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

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!

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

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

Wonderful interview! Thank you Craig and Ilya!

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.

- 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?

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. :hand-orange-covering-eyes::trophy-yellow-smiling::cat-orange-whistling:

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. :hand-orange-covering-eyes::hand-orange-covering-eyes:

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 👌

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

Interesting and important discussion Craig and Ilya. Thank you Mike

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.

Beautiful. Thank you for this interview.

Way to go Ilya! Rocked it.

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

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

Thank you for uploading, great content, insightful.