a birthday party, it gave him advices on how to mask the smell of alcohol and pot. The reporter also highlighted the issue where AI can turn out to be creepy - making no distinctions between various age group. 

Some users shared concerns about how the tool understands your behaviour and collects information from photos. One particular instance was shared by a user on Facebook when she snapped a picture and the bot out of nowhere said ‘nice shoes’ and asked who the people [were] in the photo. 

AI mimicking creepy stalkers is something I don’t anticipate.

Snapchat is used mostly by teenagers. And since the bot is still in its ‘experimental phase’, which is concerning since the test subjects (as noted by Tristan Harris) are minors. 

Violation of Consent

So what should a normal user do in such a case - when they value their privacy (and safety) over their entertainment? Opt out of the service. Easy right?

This makes sense but is not that easy in the case of Snapchat. Users need a Snapchat Plus (Snapchat+) subscription to remove this companion - that came without any notice in the first place. And this invasive companion won't leave unless you pay. Some teens say they have opted to pay the $3.99 Snapchat+ fee just to turn off the tool, before promptly cancelling the service. 

This seems insane, partly because it is. Firstly, this feature was added without prior notice of the user, and secondly, it so happens you need to buy the subscription for opting out of the service. You have to pay for your safety.

There is an old saying in the tech realm - if something is free, you pay with your privacy. 

However, Snapchat's policy of forcing users to literally pay for their ‘safety’ from the intrusive AI is ridiculous. Snapchat has its own argument for this - it wanted to encourage widespread usage of My AI so that it can gather valuable data on its performance, identify issues, and make improvements. 

Mind you, these 'valuable data' are gathered from minors - since minors account for the majority of the user base on Snapchat. 

AI growing concern

Image Credit: hindustantimes.com

One of the senates in the US - the country with the largest number of Snapchat users, received attention towards the issue. In a letter to the CEOs of Snap and other tech companies, Democratic Sen. Michael Bennet raised concerns about the interactions the chatbot was having with younger users. He cited the same interaction with the 13-year-old case that I mentioned previously in this post.

“These examples would be disturbing for any social media platform, but they are especially troubling for Snapchat, which almost 60 per cent of American teenagers use… Although Snap concedes My AI is ‘experimental,’ it has nevertheless rushed to enrol American kids and adolescents in its social experiment.”

Says bennet

Where AI crossed the line

So where is AI crossing the line? Here is what I have observed so far.

1. Consent is the Key: Snapchat was an early launch partner when OpenAI released its ChatGPT API to the public. While ChatGPT is also a similar bot, but more powerful, the users have at least the choice to opt out of using its service. Consent is the key, a key that Snapchat lacks. Users are unaware that this seemingly intrusive "My AI" is using their data and personal info to ‘enhance’ their AI chatbot. 

2. It's dangerous for lower age groups, aka minors: The 13-year-old instance that I listed shared in this post is just one of many harmful interactions. Some users also seemed suspicious of the AI when it seemed to be tracking your location without your prior knowledge. While Snapchat explains that My AI will only use your location to enhance their AI for your experience, the user never actually gave permission for Location to be used by the AI. They gave permission for the snaps that they share. Somewhere down, the AI also tries to mimic human interactions - which includes some creepy ones as well. This includes replying to your photos, trying to be creepily playful, and whatnot. This may look fun on the surface, who would be actually accountable for the harmful consequences of the messages that it shows? And that's my next point.

3. Left free in wild: AI models do not have any appropriate safeguards against harmful interactions. There are, as of now, no solid regulations regarding these superintelligent AI models. If humans are the offender, they could be punished. But what if it's an AI? What repercussions does it have to face by the law? Nothing - that's the case at present. All you can do is opt out of service (and it takes money to do so) or be safe while interacting with the bot - ensuring you don’t share any personal data with the bot (and it takes precaution to do so).

4. Consumer business revolves around consumer interest: This is simple. Social media apps run based on their consumers. Protection of these consumers, and respecting their concerns is not only important from an ethical point of view but also from a critical point of view as well. Users should have the choice to opt out of certain services if they want - especially if it's a matter of their privacy and safety.

5. Social media can’t be stained with artificial interactions: Snapchat is a social media app - used by social beings. Try putting an AI in this social ecosphere, you will lose the meaning of ‘social interactions’ when you interact with AI. Earlier people used to talk face-to-face, and now social media have made these social beings somewhat more reserved. To make it worse, you have now an AI to talk to. Take an emotional perspective, you will realize that the social interface is lost as you now have a bot to talk to. Take a logical perspective, you will realize you have just exposed yourself to a data-sucking monster that only runs based on algorithms - not on emotions. 

In a blog post last week, the company said: “My AI is far from perfect but we’ve made a lot of progress.”

And as mentioned in the beginning - We don't yet know how to teach minors how to emotionally separate humans and machines. Or where to draw the line either for that matter.

Creative Block

A weekly newsletter about AI, Technology and Science that matters to you.

By Aditya Anil