So, you might be surprised to hear that most state-of-the-art foundation models for robotics have no memory or no context. They're just operating on the current sensor observations, the current camera readings, uh, and predicting actions based off of that.
And so if I say, "Hey, make this change." and the agent makes the change and then it runs the test and then they're broken and then it fixes the test. I have very high confidence the next change I asked it to make, it's going to follow that path again
So we just could not see a way this could happen by chance and once we saw that we really felt quite convinced that this was a real signal and that really somehow there has been natural selection to increase the genetic changes that today manifest themselves as more years of school at predicting more years of schooling.
Extrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.
But but that's actually a very weak criterion, right? People thought I was saying like we won't need 90% of the software engineers. Those things are worlds apart, right?
if you think about it, it sort of makes sense because the LLM is an averaging machine, right? It's predicting the most likely that's an averaging kind of a thing. And so when what you're looking for is a kind of average, it's usually pretty good. So, summarization, topics, themes. But when you're asking for like what is interesting and not average, it's actually pretty bad at it.
Collapse is such a strong word, and the West has been using this word repeatedly. If I remember correctly, maybe four to five times, maybe even six times since 1980s during the period of China’s fastest growth. I tend to not think that Chinese economy will collapse.
As much as possible, try to not predict what the future may hold, but just wait as long as possible for that future to become the present and show you what it actually needs.
it will take time for deep learning systems and training data to mature enough to be useful for the truly value-adding task of predicting safety and efficacy in humans
Friedman’s focus on the money supply has not held up, as Samuelson suggested, but the alternative Keynesian macro models recommended by Samuelson in the same interview have not done better and they were not outperforming simple random walk models of predicting the macroeconomic future.
This is because the language modeling objective used for many recent large LMs-predicting the next token on a webpage from the internet-is different from the objective "follow the user's instructions helpfully and safely" (Radford et al.,, 2019; Brown et al.,, 2020; Fedus et al.,, 2021; Rae et al.,, 2021; Thoppilan et al.,, 2022). Thus, we say that the language modeling objective is misaligned.
I have reviewed before the effectiveness of peer review at figuring out "what's good" in the context of grant awards, finding that peer review as currently practiced does substantially better than chance at predicting future impact, especially for the most impactful of the papers; but at the same time is is far from perfect, leaving plenty of variance unexplained.
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