Shane Legg (DeepMind Founder) - 2028 AGI, Superhuman Alignment, New Architectures | Summary and Q&A

TL;DR

Google DeepMind's Chief AGI Scientist, Shane Legg, shares insights on measuring progress towards AGI, the limitations of current benchmarks, the importance of sample efficiency and episodic memory, the need for search and reasoning in AI systems, and the challenges of aligning AI with human values.

Key Insights

• 🪡 AGI progress is challenging to measure due to its focus on generality and the need for comprehensive testing across a breadth of cognitive tasks.
• 🖤 Current benchmarks lack measurements for understanding streaming video and episodic memory, which are unique to human cognition.
• 🖐️ Sample efficiency and episodic memory play crucial roles in achieving human-like intelligence in AI systems.
• 👨‍🔬 Search capabilities, reasoning, and ethical decision-making are additional areas that need improvement for AGI alignment.
• 🦺 DeepMind emphasizes interpretability, reinforcement learning, and governance to address AGI safety concerns.

Transcript

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. First question. How do we measure progress   towards AGI concretely? We have these loss  numbers and we can see how the loss improves   from one model to ... Read More

Questions & Answers

Q: How is progress towards AGI measured?

AGI progress is measured by assessing the generality and cognitive abilities of AI models across a wide range of tasks, comparing their performance to human benchmarks.

Q: What aspects of human cognition are missing from current benchmarks?

Current benchmarks do not adequately measure understanding streaming video and episodic memory, which are unique capabilities of human intelligence.

Q: How does sample efficiency relate to episodic memory?

Sample efficiency enables rapid learning, similar to episodic memory, which allows humans to learn specific information quickly. Current models lack this kind of memory and need improvements.

Q: Will current deep learning models require trillions of tokens to achieve human-like capabilities, or can this be solved over time?

While current models require large amounts of training data, there is hope for addressing the limitations of sample efficiency and episodic memory through architectural improvements and research advancements.

Q: What criteria would indicate the arrival of human-level AI?

The arrival of human-level AI would require comprehensive tests across various cognitive tasks. Additionally, finding examples where humans outperform AI systems even with deliberate efforts would indicate significant progress.

Q: How does DeepMind approach AGI alignment and safety?

DeepMind pursues interpretability, reinforcement learning, red teaming, and evaluation of AI capabilities to address safety concerns. They also emphasize governance, ethics, and human oversight in the decision-making process.

Q: How can AI models develop better reasoning capabilities over time?

Developing AI systems with deep understanding of the world, robust reasoning abilities, and a strong understanding of ethics can promote ethical decision-making and alignment. Continuous evaluation and human verification are essential for ensuring ethical reasoning.

Q: What would be the next landmark in AI progress?

The next landmark is expected to involve AI systems becoming more fully multimodal, integrating understanding of images, video, and other modalities. This will open up new possibilities and applications beyond text-based systems.

Summary & Key Takeaways

• AGI (Artificial General Intelligence) measures progress based on generality, requiring a broad range of measurements and tests that span cognitive tasks humans can perform.

• Current benchmarks lack measurements for understanding streaming video and episodic memory, which are important aspects of human cognition.

• Sample efficiency, episodic memory, and search capabilities are crucial for achieving human-level intelligence in AI systems.

• Google DeepMind is pursuing research in interpretability, reinforcement learning, and governance to address safety concerns regarding AGI.