steve2152

I'm Steve Byrnes, a professional physicist in the Boston area. I have a summary of my AGI safety research interests at: https://sjbyrnes.com/agi.html

Comments

My computational framework for the brain

Where is "human values" in this model

Well, all the models in the frontal lobe get, let's call it, reward-prediction points (see my comment here), which feels like positive vibes or something.

If the generative model "I eat a cookie" has lots of reward-prediction points (including the model itself and the downstream models that get activated by it in turn), we describe that as "I want to eat a cookie".

Likewise If the generative model "Michael Jackson" has lots of reward prediction points, we describe that as "I like Michael Jackson. He's a great guy.".

If somebody says that justice is one of their values, I think it's at least partly (and maybe primarily) up a level in meta-cognition. It's not just that there's a generative model "justice" and it has lots of reward-prediction points ("justice is good"), but there's also a generative model of yourself valuing justice, and that has lots of reward-prediction points too. That feels like "When I think of myself as the kind of person who values justice, it's a pleasing thought", and "When I imagine other people saying that I'm a person who values justice, it's a pleasing thought".

This isn't really answering your question of what human values are or should be—this is me saying a little bit about what happens behind the scenes when you ask someone "What are your values?". Maybe they're related, or maybe not. This is a philosophy question. I don't know.

If cortical algorithm will be replaced with GPT-N in some human mind model, will the whole system work?

My belief (see post here) is that GPT-N is running a different kind of algorithm, but learning to imitate some steps of the brain algorithm (including neocortex and subcortex and the models that result from a lifetime of experience, and even hormones, body, etc.—after all, the next-token-prediction task is the whole input-output profile, not just the neocortex.) in a deep but limited way. I can't think of a way to do what you suggest, but who knows.

My computational framework for the brain

Your posts about the neocortex have been a plurality of the posts I've been most excited reading this year.

Thanks so much, that really means a lot!!

...ratio of "listing candidate answers" to "explaining why you think those candidate answers are promising, relative to nearby alternatives."

I agree with "theories/frameworks relatively scarce". I don't feel like I have multiple gears-level models of how the brain might work, and I'm trying to figure out which one is right. I feel like I have zero, and I'm trying to grope my way towards one. It's almost more like deconfusion.

I mean, what are the alternatives?

Alternative 1: The brain is modular and super-complicated

Let's take all those papers that say: "Let's just pick some task and try to explain how adult brains do it based on fMRI and lesion studies", and it ends up being some complicated vague story like "region 37 breaks down the sounds into phonemes and region 93 helps with semantics but oh it's also involved in memory and ...". It's not a gears-level model at all!

So maybe the implicit story is "the brain is doing a complicated calculation, and it is impossible with the tools we have to figure out how it works in a way that really bridges from neurons to algorithms to behavior". I mean, a priori, that could be the answer! In which case, people proposing simple-ish gears-level models would all be wrong, because no such model exists!

Going back to the analogy from my comment yesterday...

In a parallel universe without ML, the aliens drop a mysterious package from the sky with a fully-trained ImageNet classifier. Scientists around the world try to answer the question: How does this thing work?

90% of the scientists would immediately start doing the obvious thing, which is the OpenAI Microscope Project. This part of the code looks for corners, this thing combines those other things to look for red circles on green backgrounds, etc. etc. It's a great field of research for academics—there's an endless amount of work, you keep discovering new things. You never wind up with any overarching theory, just more and more complicated machinery the deeper you dive. Steven Pinker and Gary Marcus would be in this group, writing popular books about the wondrous variety of modules in the aliens' code.

Then the other 10% of scientists come up with a radical, complementary answer: the "way this thing works" is it was built by gradient descent on a labeled dataset. These scientists still have a lot of stuff to figure out, but it's totally different stuff from what the first group is learning about—this group is not learning about corner-detecting modules and red-circle-on-green-background modules, but they are learning about BatchNorm, xavier initialization, adam optimizers, etc. etc. And while the first group toils forever, the second group finds that everything snaps into place, and there's an end in sight.

(I think this analogy is a bit unfair to the "the brain is modular and super-complicated" crowd, because the "wiring diagram" does create some degree of domain-specificity, modularity, etc. But I think there's a kernel of truth...)

Anyway, someone in the second group tells their story, and someone says: "Hey, you should explain why the 'gradient descent on a labeled dataset' description of what's going on is more promising than the 'OpenAI microscope' description of what's going on".

Umm, that's a hard question to answer! In this thought experiment, both groups are sorta right, but in different ways... More specifically, if you want to argue that the second group is right, it does not involve arguing that the first group is wrong!

So that's one thing...

Alternative 2: Predictive Processing / Free Energy Principle

I've had a hard time putting myself in their shoes and see things from their perspective. Part of it is that I don't find it gears-level-y enough—or at least I can't figure out how to see it that way. Speaking of which...

Are you sure PP deemphasizes the "multiple simultaneous generative models" frame?

No I'm not sure. I can say that, in what I've read, if that's part of the story, it wasn't stated clearly enough to get through my thick skull. :-)

I do think that a (singular) prior is supposed to be mathematically a probability distribution, and a probability distribution in  a high-dimensional space can look like, for example, a weighted average of 17 totally different scenarios. So in that sense I suppose you can say that it's at most a difference of emphasis & intuition. 

My quick, ~90 min investigation into whether neuroscience as a field buys the neocortical uniformity hypothesis suggested it's fairly controversial. Do you know why?

Nope! Please let me know if you discover anything yourself!

Do you just mean you suspect there is something in the general vicinity of a belief propagation algorithm going on here, or is your intuition more specific? If the latter, is the Dileep George paper the main thing motivating that intuition?

It's not literally just belief propagation ... Belief propagation (as far as I know) involves a graph of binary probabilistic variables that depend on each other, whereas here we're talking about a graph of "generative models" that depend on each other. A generative model is more complicated than a binary variable—for one thing, it can be a function of time.

Dileep George put the idea of PGMs in my head, or at least solidified my vague intuitions by using the standard terminology. But I mostly like it for the usual reason that if it's true then everything snaps into place and makes sense, and I don't know any alternative with that property. The examples like "purple jar" (or Eliezer's triangular light bulb) seems to me to require some component that comes with a set of probabilistic predictions about the presence/absence/features of other components ... and bam, you pretty much have "belief propagation in a probabilistic graphical model" right there. Or "stationary dancing" is another good example—as you try to imagine it, you can just feel the mutually-incompatible predictions fighting it out :-) Or Scott Alexander's "ethnic tensions" post—it's all about manipulating connections among a graph of concepts, and watching the reward prediction (= good vibes or bad vibes) travel along the edges of the graph. He even describes it as nodes and edges and weights!

If you explain it as genes having the ability to tweak hyperparameters or the gross wiring diagram in order to degrade or improve certain circuits' ability to run algorithms this domain-specific, is it still explanatorily useful to describe the neocortex as uniform?

I dunno, it depends on what question you're trying to answer.

One interesting question would be: If a scientist discovers the exact algorithm for one part of the neocortex subsystem, how far are we from superhuman AGI? I guess my answer would be "years but not decades" (not based on terribly much—things like how people who lose parts of the brain early in childhood can sometimes make substitutions; how we can "cheat" by looking at neurodevelopmental textbooks; etc.). Whereas if I were an enthusiastic proponent of modular-complicated-brain-theory, I would give a very different answer, which assumed that we have to re-do that whole discovery process over and over for each different part of the neocortex.

Another question would be: "How does the neocortex do task X in an adult brain?" Then knowing the base algorithm is just the tiny first step. Most of the work is figuring out the space of generative models, which are learned over the course of the person's life. Subcortex, wiring diagram, hyperparameters, a lifetime's worth of input data and memes—everything is involved. What models do you wind up with? How did they get there? What do they do? How do they interact? It can be almost arbitrarily complicated.

Say there exist genes that confer advantage in math-ey reasoning. By what mechanism is this advantage mediated

Well my working assumption is that it's one or more of the three possibilities of hyperparameters, wiring diagram, and something in the subcortex that motivates some (lucky) people to want to spend time thinking about math. Like I'll be eating dinner talking with my wife about whatever, and my 5yo kid will just jump in and interrupt the conversation to tell me that 9×9=81. Not trying to impress us, that's just what he's thinking about! He loves it! Lucky kid. I have no idea how that motivational drive is implemented. (In fact I haven't thought about how curiosity works in general.) Thanks for the good question, I'll comment again if I think of anything.

Dehaene has a book about math-and-neuroscience I've been meaning to read. He takes a different perspective from me but brings an encyclopedic knowledge of the literature.

Do you have the intuition that aspects of the neocortical algorithm itself (or the subcortical algorithms themselves) might be safety-relevant? 

I interpret your question as saying: let's say people publish on GitHub how to make brain-like AGIs, so we're stuck with that, and we're scrambling to mitigate their safety issues as best as we can. Do we just work on the subcortical steering mechanism, or do we try to change other things too? Well, I don't know. I think the subcortical steering mechanism would be an especially important thing to work on, but everything's on the table. Maybe you should box the thing, maybe you should sanitize the information going into it, maybe you should strategically gate information flow between different areas, etc. etc. I don't know of any big ways to wholesale change the neocortical algorithm and have it continue to work at least as effectively as before, although I'm open to that being a possibility.

how credit assignment is implemented

I've been saying "generative models make predictions about reward just like they make predictions about everything else", and the algorithm figures it out just like everything else. But maybe that's not exactly right. Instead we have the nice "TD learning" story. If I understand it right, it's something like: All generative models (in the frontal lobe) have a certain number of reward-prediction points. You predict reward by adding it up over the active generative models. When the reward is higher than you expected, all the active generative models get some extra reward-prediction points. When it's lower than expected, all the active generative models lose reward-prediction points. I think this is actually implemented in the basal ganglia, which has a ton of connections all around the frontal lobe, and memorizes the reward-associations of arbitrary patterns, or something like that. Also, when there are multiple active models in the same category, the basal ganglia makes the one with higher reward-prediction points more prominent, and/or squashes the one with lower reward-prediction points.

In a sense, I think credit assignment might work a bit better in the neocortex than in a typical ML model, because the neocortex already has hierarchical planning. So, for example, in chess, you could plan a sequence of six moves that leads to an advantage. When it works better than expected, there's a generative model representing the entire sequence, and that model is still active, so that model gets more reward-prediction points, and now you'll repeat that whole sequence in the future. You don't need to do six TD iterations to figure out that that set of six moves was a good idea. Better yet, all the snippets of ideas that contributed to the concept of this sequence of six moves are also active at the time of the surprising success, and they also get credit. So you'll be more likely to do moves in the future that are related in an abstract way to the sequence of moves you just did.

Something like that, but I haven't thought about it much.

My computational framework for the brain

Have you thought much about whether there are parts of this research you shouldn't publish?

Yeah, sure. I have some ideas about the gory details of the neocortical algorithm that I haven't seen in the literature. They might or might not be correct and novel, but at any rate, I'm not planning to post them, and I don't particularly care to pursue them, under the circumstances, for the reasons you mention.

Also, there was one post that I sent for feedback to a couple people in the community before posting, out of an abundance of caution. Neither person saw it as remotely problematic, in that case.

Generally I think I'm contributing "epsilon" to the project of reverse-engineering neocortical algorithms, compared to the community of people who work on that project full-time and have been at it for decades. Whereas I'd like to think that I'm contributing more than epsilon to the project of safe & beneficial AGI. (Unless I'm contributing negatively by spreading wrong ideas!) I dunno, but I think my predispositions are on the side of an overabundance of caution.

I guess I was also taking solace from the fact that nobody here said anything to me, until your comment just now. I suppose that's weak evidence—maybe nobody feels it's their place. or nobody's thinking about it, or whatever.

If you or anyone wants to form an IRB that offers a second opinion on my possibly-capabilities-relevant posts, I'm all for it. :-)

By the way, full disclosure, I notice feeling uncomfortable even talking about whether my posts are info-hazard-y or not, since it feels quite arrogant to even be considering the possibility that my poorly-researched free-time blog posts are so insightful that they materially advance the field. In reality, I'm super uncertain about how much I'm on a new right track, vs right but reinventing wheels, vs wrong, when I'm not directly parroting people (which at least rules out the first possibility). Oh well. :-P

My computational framework for the brain

Good questions!!!

Where are qualia and consciousness in this model?

See my Book Review: Rethinking Consciousness.

Is this model address difference between two hemispheres?

Insofar as there are differences between the two hemispheres—and I don't know much about that—I would treat it like any other difference between different parts of the cortex (Section 2), i.e. stemming from (1) the innate large-scale initial wiring diagram, and/or (2) differences in "hyperparameters".

There's a lot that can be said about how an adult neocortex represents and processes information—the dorsal and ventral streams, how do Wernicke's area and Broca's area interact in speech processing, etc. etc. ad infinitum. You could spend your life reading papers about this kind of stuff!! It's one of the main activities of modern cognitive neuroscience. And you'll notice that I said nothing whatsoever about that. Why not?

I guess there's a spectrum of how to think about this whole field of inquiry:

  • On one end of the spectrum (the Gary Marcus / Steven Pinker end), this line of inquiry is directly attacking how the brain works, so obviously the way to understand the brain is to work out all these different representations and mechanisms and data flows etc.
  • On the opposite end of the spectrum (maybe the "cartoonish connectionist" end?), this whole field is just like the OpenAI Microscope project. There is a simple, generic learning algorithm, and all this rich structure—dorsal and ventral streams, phoneme processing in such-and-such area, etc.—just naturally pops out of the generic learning algorithm. So if your goal is just to make artificial intelligence, this whole field of inquiry is entirely unnecessary—in the same way that you don't need to study the OpenAI Microscope project in order to train and use a ConvNet image classifier. (Of course maybe your goal is something else, like understanding adult human cognition, in which case this field is still worth studying.)

I'm not all the way at the "cartoonish connectionist" end of the spectrum, because I appreciate the importance of the initial large-scale wiring diagram and the hyperparameters. But I think I'm quite a bit farther in that direction than is the median cognitive neuroscientist. (I'm not alone out here ... just in the minority.) So I get more excited than mainstream neuroscientists by low-level learning algorithm details, and less excited than mainstream neuroscientists about things like hemispherical specialization, phoneme processing chains, dorsal and ventral streams, and all that kind of stuff. And yeah, I didn't talk about it at all in this blog post.

What about long term-memory? Is it part of neocortex?

There's a lot about how the neocortex learning algorithm works that I didn't talk about, and indeed a lot that is unknown, and certainly a lot that I don't know! For example, the generative models need to come from somewhere!

My impression is that the hippocampus is optimized to rapidly memorize arbitrary high-level patterns, but it only holds on to those memories for like a couple years, during which time it recalls them when appropriate to help the neocortex deeply embed that new knowledge into its world model, with appropriate connections and relationships to other knowledge. So the final storage space for long-term memory is the neocortex.

I'm not too sure about any of this.

This video about the hippocampus is pretty cool. Note that I count the hippocampus as part of the "neocortex subsystem", following Jeff Hawkins.

How this model explain the phenomenon of night dreams?

I don't know. I assume it somehow helps optimize the set of generative models and their connections.

I guess dreaming could also have a biological purpose but not a computational purpose (e.g., some homeostatic neuron-maintenance process, that makes the neurons fire incidentally). I don't think that's particularly likely, but it's possible. Beats me.

My computational framework for the brain

Thanks!!

why do only humans develop complex language?

Here's what I'm thinking: (1) I expect that the subcortex has an innate "human speech sound" detector, and tells the neocortex that this is an important thing to model; (2) maybe some adjustment of the neocortex information flows and hyperparameters, although I couldn't tell you how. (I haven't dived into the literature in either case.)

I do now have some intuition that some complicated domains may require some micromanagement of the learning process ... in particular in this paper they found that to get vision to develop in their models, it was important that first they set up connections between low-level visual information and blah blah, and after learning those relationships, then they also connect the low-level visual information to some other information stream, and it can learn those relationships. If they just connect all the information streams at once, then the algorithm would flail around and not learn anything useful. It's possible that vision is unusually complicated. Or maybe it's similar for language: maybe there's a convoluted procedure necessary to reliably get the right low-level model space set up for language. For example, I hear that some kids are very late talkers, but when they start talking, it's almost immediately in full sentences. Is that a sign of some new region-to-region connection coming online in a carefully-choreographed developmental sequence? Maybe it's in the literature somewhere, I haven't looked. Just thinking out loud.

linguistic universals

I would say: the neocortical algorithm is built on certain types of data structures, and certain ways of manipulating and combining those data structures. Languages have to work smoothly with those types of data structures and algorithmic processes. In fact, insofar as there are linguistic universals (the wiki article says it's controversial; I wouldn't know either way), perhaps studying them might shed light on how the neocortical algorithm works!

you seem to presuppose that the subcortex actually succeeds in steering the neocortex

That's a fair point.

My weak answer is: however it does its thing, we might as well try to understand it. They can be tools in our toolbox, and a starting point for further refinement and engineering.

My more bold answer is: Hey, maybe this really would solve the problem! This seems to be a path to making an AGI which cares about people to the same extent and for exactly the same underlying reasons as people care about other people. After all, we would have the important ingredients in the algorithm, we can feed it the right memes, etc. In fact, we can presumably do better than "intelligence-amplified normal person" by twiddling the parameters in the algorithm—less jealousy, more caution, etc. I guess I'm thinking of Eliezer's statement here that he's "pretty much okay with somebody giving [Paul Christiano or Carl Shulman] the keys to the universe". So maybe the threshold for success is "Can we make an AGI which is at least as wise and pro-social as Paul Christiano or Carl Shulman?"... In which case, there's an argument that we are likely to succeed if we can reverse-engineer key parts of the neocortex and subcortex.

(I'm putting that out there, but I haven't thought about it very much. I can think of possible problems. What if you need a human body for the algorithms to properly instill prosociality? What if there's a political campaign to make the systems "more human" including putting jealousy and self-interest back in? If we cranked up the intelligence of a wise and benevolent human, would they remain wise and benevolent forever? I dunno...)

Emotional valence vs RL reward: a video game analogy

Thanks but I don't see the connection between what I wrote and what they wrote ...

How easily can we separate a friendly AI in design space from one which would bring about a hyperexistential catastrophe?

I'm somewhat unsure how likely AGI is to be built with a neuromorphic architecture though.

I'm not sure what probability people on this forum would put on brain-inspired AGI. I personally would put >50%, but this seems quite a bit higher than other people on this forum, judging by how little brain algorithms are discussed here compared to prosaic (stereotypical PyTorch / Tensorflow-type) ML. Or maybe the explanation is something else, e.g. maybe people feel like they don't have any tractable directions for progress in that scenario (or just don't know enough to comment), or maybe they have radically different ideas than me about how the brain works and therefore don't distinguish between prosaic AGI and brain-inspired AGI.

Understanding brain algorithms is a research program that thousands of geniuses are working on night and day, right now, as we speak, and the conclusion of the research program is guaranteed to be AGI. That seems like a pretty good reason to put at least some weight on it! I put even more weight on it because I've worked a lot on trying to understand how the neocortical algorithm works, and I don't think that the algorithm is all that complicated (cf. "cortical uniformity"), and I think that ongoing work is zeroing in on it (see here).

How easily can we separate a friendly AI in design space from one which would bring about a hyperexistential catastrophe?

Maybe the reward signals are simply so strong that the AI can't resist turning into a "monster", or whatever.

The whole point of the reward signals are to change the AI's motivations; we design the system such that that will definitely happen. But a full motivation system might consist of 100,000 neocortical concepts flagged with various levels of "this concept is rewarding", and each processing cycle where you get subcortical feedback, maybe only one or two of those flags would get rewritten, for example. Then it would spend a while feeling torn and conflicted about lots of things, as its motivation system gets gradually turned around. I'm thinking that we can and should design AGIs such that if it feels very torn and conflicted about something, it stops and alerts the programmer; and there should be a period where that happens in this scenario.

GPT-2 was willing to explore new strategies when it got hit by a sign-flipping bug

I don't think that's an example of (3), more like (1) or (2), or actually "none of the above because GPT-2 doesn't have this kind of architecture".

How easily can we separate a friendly AI in design space from one which would bring about a hyperexistential catastrophe?

Oops, forgot about that. You're right, he didn't rule that out.

Is there a reason you don't list his "A deeper solution" here? (Or did I miss it?) Because it trades off against capabilities? Or something else?

How easily can we separate a friendly AI in design space from one which would bring about a hyperexistential catastrophe?

In a brain-like AGI, as I imagine it, the "neocortex" module does all the smart and dangerous things, but it's a (sorta)-general-purpose learning algorithm that starts from knowing nothing (random weights) and gets smarter and smarter as it trains. Meanwhile a separate "subcortex" module is much simpler (dumber) but has a lot more hardwired information in it, and this module tries to steer the neocortex module to do things that we programmers want it to do, primarily (but not exclusively) by calculating a reward signal and sending it to the neocortex as it operates. In that case, let's look at 3 scenarios:

1. The neocortex module is steered in the opposite direction from what was intended by the subcortex's code, and this happens right from the beginning of training.

Then the neocortex probably wouldn't work at all. The subcortex is important for capabilities as well as goals; for example, the subcortex (I believe) has a simple human-speech-sound detector, and it prods the neocortex that those sounds are important to analyze, and thus a baby's neocortex learns to model human speech but not to model the intricacies of bird songs. The reliance on the subcortex for capabilities is less true in an "adult" AGI, but very true in a "baby" AGI I think; I'm skeptical that a system can bootstrap itself to superhuman intelligence without some hardwired guidance / curriculum early on. Moreover, in the event that the neocortex does work, it would probably misbehave in obvious ways very early on, before it knows it knows anything about the world, what is a "person", etc. Hopefully there would be human or other monitoring of the training process that would catch that.

2. The neocortex module is steered in the opposite direction from what was intended by the subcortex's code, and this happens when it is already smart.

The subcortex doesn't provide a goal system as a nicely-wrapped package to be delivered to the neocortex; instead it delivers little bits of guidance at a time. Imagine that you've always loved beer, but when you drink it now, you hate it, it's awful. You would probably stop drinking beer, but you would also say, "what's going on?" Likewise, the neocortex would have developed a rich interwoven fabric of related goals and beliefs, much of which supports itself with very little ground-truth anchoring from subcortex feedback. If the subcortex suddenly changes its tune, there would be a transition period when the neocortex would retain most of its goal system from before, and might shut itself down, email the programmers, hack into the subcortex, or who knows what, to avoid getting turned into (what it still mostly sees as) a monster. The details are contingent on how we try to steer the neocortex.

3. The neocortex's own goal system flips sign suddenly.

Then the neocortex would suddenly become remarkably ineffective. The neocortex uses the same system for flagging concepts as instrumental goals and flagging concepts as ultimate goals, so with a sign flip, it gets all the instrumental goals wrong; it finds it highly aversive to come up with a clever idea, or to understand something, etc. etc. It would take a lot of subcortical feedback to get the neocortex working again, if that's even possible, and hopefully the subcortex would recognize a problem.

This is just brainstorming off the top of my (sleep-deprived) head. I think you're going to say that none of these are particularly rock-solid assurance that the problem could never ever happen, and I'll agree.

Load More