One of the most critical missing elements in current AI is continual learning — systems need to keep learning after deployment when they encounter novel situations, rather than only learning during training and then being frozen.
Beck identifies continual learning — the ability to recognize novel situations post-deployment and update internal models on the fly — as a critical missing piece for real intelligence. ✦ AI generated
Jeff Beck · Machine Learning Street Talk · 2026-01-25 · original ↗
starts at this moment · 28:51
“Do you think that the concept of recursive self-improving intelligence is a valid one?”
I think that one of the most critical missing elements right now is some form of continual learning, right? At the end of the day, you really want an algorithm that doesn't just learn on the training set and then just gets deployed. You want something that runs around in the world and comes across things that it doesn't understand, and then is able to build, append its model in some sense.
verbatim transcript · starts at 28:51
28:31complete suite of human behaviors and then someone will come up with some criticism like, "Yeah, but it's not really doing X. It's just faking it, right? This is just the direction things go because people really do think they're important." >> Yeah. Do do you think that the concept of recursive self-improving intelligence is a valid one? Yes, I do think that is so so I think that one of the most
28:51critical missing elements right now is some form of continual learning, right? You at the end of the day, you really want an algorithm that that doesn't just learn on the training that on the training set and then just gets deployed. You want something that that that runs around in the world and comes across things that it doesn't understand, right? And then is able to incorp to build, you know, append its
29:13model in some sense, right? So this is like the this you know and there are some approaches to it's all based on like basian nonparametrics and dish process priors and stuff like that where you you sort of see something that's surprising or unique or different something you didn't expect and it causes you to say I need to turn learning on because I got to figure this out. That is an absolutely critical
29:35element that we need to be developing. We are developing that. And it turns out that that's one of the nice things about this sort of object- centered physics discovery thing is because it's object- centered. If it comes across a new situation that it does not understand, it is capable of instantiating a completely brand new object just to explain this new situation. >> Continually learning agents can acquire
29:54new knowledge autonomously and and the whole you know the whole thing just learns more knowledge. But intelligence feels different. It it it feels like in the system that we've been describing the intelligence is the way we're implementing the you know the basian updates and and you know actually building the algorithms. Could could the systems on their own meta program themselves and develop better algorithms or something like that? That's a very
30:20good question. something that would be closer to true artificial intelligence than what we currently have would be capable of building models on the fly to deal with new situations to taking things that it knows about right and combining them in new and different ways. Um uh there are approaches that have some of that aspect to it. Like GFlow nets from like Benio stuff is like is like a great example of
30:44something that at least in principle is a generative model of generative models, right? It's sort of like oh like you know I might actually need a new node like it's time to create a new latent variable cuz like like the current set's just not cutting the mustard anymore. Those are things that that that I think are hallmarks of of true intelligence. I don't want to ever make the statement as