
23 segments available
I had a lot of fun chatting with Shane Legg - Founder & Chief AGI Scientist, Google DeepMind! We discuss: * Why he expects AGI around 2028 * How to align superhuman models * What new architectures needed for AGI * Has Deepmind sped up capabilities or safety more? * Why multimodality will be next big landmark * & much more 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkeshpatel.com/p/shane-legg * Apple Podcasts: https://podcasts.apple.com/us/podcast/shane-legg-deepmind-founder-2028-agi-new-architectures/id1516093381?i=1000632720307 * Spotify: https://open.spotify.com/episode/0Ru2CtaJqsQ5mpA5dqHWAK?si=4AsglwIZQpqht7p9Wpc_CA * Twitter: https://twitter.com/dwarkesh_sp/status/1717566262472237134 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 - Measuring AGI 00:11:41 - Do we need new architectures? 00:16:26 - Is search needed for creativity? 00:19:19 - Superhuman alignment 00:29:58 - Impact of Deepmind on safety vs capabilities 00:34:03 - Timelines 00:41:24 - Multimodality
Shane Legg discusses the complexities of measuring progress towards Artificial General Intelligence (AGI). He emphasizes that AGI is about general cognitive abilities, not just specific tasks, and outlines the need for diverse measurements to assess human-level performance. Legg highlights the difficulty in creating a comprehensive set of tests that can evaluate the breadth of human cognitive tasks.
"Today I have the pleasure of interviewing Shane Legg, who is the founder and the Chief AGI scientist of Google DeepMind. Shane, welcome to the podcast. Thank you. It's a pleasure being here. Firs..."
In this segment, Shane Legg critiques existing benchmarks for AI, such as MMLU, noting their inadequacies in measuring aspects of human cognition like episodic memory and understanding streaming video. He explains the limitations of language models in capturing the full spectrum of human cognitive abilities and the need for new benchmarks that reflect these gaps.
"then for all practical purposes, you have an AGI. Let's get more concrete. We measure the performance of these large language models on MMLU and other benchmarks. What is missing from the benchma..."
Shane Legg elaborates on the concept of sample efficiency in AI, relating it to human learning capabilities. He discusses how current models learn from vast amounts of data but lack the rapid learning abilities seen in humans. Legg believes that while there are challenges, these issues are not fundamental limitations and can be addressed through architectural advancements.
"The thing you're referring to with episodic memory, would it be fair to call that sample efficiency or is that a different thing? It's very much related to sample efficiency. It's one of the thin..."
Legg outlines his vision for determining when AGI has been achieved, emphasizing the need for a comprehensive suite of tests that cover a wide range of cognitive tasks. He suggests an adversarial approach to testing, where attempts are made to find weaknesses in the AI's capabilities, ensuring that it can perform at or above human levels across various domains.
"whether it's about delusions, factuality, the type of memory and learning that they have, or understanding video, or all sorts of things like that. I don't see any big blockers. I don't see big ..."
Shane Legg reflects on his earlier research regarding intelligence measurement and the importance of a reference machine. He discusses how the understanding of intelligence has evolved, advocating for a focus on human intelligence as a meaningful benchmark for developing AGI. Legg emphasizes the economic and philosophical significance of achieving human-level intelligence.
"A lot of your earlier research, at least the ones I could find, emphasized that AI should be able to manipulate and succeed in a variety of open-ended environments. It almost sounds like a video..."
In this segment, Legg addresses the need for architectural changes in AI to incorporate episodic memory and improve learning efficiency. He compares current AI architectures to human cognitive processes, highlighting the necessity for systems that can learn rapidly and integrate information over time. Legg believes that these challenges can be overcome with targeted architectural innovations.
"obviously, because it exists in the world. We know that human intelligence is very, very powerful because it's affected the world profoundly in countless ways. And we know if human level intelli..."
Shane Legg discusses DeepMind's broad research initiatives beyond AGI, including projects like AlphaFold and their implications for AI development. He clarifies that while these projects may not directly lead to AGI, they contribute to the overall understanding and capabilities of AI systems. Legg emphasizes the importance of exploring various domains to uncover significant advancements.
"you need to do this. They basically have a context window, which is very, very fluid, of course, and they have the weights, which things get baked into very slowly. So to my mind, that feels lik..."
Legg discusses the necessity of search mechanisms in AI for true creativity. He contrasts the capabilities of current language models, which primarily mimic existing data, with the potential for AI to discover innovative solutions through exploration. This segment emphasizes the importance of searching through possibilities to achieve creative breakthroughs.
"At the time, did it stick out to you as an especially fruitful thing to train for? Well, yeah. In the sense what's happened is actually very aligned with what I wrote about in my thesis. The idea..."
In this segment, Legg explores the challenges of aligning human-level and superhuman AIs. He discusses the importance of developing AI systems that can reason through ethical dilemmas, rather than simply reacting based on initial impulses. This highlights the need for a robust understanding of ethics in AI development.
"is that something that needs to be added to LLMs where they can actually interact with their data or the world or in some way? Yeah, I think that's on the right track. These foundation models are..."
Shane Legg outlines the requirements for creating ethical AI systems capable of making sound decisions. He emphasizes the need for a deep understanding of ethics, world models, and reasoning capabilities. This segment addresses the fundamental alignment problem and the importance of instilling ethical values in AI from the outset.
"I think it's fair to say we're roughly doing the sorts of scaling and training that you see many people in the field doing but we have our own take on it and our own different tricks and technique..."
Legg discusses the complexities of ensuring that AI systems understand and preserve human values. He highlights the necessity of teaching AI about ethics and the challenges involved in communicating nuanced values effectively. This segment underscores the importance of aligning AI's decision-making processes with human ethical standards.
"fundamentally a highly ethical value aligned system from the get go. How do you do that? Maybe this is slightly naive, but this is my take on it — How do people do it? If you have a really diffic..."
In this concluding segment, Shane Legg emphasizes the need for AI systems to be trained on human ethics comprehensively. He discusses the importance of ensuring that AI understands ethical principles as well as a skilled ethicist. This segment wraps up the conversation on the future of AGI and the ethical considerations that must be addressed.
"thing. When you sample from a foundation model at the moment, it's blurting out the first thing. It's like System 1, if you like, from psychology, from Kahneman et al. That's not good enough. And..."
Shane Legg addresses the need for continuous verification of AI systems' ethical decision-making processes. He suggests that AI should be rigorously tested to ensure it understands ethics robustly and consistently. This segment highlights the importance of human oversight in AI reasoning and the challenges of ensuring that AI adheres to the ethical principles defined by society.
"in terms of the behavior of these AGI systems. And then what you do is you engineer the system to actually follow these things so that every time it makes a decision, it does an analysis using a..."
In this segment, Shane Legg reflects on DeepMind's impact on AGI safety versus capabilities. He discusses the challenges of hiring for AGI safety roles and the importance of openly addressing safety concerns within the AGI community. Legg emphasizes the credibility DeepMind has brought to AGI safety discussions and the ongoing efforts to balance safety with advancing capabilities.
"Do you have some sort of framework for that at Google DeepMind? This is not so much a Google DeepMind perspective on this. This is my take on how I think we need to do this kind of thing. There a..."
Shane Legg explores the complexities of assessing DeepMind's influence on AI progress. He discusses the challenges of determining what advancements would have occurred without DeepMind's contributions and the broader context of the AI community's growth. This segment delves into the interplay between safety and capabilities in AI development.
"I've been worried about AGI safety for a long time, well before DeepMind. But it was always really hard to hire people to work on AGI safety, particularly in the early days. Back in 2013 or so, ..."
Shane Legg shares his historical perspective on predicting the arrival of AGI, referencing his early estimates from 2008. He discusses the exponential growth of computational power and data, and how these factors contribute to the potential realization of AGI by 2028. Legg reflects on the reasoning behind his predictions and the significance of scalable algorithms in unlocking AGI.
"The impact that DeepMind has had: I guess we were the first AGI company and as the first AGI company, we always had an AGI safety group. We've been publishing papers on this for many years. I th..."
Shane Legg discusses the exponential growth of computational power and data, referencing Ray Kurzweil's insights. He explains how this growth creates a demand for scalable algorithms, leading to a positive feedback loop that drives advancements in AI. Legg believes that this trend will enable the training of models on data beyond human experience, paving the way for AGI.
"I first formed those beliefs around 2001 after reading Ray Kurzweil's The Age of Spiritual Machines. There were two really important points in his book that I came to believe as true. One is tha..."
In this segment, Shane Legg shares his belief that there is a 50% chance of achieving AGI by 2028. He acknowledges the unpredictability of research timelines but expresses optimism about solving current challenges. Legg emphasizes that while he expects significant advancements, he remains cautious about the timeline.
"And then the second thing was just looking at the trends. If the scalable algorithms were to be discovered, then during the 2020s, it should be possible to start training models on significantly ..."
Legg outlines his expectations for AI models leading up to 2028, predicting they will become more factual, less delusional, and multimodal. He anticipates a surge in impressive applications, while also acknowledging potential misuse. This segment highlights the transformative potential of AI in the coming years.
"then. You often hit unexpected problems in research and science and sometimes things take longer than you expect. If we're in 2029 and it hasn't happened yet, if there was a problem that caused i..."
Shane Legg discusses various research directions at DeepMind, particularly focusing on the concept of System 2 thinking and deliberative dialogue. He explains how these approaches aim to enhance alignment in increasingly powerful AI systems, showcasing the promise of collaborative decision-making in AI.
"anything is just loads of great applications for the coming years. There can be some misuse cases as well. I'm sure somebody will come up with something to do with these models that is quite unh..."
In this segment, Legg explores the relationship between episodic memory and System 2 thinking in AI. He explains how these concepts can interact and influence each other, emphasizing their importance in improving AI's factual accuracy and creative capabilities.
"to scale the alignment to increasingly powerful systems. I think things of that kind of flavor have quite a lot of promise in my opinion, but that's kind of quite a broad category. There are man..."
Shane Legg predicts that the next significant milestone in AI will be achieving full multimodality. He envisions a future where AI systems can understand and process various forms of data, such as text, images, and video, leading to a more comprehensive understanding of the world.
"limits their applications in many ways. The final question is this. You've been in this field for over a decade, much longer than many others, and you've seen different landmarks like ImageNet an..."
Legg discusses the early stages of multimodal AI and its potential to revolutionize applications. He highlights the importance of integrating diverse data types and anticipates that new applications will emerge as AI systems gain a more grounded understanding of the world.
"a very narrow thing whereas now they understand when you talk to them and they understand images and pictures and video and you can show them things or things like that. And they will have much ..."