Discord: https://discord.gg/ESrGqhf5CB

Prof. Melanie Mitchell argues that the concept of "understanding" in AI is ill-defined and multidimensional - we can't simply say an AI system does or doesn't understand. She advocates for rigorously testing AI systems' capabilities using proper experimental methods from cognitive science. Popular benchmarks for intelligence often rely on the assumption that if a human can perform a task, an AI that performs the task must have human-like general intelligence. But benchmarks should evolve as capabilities improve.

Large language models show surprising skill on many human tasks but lack common sense and fail at simple things young children can do. Their knowledge comes from statistical relationships in text, not grounded concepts about the world. We don't know if their internal representations actually align with human-like concepts. More granular testing focused on generalization is needed.

There are open questions around whether large models' abilities constitute a fundamentally different non-human form of intelligence based on vast statistical correlations across text. Mitchell argues intelligence is situated, domain-specific and grounded in physical experience and evolution. The brain computes but in a specialized way honed by evolution for controlling the body. Extracting "pure" intelligence may not work.

Other key points:

- Need more focus on proper experimental method in AI research. Developmental psychology offers examples for rigorous testing of cognition.

- Reporting instance-level failures rather than just aggregate accuracy can provide insights.

- Scaling laws and complex systems science are an interesting area of complexity theory, with applications to understanding cities.

- Concepts like "understanding" and "intelligence" in AI force refinement of fuzzy definitions.

- Human intelligence may be more collective and social than we realize. AI forces us to rethink concepts we apply anthropomorphically.

The overall emphasis is on rigorously building the science of machine cognition through proper experimentation and benchmarking as we assess emerging capabilities.

TOC:

[00:00:00] Introduction and Munk AI Risk Debate Highlights

[05:00:00] Douglas Hofstadter on AI Risk

[00:06:56] The Complexity of Defining Intelligence

[00:11:20] Examining Understanding in AI Models

[00:16:48] Melanie's Insights on AI Understanding Debate

[00:22:23] Unveiling the Concept Arc

[00:27:57] AI Goals: A Human vs Machine Perspective

[00:31:10] Addressing the Extrapolation Challenge in AI

[00:36:05] Brain Computation: The Human-AI Parallel

[00:38:20] The Arc Challenge: Implications and Insights

[00:43:20] The Need for Detailed AI Performance Reporting

[00:44:31] Exploring Scaling in Complexity Theory

Eratta:

Note Tim said around 39 mins that a recent Stanford/DM paper modelling ARC “on GPT-4 got around 60%”. This is not correct and he misremembered. It was actually davinci3, and around 10%, which is still extremely good for a blank slate approach with an LLM and no ARC specific knowledge. Folks on our forum couldn’t reproduce the result. See paper linked below.

Books (MUST READ):

Artificial Intelligence: A Guide for Thinking Humans (Melanie Mitchell)

https://www.amazon.co.uk/Artificial-Intelligence-Guide-Thinking-Humans/dp/B07YBHNM1C/?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=44ccac78973f47e59d745e94967c0f30&camp=1634&creative=6738

Complexity: A Guided Tour (Melanie Mitchell)

https://www.amazon.co.uk/Audible-Complexity-A-Guided-Tour?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=3f8bd505d86865c50c02dd7f10b27c05&camp=1634&creative=6738