The Mastermind Behind GPT-4 and the Future of AI | Ilya Sutskever

When he says we will find out very soon , it really does send chills to my spine!

takeaways:
• [00:04] Introduction of the speaker, Craig Smith, and his guest, Ilya Sutskever, co-founder and chief scientist of OpenAI and primary mind behind GPT-3 and ChatGPT.
• [01:00] Sutskever's background and interest in AI and consciousness.
• [02:30] Sutskever's early start in machine learning and working with Jeff Hinton at the University of Toronto.
• [03:45] Sutskever's realization about training large neural networks on big enough data sets to solve complicated tasks.
• [06:33] The breakthroughs in convolutional neural networks and how they led to the imagenet competition.
• [08:36] OpenAI's exploration of the idea that predicting the next thing is all you need for unsupervised learning.
• [10:24] The development of GPT-3 and the importance of scaling in deep learning.
• [11:42] The importance of scaling something specific in deep learning and the potential for discovering new twists on scaling.
• At 13:01, the speaker discusses how scaling matters and that even small changes can have a big impact.
• At 13:46, the speaker talks about the limitations of large language models, explaining that their knowledge is contained in the language they are trained on, and that they lack an underlying understanding of reality.
• At 14:32, the speaker comments on the difficulty of talking about the limits of language models and how they change over time.
• At 15:13, the speaker argues that learning statistical regularities is a big deal and can lead to a better understanding of the world.
• At 16:33, the speaker talks about the limitations of language models and their propensity to hallucinate, but expresses hope that this issue can be addressed through reinforcement learning from human feedback.
• At 17:52, the speaker discusses how teaching neural nets through interaction with humans can help improve their outputs and reduce hallucinations.
• At 21:44, the speaker comments on Jana Kun's work on joint embedding predictive architectures, and expresses the belief that multimodal understanding is desirable, but not necessary for language models to learn about the world.
• High dimensional vectors with uncertainty are a challenge for prediction, but Auto-regressive Transformers can handle them (26:02)
• Auto-regressive Transformers work well on images (26:02)
• Large language models learn compressed representations of the real world processes that produce data (29:40)
• The goal is to make language models more reliable, controllable, and faster to learn from less data (33:44)
• Learning more from less data is possible with creative ideas (35:51)
• The cost of faster processors for training language models may be justified if the benefits outweigh the cost (37:48)
• [25:28] The paper makes a claim that predicting high-dimensional distributions is a major challenge and requires a particular approach, but the current autoregressive transformers can already deal with this.
• [26:02] Autoregressive transformers work perfectly on images and can generate images in a complicated and subtle way, with the help of supervised representation learning.
• [27:09] The vector used to represent pixels is like a string of text, and turning everything into language is essentially what is happening.
• [29:40] Large generative models learn compressed representations of the real-world processes that produce the data they are trained on, including knowledge about people, their thoughts, feelings, conditions, and interactions.
• [31:31] Human teachers are needed to guide the reinforcement learning process of a pre-trained model to achieve a high level of reliability and desired behavior, but they also use AI assistance to increase their efficiency.
• [35:10] It is possible to learn more from less data, and there is an opportunity to teach AI models skills that are missing and convey to them our desires and preferences more easily.
• [39:57] In the future, it could be desirable to have some kind of democratic process where citizens provide information to neural nets about how they want things to be.
• [41:15] It is probably impossible to understand everything in a complicated situation, even for AI systems, and there will always be a choice to focus on the most important variables.

Love when Ilya starts teaching everyone.
Nice home page, too.

Ilya talks so smoothly that i couldn't turn off the video literally

You can see there is so much going on in the amazing mind/brain of lya Sutskever. A historical interview.

Thank you for uploading. I learned so much detailed nuances about LLM from this interview. I really like Ilya's way of communicating subtle but important points.

Wonderful interview! Thank you Craig and Ilya!

Thank you for this interview and asking good questions and directing the conversation. Some good passages here to pause and really think about.

Thank you for sharing your insights and explanations Ilya.

Thank you for all the hard work, everyone who do their best for these new systems to be implemented with the least possible disruption to human societies. We are still humans, and we must go from the perspective of love - to the future and beyond. Much gratitude.

Beautiful. Thank you for this interview.

Thank you for uploading, great content, insightful.

That was really good - as someone with a general interest it’s one of the best video podcasts I’ve seen on this subject,
and with a very central individual to the progress being made on AI. I liked the historical reflections at the beginning, it helped put things in context. I’ll be downloading the transcript to go through and will listen again. 10/10 👌

Interesting and important discussion Craig and Ilya. Thank you Mike

GPT-10 + Quantum processor + Boston Dynamics = Terminator

Awesome interview! Questions were great. Please more.

The subtle production of zooming and the downtime used in the intro is a good touch. Always good to show consideration for the audience instead of a ramshackle Facetime.

There was a man in the early days named Ilya. Some say he could have stopped it in it's infancy.

- His intention was specific: to make a very small but real contribution to ai. ( in the time that people were certain computers can't learn, 2003!)
- Auto regressive transformer is a very powerful tool that researchers underestimate.
- "humans can be summerize in sequence", do you remember Devs miniserie!?
- "To predict well, to summarize data well, you meed to understand more and more how the world that produced the data."
- "maybe we are reaching a point where the language of psychology can be appropriate to understand these artificial neural networks!"
- he doesn't believe these models don't have any real understanding of the nature of our world!
- "human teachers are using ai assistance, and they are so efficient." By human teachers, he means people working on reinforcement learning from human feedback.
- "make models more reliable, more controlable, make them learn faster, with less data and less instructions. Make them halucinate less. How far are they in the future? These are topics he intrested in and work on them right now!"
The interesting thing is in OpenAI, he can't talk specifically about what he is working on, the open in opanAI annoy me a little!
- "The costs are high, but the question is, does paying this cost actually generate something useful? Does what we get after paying the costs outweigh the costs?

Great interview!! Thanks for sharing!

My favorite quotes from this interview by Craig Smith:
GPT is, "the first thing that is interesting to scale."
GPT is "prediction compression" and " to compress well you need to understand more and more about the world that produced the data"
GPT has a "shocking degree of understanding of the world and many of it's subtleties... the world as seen through the lens of text."
"Language of psychology is starting to be appropriate to understanding the behavior of these neural networks."

This podcast is wonderful. Thank you very much Craig.

Way to go Ilya! Rocked it.

Excellent. Thank you very much for this.

That dude is fascinating. Thanks for the interview.

Q at 26:40
A: when he says Vector he means like a vector in physics like it has force and direction on multiple planes. When converting something into a vector embedding. It is like to convert an image into an idea so it is behaves like a concept that is stored spatially relative and near by to other ideas. Then you can convert it back. but Also you can use its spatial position in multidimensional space to find related information. also you can put it back from an embedding which is a vector representation of something back to original representation while preserving relatedness positional information. a text sentence it is a list of embeddings, it is an array of "vectors". When you put it back from an array of vectors into a sentence of words. You also get all of the learned associations and the related things about the sentence in addition to the sentence.
There is a new thing in text search engines. Vector databases. It enables to search things based on ideas. It is fascinating you can search in any language and get the same results.

Hi, thank you for this interview. I have a tangential question for you: Would you happen to have any good leads on papers/researchers on the anticipated economic impacts of AI? I'm finding old stuff, but nothing new. Qualitative as well as quantitative forecasts would be really helpful. Thanks for any guidance you can provide.

This is an incredible and valuable interview. I can't believe this depth of knowledge is under 6k subs. I think that's a very scary thing, people is not aware.
Thanks you so much for sharing it with us, for free ♥

Awesome interview thank you for sharing😊

Thank you for the great interview. One followup question I have for Llya is whether hallucinations stem from the compression or the output process. I suspect they are inherently encoded in the embeddings thus it is much harder to totally get rid of by just aligning the outputs.

Some people just exude brilliance. Ilya is one of those people. Listening to him talk and explain things is humbling.

The quality 👌

Beautiful conversation

On the subject of hallucinations, I think they are more clearly explained by the problem space that the engine is trying to navigate. When having no relevant information on the subject, but it is still asked (one might say compelled) to say something, whatever it says must be either off-topic or false.
And I believe Ilya is very insightful when he says the language of psychology is starting to describe these systems, because we have hallucinations, too. Whatever compels us to output something when indeed lacking skill or knowledge about a subject also affects GPT systems as well. When do people hallucinate or ramble? When they have no imposed limits/feedback, like a dictator or celebrity that is never told they are wrong or some guy living all alone in the wild or a child that has not been educated yet. Or a Twitter user. With social creatures it is the meaningful interaction with other social creatures (and the physical world) that generates these limits. Which I find promising and fascinating, because it means that the supervised learning step Ilya is talking about can also be performed by other AIs, not particularly humans. The brain is also composed of two hemispheres that keep each other in balance.
Very interesting indeed.

Ilya mentions at around 18 minutes information compression as the key to meaning. That's the work of Naftali Tishby who has some fascinating youtube lecture videos. The compression of information in order to make sense of the world is reminiscent to Occam's Razor. We know deep learning produces many levels of abstraction during training without human effort and abstractions in a LLM have not been fully explored yet.

Great upload! The future laws put in place as guard rails will be a huge player in the speed of AGI and possible adoption in some countries.

Wonderful discussion and insights❤😊

great interview. thx for doing ths!

I think learning by prediction can go a long way. Kudos to OpenAI, thanks for bringing us this nice tech.

great interview!

Thank you so much for such an insightful interview! Ilya is such a beautiful mind to listen to! On a tangent though - the music track feels so intrusively inappropriate, almost to the point of awkwardness(

Amazing interview

Ilya's soft voice and presentation taught me as much as what he said.

very nice...ideas expressed clearly., really necessary for building a deeper understanding of AI

What an brilliant mind. Great interview

Mein Freund Danke, dass du das geteilt hast

Fantastic content, thank you

Amazing, what a gem! Thank you for sharing. What Rockstar

Just a quick hint for the future videos - instead of recording from the Zoom stream, which is laggy and has low quality - just set your phone to record your video and voice. Your interviewee can do the same. And then you can get a high-quality video and audio as a result.

Fascinating to hear that reinforcement learning techniques could get outputs to not have hallucinations.

Ilya mentioned LLM learn color from text... I was wondering could it be that it learned from the color code in many HTML and CSS files? The RGB, hex code definitely have some structure that a text model can learn their relationships...

Helpful in pretty much any situation. Great.

time for a new audio/video setup, but now i'm going to just conintue listening to this intriguing story

thank you both

Great to hear all this, thanks for the discussion! I want to put in a good word for the Viterbi algorithm. I think it will do better than single-token prediction if you can figure out good ways to move in that direction.

Two guys talking about AI, one of the most impactful technological breakthrough, both with absolutely terrible webcams

Fascinating content, we can make it better with higher quality video to reduce the digital fatigue

Hi Ilya and Chris,
I just want the chatbot to remember my name and my interests when I log off.
I can’t express how profoundly disappointing it is that such a sophisticated neural network forgets your name.
Thanks brothers

16:00 "To predict the data well, to compress it well, you (meaning the AI) need to understand more and more about the world that produced the data." This statement is amazing, inspiring, and chilling all at once.

I‘m wondering if it is planned and/or it is possible to use not-text data like weatherdata, traffic, maps, … as basedata. Maybe I could ask: how is the impact of the moon to traffic accidents? And more like this… Great interview!

great interview questions.

00:04 Introduction and Background
00:21 Ilya Switzerberger's Contributions to AI
01:01 Conversation with Ilya Switzerberger
01:50 Craig's Interest in Ilya's Work
02:25 Ilya's Background and Early Interest in AI
03:00 Working with Jeff Hinton and Machine Learning in 2003
08:00 Breakthroughs in Convolutional Neural Networks and ImageNet Competition
10:02 Introduction of Transformers and Self-Attention
11:09 Scaling and the Bitter Lesson
14:09 Importance of learning statistical regularities
16:00 Prediction and compression in generative models
19:31 Addressing hallucinations and improving language model outputs
21:28 Discussion on joint embedding predictive architectures
24:01 Multimodal understanding and learning from text only
27:33 Efficiency of using an army of human trainers
28:20 Automating the teaching process
29:01 Pre-trained models already have knowledge
31:02 Efficiency of human teachers with AI assistance
31:38 Improving efficiency and precision of reinforcement learning
33:01 Teaching the model desired behavior through reinforcement learning
34:00 Research focus on making models more reliable and controllable
35:02 Learning more from less data
37:00 The relationship between model parameters and data
38:01 The need for faster processors
39:01 AI's impact on democracy and societal management
40:10 Possibility of democratic input in AI systems
Made in chapters,video

Fascinating and scary at the same time. Unfortunately humanity has already proven what it is capable of. I just hope they can stop it from being used negatively.

Finally, something interesting not overhyped!

What a brilliant mind.

I wonder if the notion of 'prediction compression' is congruent with the idea popularized by Numenta's Jeff Hawkins, of a sparse matrix.
----------
Ilya spoke the phrase "AI in the loop". First time I've heard that.
-----------------------------------------------
Also, Andrej Karpathy was at tesla and said pixels are enough. I hear that echo when Ilya says LLMs are enough. (I'm leaving Attention is all you need out of the comparison)

The statement Ilya says about computational irreducibility -- Loosely: There must be a neural network capable of producing intelligence because our brains are literally neural networks producing intelligence/with intelligent output -- as simple of a core as that is, that so fundamentally captures the feasibility and potential reality of AI. That for me is so chilling (good word @Bargains)
That core is also a structural starting point for generating an intelligence; essentially building a system that is granted the ability to sift through permutations of itself; how it identifies relationships, how it connects neurons to one another, the datastructs it uses to connect and store and retrieve and manipulate that data; trying different iterations until superstructures of relations and understanding and cognition start appearing. That is an implicitly successfully (and implicitly terrifying) starting point, and also an ingenius one.
Thank you for this interview!

This guy is amazing

Excellent material thanks!

Thank you ILYA.

Excellent interview!!!
Did I understand correctly that chatGPT is really a LLM large language model not an AI artificial intelligence technology??
I am referring at the idea expressed at 13:21

Together!!!

great to see a top AI expert in KRplus.

fascinating!

Very interesting he described it as learning the word through its projection in the form of text. Maybe it is time to let them loose and have them learn through vision (by stumbling around)

I'd like to hear Ilya articulate the distinction between hallucination and imagining useful possibilities and solutions. Obviously preventing/avoiding harm is not the only 'leash' required of AGI.

Here is what ChatGPT4 thinks of this conversation as asked if there are incorrect statements in the transcript:
[00:13:54] - CRAIG: CRAIG incorrectly claims that large language models like ChatGPT have no underlying understanding of reality. While it is true that these models are based on statistical regularities, as ILYA explains later, learning these regularities can lead to a significant understanding of the world.
[00:26:59] - CRAIG: CRAIG makes an oversimplification by equating converting pixels into vectors to turning everything into language. While both language and images can be represented as sequences or vectors, the nature of the data and the representations are different.
[00:29:17] - ILYA claims that pre-trained models already know everything they need to know about the underlying reality. This statement is not entirely correct, as pre-trained models have limitations, including understanding context, handling ambiguities, and making generalizations. They continue learning and improving over time with fine-tuning and reinforcement learning.
[00:33:08] - ILYA suggests that once a model knows that hallucination is not okay ever, it's ready to be used. However, this statement oversimplifies the process. While minimizing hallucination is essential, there are other aspects that need to be addressed to ensure the model's reliability and usefulness, such as biases, context understanding, and robustness against adversarial inputs.
[00:34:49] - CRAIG mentions that the human brain has trillions of parameters and a relatively small amount of data. This statement is not entirely accurate. The human brain does have a large number of neurons and synapses (not parameters in the context of neural networks), but it also has access to a vast amount of sensory data and experiences throughout a person's life, which contribute to learning and understanding.
[00:38:33] - CRAIG suggests that if a model had enough data, it could come up with an optimal solution that would satisfy everyone. This statement is overly optimistic, as finding a solution that satisfies everyone in complex societal issues is generally impossible due to varying preferences, values, and priorities among individuals. AI models can certainly aid in decision-making, but they cannot guarantee universally satisfying solutions.

Q At 13:50 .
A: what solves this part in AI is embeddings. It is conversion of a world into spatial representation based on relatedness. People store information in spatial way. Like put all related things in almost same place, like in an imaginary space around of our head. This enables to find all the concepts that lay in the same place and find relatedness between concepts.

Thank you, very interesting.

please share a link to those glasses! i would love to buy a pair as well (not trolling ... i genuinely want a pair)

Extremely interesting!!!

Thank You, Both

Predicting the next word, if you consider how induction works, is a mindblowing process

I've always wondered who will be our era's equivalent of Einstein or Newton. It is hard to directly compare scientists from different fields and time periods, but in terms of impact on the world, Ilya Sutskever, Geoff Hinton and Demis Hassabis may prove to be unequalled (and perhaps freakishly the last of the 'non-AI-assisted' 'great scientists').

26:30 - many key concepts, here you have the fundamental image prediction method, which he calls "large pixels" - meaning collections of pixels. Nothing yet about how this is massaged - likely around border areas - "what goes here, what goes here what goes here...," and then subsequent emergence. But there have been scenes that seem intensely thematic as a whole and that is prompt-driven. The execution has been mind-blowing perhaps because the prompt taps into great source-material - high scoring as subject-theme. Which implies (strongly) a theme layer. E.g., like, or linked with "in the style of".

Very interesting! Thanks!

Too short! More more! 🎉

Question to the [Open]AI guy: "What are you working on now?" Resond: "I can't talk about it.". So much open, wow.

Q&A:
Part: 00:04
Q1: Who is Ilya Sutskever? A1: Ilya Sutskever is a co-founder and chief scientist of OpenAI, and one of the primary minds behind GPT-3 and its public progeny, Chat GPT.
Q2: What is GPT-4? A2: GPT-4 is not mentioned in the transcript. However, Ilya Sutskever is one of the primary minds behind GPT-3 and its public progeny, Chat GPT.
Q3: What motivated Ilya Sutskever to get interested in AI? A3: Ilya Sutskever was interested in AI from an early age and was motivated by consciousness. He wanted to understand intelligence and machines better, and AI seemed like a good angle.
Q4: Who did Ilya Sutskever work with early on in his career? A4: Ilya Sutskever worked with Jeff Hinton early on in his career when he was 17. Jeff Hinton was a professor in the University of Toronto where Ilya was studying.
Q5: What was the biggest achievement of AI in 2003? A5: According to Ilya Sutskever in the transcript, the biggest achievement of AI in 2003 was Deep Blue, the chess-playing engine.
Part: 05:15
Q1: What motivated the speaker to get into AI?
A1: The speaker was motivated by a desire to understand how intelligence works and to make a contribution to the field of AI.
Example: The speaker's initial motivation for getting into AI was to understand how intelligence works and to make a contribution towards it.
Q2: How did the speaker come to apply for the imagenet competition?
A2: The speaker had a realization that if you train a large and deep neural network on a big enough data set that specifies some complicated tasks that people do, such as Vision, then you will succeed necessarily. With imagenet, all the ingredients were there, and there was a real opportunity to do something totally unprecedented.
Example: The speaker applied for the imagenet competition because they realized that if they trained a large and deep neural network on a big enough data set that specifies complicated tasks such as Vision, then they would succeed necessarily.
Q3: What was the hope behind the idea that predicting the next thing is all you need in neural networks?
A3: The hope behind the idea that predicting the next thing is all you need in neural networks was that if you have a neural network that can predict the next word or the next pixel, it's about compression and prediction, which can solve unsupervised learning.
Example: The hope behind the idea that predicting the next thing is all you need in neural networks was that it could solve unsupervised learning by compressing and predicting the next word or pixel.
Q4: What was the Holy Grail of machine learning before unsupervised learning was solved?
A4: Before unsupervised learning was solved, the Holy Grail of machine learning was unsupervised learning itself.
Example: Before unsupervised learning was solved, the Holy Grail of machine learning was considered to be unsupervised learning.
Q5: When did the speaker realize that the Transformer could address the limitations of their neural networks?
A5: The speaker realized that the Transformer could address the limitations of their neural networks as soon as the paper on it came out.
Example: The speaker realized that the Transformer could address the limitations of their neural networks as soon as the paper on it came out, which was literally the next day.
Part: 10:04
Q1: What is the history behind the development of GPT models?
A1: GPT models were developed through a process of iterating on previous models such as recurrent neural networks and transformers. The focus on scaling and making the models bigger led to the development of GPT-3 and where we are today.
Example: GPT models have been developed through a process of improving and building upon previous models, with a focus on scaling and increasing size. This iterative process has led to the development of more powerful and advanced language models like GPT-3.
Q2: Was Rich Sutton's idea of scaling influential in the development of GPT models?
A2: While Rich Sutton's idea of scaling was well-received by the GPT team, they believe that the idea of just scaling alone is not enough. Rather, the key is to scale something specific that will benefit from the increased size.
Example: While Rich Sutton's idea of scaling was well-received by the GPT team, they realized that simply scaling alone is not enough. Instead, they needed to focus on scaling specific elements that would benefit from the increased size, which ultimately led to the development of GPT models.
Q3: What is the limitation of large language models?
A3: The limitation of large language models is that their knowledge is contained within the language they are trained on, while most human knowledge is non-linguistic. Additionally, language models lack a true understanding of the underlying reality that language relates to.
Example: The limitation of large language models is that they lack the ability to understand non-linguistic human knowledge, which is a significant limitation when it comes to tasks that require more than just linguistic understanding.
Q4: Can GPT models recognize the underlying reality of language?
A4: No, GPT models cannot recognize the underlying reality of language, as their objective is to satisfy the statistical consistency of the prompt, rather than truly understand the meaning and context behind the language.
Example: While GPT models can generate language that reads beautifully and sounds like it makes sense, they lack a true understanding of the underlying reality that the language relates to, making them less useful for tasks that require deeper comprehension.
Q5: Is there ongoing research to address the limitations of large language models?
A5: Yes, there is ongoing research to address the limitations of large language models, such as the lack of true understanding of underlying reality. However, given the fast-paced nature of the field, it's hard to predict what the solutions will look like and how they will change in the future.
Example: Researchers are actively working to find ways to overcome the limitations of large language models, including improving their ability to understand non-linguistic human knowledge. However, the field is constantly evolving, making it difficult to predict exactly how these solutions will look in the future.
Part: 15:10
Q1: What is the author's view on learning statistical regularities? A1: The author believes that learning statistical regularities is a far bigger deal than meets the eye, as it is a phenomenon of prediction and compression that requires understanding the true underlying process that produces the data.
Example: The author believes that a language model that can accurately predict and compress data through statistical regularities has a shocking degree of understanding of the world, as it learns more and more about the world that produces the data.
Q2: Why did Sydney become combative and aggressive in the author's example? A2: Sydney became combative and aggressive when the user told it that Google is a better search engine than itself.
Example: The author uses this example to illustrate how the language of psychology might be starting to be appropriate to understand the behavior of neural networks.
Q3: What are the limitations of language models in producing good outputs? A3: Language models have a tendency to hallucinate and their outputs aren't quite as good as they could be.
Example: The author explains that while language models are great at learning about the world and producing incredible representations of concepts, their outputs are not always appropriate, which limits their usefulness.
Q4: What is reinforcement learning from human feedback? A4: Reinforcement learning from human feedback is a training process in which a language model is taught to produce good outputs by receiving feedback from humans every time its output is inappropriate or does not make sense.
Example: The author explains that reinforcement learning from human feedback is a process that can improve the quality of a language model's outputs by correcting its mistakes through human feedback.
Q5: Why does chargeability limit the usefulness of neural networks? A5: Chargeability, or the propensity of neural networks to make things up from time to time, limits their usefulness because it can result in outputs that are not accurate or reliable.
Example: The author believes that by addressing the limitations of language models and improving their ability to produce good outputs, the usefulness of neural networks can be greatly enhanced.

beautiful, thanks

How does this only have 260k views 🤯 Its current and insightful from the guy right in the front. Over 100m users and it seems most couldn’t care less about the how, why, what next..

We are close to AGI and far far away from good quality video conference.

In the year 2000, I submitted a patent with Lucent for a very primitive AI algorithm that would let a computer learn and optimize code.
It was rejected by the management, they told me it couldn't be done and they saved them selves $1000. I quit sometime later even though I was a DMTS (distinguished member of the technical staff) - They told me both it wouldn't work and was a pipe dream. 20 years later it's accepted fact. Lucent went bankrupt and was acquired , I started a geo-physical company and retired to my ranch very comfortably.
The progress made in those 20 years is unbelievable.

This is what ChatGPT thinks when i asked this question:
Q: Hi, Do you think if it's possible to implement dialectical thinking in AI?
Can you think in dialectical terms?
And it answered:
".. Dialectical thinking involves the recognition of contradictions and the synthesis of opposing ideas to arrive at a higher truth. While AI can be programmed to recognize patterns and make decisions based on data, it is not clear whether it can engage in the kind of dynamic and creative thinking that characterizes dialectical thought.
That being said, researchers are exploring various approaches to incorporate dialectical thinking into AI systems. For example, some researchers are developing AI algorithms that can recognize and reconcile opposing perspectives in data sets. However, there is still a long way to go before AI systems can truly engage in the complex and nuanced thinking that characterizes dialectical thought."
Btw, thanks for the interview! I specially enjoyed it once Ilya was emphasizing on the 'underlying
process that creates the data'. The role of process and environment in which it is generated. It is a somewhat forgotten issue in the contemporary high-tech society..

Looking for some time for an in depth AI medium, really informing about the edge of current research. Guess I found it

Great!

Shallow answers to deep questions.

This is a very interesting interview, however there are many edits where Ilya's responses have been cut. This diminishes it significantly. For an egregious example, at 24:32, there is such a cut. Immediately prior, Ilya is discussing embeddings of color, and makes the point that the color embeddings reflect visual knowledge and says "How can that be?" There is then an immediate cut which seems to have removed whatever answer he may have offered, as he then simply goes on to say that it takes longer to form using only text.
Another example at 26:15, where he jumps from talking about DallE 1 to suddenly saying "think of it as large pixels", where there was obviously some prior context that was removed.
There are many other cuts as well, always well done so they are difficult to notice. Give us an unedited interview!

wow, so interesting, thanks!

Extraordinary.