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

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.

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

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

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

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 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.

Thank you for sharing your insights and explanations Ilya.

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

Beautiful. Thank you for this interview.

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.

Thank you for uploading, great content, insightful.

Interesting and important discussion Craig and Ilya. Thank you Mike

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 👌

Awesome interview! Questions were great. Please more.

- 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?

GPT-10 + Quantum processor + Boston Dynamics = Terminator

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

That dude is fascinating. Thanks for the interview.

Way to go Ilya! Rocked it.

This podcast is wonderful. Thank you very much Craig.

Excellent. Thank you very much for this.

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.

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.

The quality 👌

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

Great interview!! Thanks for sharing!

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 ♥

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.

Mein Freund Danke, dass du das geteilt hast

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."

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.

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

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

Beautiful conversation

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

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

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.

Awesome interview thank you for sharing😊

great interview!

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

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

Wonderful discussion and insights❤😊

great interview. thx for doing ths!

Fantastic content, thank you

What an brilliant mind. Great interview

Amazing interview

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.

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)

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

thank you both

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

This guy is amazing

Thank you ILYA.

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.

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)

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.

What a brilliant mind.

Finally, something interesting not overhyped!

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.

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...

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!

Helpful in pretty much any situation. Great.

great interview questions.

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

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

great to see a top AI expert in UKposts.

fascinating!

Together!!!

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.

Thank You, Both

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.

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

Too short! More more! 🎉

Extremely interesting!!!

Thank you, very interesting.

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!

Excellent material thanks!

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.

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".

Extraordinary.

beautiful, thanks

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

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..

wow, so interesting, thanks!

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.

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!

Speed of Ilya’s talking resembles the speed with which ChatGPT generating answers.

Great!

Very interesting! Thanks!

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').

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..

Shallow answers to deep questions.

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

hallucination is great, it can be used to drive creativity in the model. All the model needs is to be cognizant that of "how" to hallucinate and to know when might be appropriate to employ hallucinations to circumvent logical impasses or create a richer set of outputs...

It really is like a child that we have to raise properly 🌎☀️💙

What a mind! Wow!

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