Sebastien Bubeck makes the case that intelligence has emerged with the trillion parameter generative AIs. He says there are internal representations and learning algorithms inside the systems. Sebastien is a Sr. Principal Research Manager in the Machine Learning Foundations group at Microsoft Research (MSR).

He says the generative systems can be used to explain itself.

GPT-4 still has weaknesses but it can use tools to mitigate those weaknesses. It can call Mathematica to precisely solve math problems.

GPT-4 does not have planning yet. There are some new systems that are trying to address this by working with GPT4.

GPT-4 is not set up to have much learning or memory. It can only remember within a session.

GPT-4 was slightly dumbed down from the internal testing system in order to address safety issues.

1:47 – Sebastien starts
5:36 – goal of the talk: there is some intelligence in the system
6:05 – “beware of trillion-dimensional space and its surprises”
8:20 – example demonstrating GPT4’s common sense
10:40 – theory of the mind
12:29 – theory of mind example
14:27 – consensus definition of intelligence by psychologists published in 1994 and if GPT4 matches this definition
18:00 – how to test GPT4’s intelligence
19:00 – Asking GP4 to write a proof of infinitude of primes
22:13 – The Strange Case of the Unicorn
27:15 – GPT4 vs Stable Diffusion
29:44 – Coding with a copilot that understands
32:57 – GPT4’s performance on coding interviews
33:41 – GPT4’s weaknesses, which can be overcome with toolsSebastien Makes the Start of AGI Case for GPT-4
36:09 – A mathematical conversation with GPT4
42:40 – GPT4 cannot do true planning
45:02 – Is GPT4 intelligent and does it matter?

A Stanford professor talks about how people and students should learn to work with the new AI.