Public evidence record
Published podcast speaker
Claim ledger
11 transcript-backed records
01 / preference
“I like John McCarthy’s definition that intelligence is the computational part of the ability to achieve goals.”
02 / commitment
“The researchers will figure it out eventually. Number three, we won’t stop just with human-level intelligence.”
03 / belief
“I think there also should be a component having to do with your increasing understanding of your environment.”
04 / evaluation
“In the old days, it was interesting because things like search and learning were called weak methods because they’re just using general principles, they’re not using the power that comes from imbuing a system with human knowledge.”
05 / evaluation
“What is intelligence? The problem is to understand your world. Reinforcement learning is about understanding your world, whereas large language models are about mimicking people, doing what people say you should do.”
06 / evaluation
“You can’t have prior knowledge if you don’t have ground truth, because the prior knowledge is supposed to be a hint or an initial belief about what the truth is.”
07 / preference
“I think a better word would be “the network” because I think you mean the network.”
08 / prediction
“Then what I usually say is that this is all human-centric. But if we step aside from being a human and just take the point of view of the universe, this is I think a major stage in the universe, a major transition, a transition from replicators.”
09 / preference
“Maybe you’re using “context” because in large language models all that information has to go into the context window.”
10 / preference
“I like the way you consider that obvious, because I consider the opposite obvious.”
11 / evaluation
“Your examples are all, “Well, really you have to” because you can teach it, but there’s all the little idiosyncrasies of the particular life they’re leading and the particular people they’re working with and what they like, as opposed to what average people like.”