My computational framework for the brain

post by steve2152 · 2020-09-14T14:19:21.974Z · score: 81 (22 votes) · LW · GW · 11 comments

Contents

  1. Two subsystems: "Neocortex" and "Subcortex"
  2. Cortical uniformity
  3. Blank-slate neocortex
  4. What is the neocortical algorithm?
    4.1. "Analysis by synthesis" + "Planning by probabilistic inference"
    4.2. Compositional generative models
  5. The subcortex steers the neocortex towards biologically-adaptive behaviors.
  6. The neocortex is a black box from the perspective of the subcortex. So steering the neocortex is tricky!
    6.1 The subcortex can learn what's going on in the world via its own, parallel, sensory-processing system.
    6.2 The subcortex can see the neocortex's outputs—which include not only prediction but imagination, memory, and empathetic simulations of other people.
  7. The subcortical algorithms remain largely unknown
  Conclusion
None
11 comments

By now I've written a bunch of blog posts on brain architecture and algorithms, not in any particular order and generally interspersed with long digressions into Artificial General Intelligence. Here I want to summarize my key ideas in one place, to create a slightly better entry point, and something I can refer back to in certain future posts that I'm planning. If you've read every single one of my previous posts (hi mom!), there's not much new here.

In this post, I'm trying to paint a picture. I'm not really trying to justify it, let alone prove it. The justification ultimately has to be: All the pieces are biologically, computationally, and evolutionarily plausible, and the pieces work together to explain absolutely everything known about human psychology and neuroscience. (I believe it! Try me!) Needless to say, I could be wrong in both the big picture and the details (or missing big things). If so, writing this out will hopefully make my wrongness easier to discover!

Pretty much everything I say here and its opposite can be found in the cognitive neuroscience literature. (It's a controversial field!) I make no pretense to originality (with one exception noted below), but can't be bothered to put in actual references. My previous posts have a bit more background, or just ask me if you're interested. :-P

So let's start in on the 7 guiding principles for how I think about the brain:

1. Two subsystems: "Neocortex" and "Subcortex"

This is the starting point. I think it's absolutely critical. The brain consists of two subsystems. The neocortex is the home of "human intelligence" as we would recognize it—our beliefs, goals, ability to plan and learn and understand, every aspect of our conscious awareness, etc. etc. (All mammals have a neocortex; birds and lizards have an homologous and functionally-equivalent structure called the "pallium".) Some other parts of the brain (hippocampus, parts of the thalamus and basal ganglia) help the neocortex do its calculations, and I lump them into the neocortex subsystem. I'll use the term subcortex for the rest of the brain (midbrain, amygdala, etc.).

2. Cortical uniformity

I claim that the neocortex is, to a first approximation, architecturally uniform [LW · GW], i.e. all parts of it are running the same generic learning algorithm in a massively-parallelized way.

The two caveats to cortical uniformity (spelled out in more detail at that link [LW · GW]) are:

3. Blank-slate neocortex

(...But not blank-slate subcortex! More on that below.)

I claim that the neocortex starts out as a "blank slate": Just like an ML model with random weights, the neocortex cannot make any correct predictions or do anything useful until it learns to do so from previous inputs, outputs, and rewards.

(By the way, I am not saying that the neocortex's algorithm is similar to today's ML algorithms. There's more than one blank-slate learning algorithm [LW · GW]! See image.)

A "blank slate" learning algorithm, as I'm using the term, is one that learns information "from scratch"—an example would be a Machine Learning model that starts with random weights and then proceeds with gradient descent. When you imagine it, you should not imagine an empty void that gets filled with data. You should imagine a machine that learns more and better patterns over time, and writes those patterns into a memory bank—and "blank slate" just means that the memory bank starts out empty. There are many such machines, and they will learn different patterns and therefore do different things. See next section, and see also the discussion of hyperparameters in the previous section.

Why do I think that the neocortex starts from a blank slate? Two types of reasons:

4. What is the neocortical algorithm?

4.1. "Analysis by synthesis" + "Planning by probabilistic inference"

"Analysis by synthesis" means that the neocortex searches through a space of generative models for a model that predicts its upcoming inputs (both external inputs, like vision, and internal inputs, like proprioception and reward). "Planning by probabilistic inference" (term from here) means that we treat our own actions as probabilistic variables to be modeled, just like everything else. In other words, the neocortex's output lines (motor outputs, hormone outputs, etc.) are the same type of signal as any generative model prediction, and processed in the same way.

Here's how those come together. As discussed in Predictive Coding = RL + SL + Bayes + MPC [LW · GW], and shown in this figure below:

This combination allows both good epistemics (ever-better understanding of the world), and good strategy (planning towards goals) in the same algorithm. This combination also has some epistemic and strategic failure modes—e.g. a propensity to wishful thinking—but in a way that seems compatible with human psychology & behavior, which is likewise not perfectly optimal, if you haven't noticed. Again, see the link above for further discussion.

Criteria by which generative models rise to prominence in the neocortex; see Predictive Coding = RL + SL + Bayes + MPC [LW · GW] for detailed discussion.

4.2. Compositional generative models

Each of the generative models consists of predictions that other generative models are on or off, and/or predictions that input channels (coming from outside the neocortex—vision, hunger, reward, etc.) are on or off. ("It's symbols all the way down.") All the predictions are attached to confidence values, and both the predictions and confidence values are, in general, functions of time (or of other parameters—I'm glossing over some details). The generative models are compositional, because if two of them make disjoint and/or consistent predictions, you can create a new model that simply predicts that both of those two component models are active simultaneously. For example, we can snap together a "purple" generative model and a "jar" generative model to get a "purple jar" generative model. They are also compositional in other ways—for example, you can time-sequence them, by making a generative model that says "Generative model X happens and then Generative model Y happens".

PGM-type message-passing: Among other things, the search process for the best set of simultaneously-active generative model involves something at least vaguely analogous to message-passing (belief propagation) in a probabilistic graphical model. Dileep George's vision model is a well-fleshed-out example.

Hierarchies are part of the story but not everything: Hierarchies are a special case of compositional generative models. A generative model for an image of "8" makes strong predictions that there are two "circle" generative models positioned on top of each other. The "circle" generative model, in turn, makes strong predictions that certain contours and textures are present in the visual input stream.

However, not all relations are hierarchical. The "is-a-bird" model makes a medium-strength prediction that the "is-flying" model is active, and the "is-flying" model makes a medium-strength prediction that the "is-a-bird" model is active. Neither is hierarchically above the other.

As another example, the brain has a visual processing hierarchy, but as I understand it, studies show that the brain has loads of connections that don't respect the hierarchy.

Feedforward and feedback signals: There are two important types of signals in the neocortex.

A "feedback" signal is a generative model prediction, attached to a confidence level, which includes all the following:

A "feedforward" signal is an announcement that a certain signal is, in fact, active right now, which includes all the following:

There are about 10× more feedback connections than feedforward connections in the neocortex, I guess for algorithmic reasons I don't currently understand.

In a hierarchy, the top-down signals are feedback, and the bottom-up signals are feedforward.

The terminology here is a bit unfortunate. In a motor output hierarchy, we think of information flowing "forward" from high-level motion plan to low-level muscle control signals, but that's the feedback direction. The forward/back terminology works better for sensory input hierarchies. Some people say "top-down" and "bottom-up" instead of "feedback" and "feedforward" respectively, which is nice and intuitive for both input and output hierarchies. But then that terminology gets confusing when we talk about non-hierarchical connections. Oh well.

(I'll also note here that "mainstream" predictive coding discussions sometimes talk about feedback signals being associated with confidence intervals for analog feedforward signals, rather than confidence levels for binary feedforward signals. I changed it on purpose. I like my version better.)

5. The subcortex steers the neocortex towards biologically-adaptive behaviors.

The blank-slate neocortex can learn to predict input patterns, but it needs guidance to do biologically adaptive things. So one of the jobs of the subcortex is to try to "steer" [LW · GW] the neocortex, and the subcortex's main tool for this task is its ability to send rewards to the neocortex at the appropriate times. Everything that humans reliably and adaptively do with their intelligence, from liking food to making friends, depends on the various reward-determining calculations hardwired into the subcortex.

6. The neocortex is a black box from the perspective of the subcortex. So steering the neocortex is tricky!

Only the neocortex subsystem has an intelligent world-model. Imagine you just lost a big bet, and now you can't pay back your debt to the loan shark. That's bad. The subcortex needs to send negative rewards to the neocortex. But how can it know? How can the subcortex have any idea what's going on? It has no concept of a "bet", or "debt", or "payment" or "loan shark".

This is a very general problem. I think there are two basic ingredients in the solution.

Here's a diagram to refer to, based on the one I put in Inner Alignment in the Brain [LW · GW]:

Schematic illustration of some aspects of the relationship between subcortex & neocortex. See also my previous post Inner Alignment in the Brain [LW · GW] for more on this.

 

6.1 The subcortex can learn what's going on in the world via its own, parallel, sensory-processing system.

Thus, for example, we have the well-known visual processing system in our visual cortex, and we have the lesser-known visual processing system in our midbrain (superior colliculus). Ditto for touch, smell, proprioception, nociception, etc.

While they have similar inputs, these two sensory processing systems could not be more different!! The neocortex fits its inputs into a huge, open-ended predictive world-model, but the subcortex instead has a small and hardwired "ontology" consisting of evolutionarily-relevant inputs that it can recognize like faces, human speech sounds, spiders, snakes, looking down from a great height, various tastes and smells, stimuli that call for flinching, stimuli that one should orient towards, etc. etc., and these hardwired recognition circuits are connected to hardwired responses.

For example, babies learn to recognize faces quickly and reliably in part because the midbrain sensory processing system knows what a face looks like, and when it sees one, it will saccade to it, and thus the neocortex will spend disproportionate time building predictive models of faces.

...Or better yet, instead of saccading to faces itself, the subcortex can reward the neocortex each time it detects that it is looking at a face! Then the neocortex will go off looking for faces, using its neocortex-superpowers to learn arbitrary patterns of sensory inputs and motor outputs that tend to result in looking at people's faces. 

6.2 The subcortex can see the neocortex's outputs—which include not only prediction but imagination, memory, and empathetic simulations of other people.

For example, if the neocortex never predicts or imagines any reward, then the subcortex can guess that the neocortex has a grim assessment of its prospects for the future—I'll discuss that particular example much more in an upcoming post on depression.

To squeeze more information out of the neocortex, the subcortex can also "teach" the neocortex to reveal when it is thinking of one of the situations in the subcortex's small hardwired ontology (faces, spiders, sweet tastes, etc.—see above). For example, if the subcortex rewards the neocortex for cringing in advance of pain, then the neocortex will learn to favor pain-prediction generative models that also send out cringe-motor-commands. And thus, eventually, it will also start sending weak cringe-motor-commands when imagining future pain, or when empathically simulating someone in pain—and the subcortex can detect that, and issue hardwired responses in turn.

See Inner Alignment in the Brain [LW · GW] for more examples & discussion of all this stuff about steering.

Unlike most of the other stuff here, I haven't seen anything in the literature that takes "how does the subcortex steer the neocortex?" to be a problem that needs to be solved, let alone that solves it. (Let me know if you have!) ...Whereas I see it as The Most Important And Time-Sensitive Problem In All Of Neuroscience—because if we build neocortex-like AI algorithms, we will need to know how to steer them towards safe and beneficial behaviors!

7. The subcortical algorithms remain largely unknown

I think much less is known about the algorithms of the subcortex (midbrain, amygdala, etc.) than about the algorithms of the neocortex. There are a couple issues:

As mentioned above, I am very unhappy about this state of affairs. For the project of building safe and beneficial artificial general intelligence, I feel strongly that it would be better if we reverse-engineered subcortical algorithms first, and neocortical algorithms second.

Conclusion

Well, my brief summary wasn't all that brief after all! Congratulations on making it this far! I'm very open to questions, discussion, and criticism. I've already revised my views on all these topics numerous times, and expect to do so again. :-)

11 comments

Comments sorted by top scores.

comment by evhub · 2020-09-14T23:06:10.024Z · score: 21 (9 votes) · LW(p) · GW(p)

Some things which don't fully make sense to me:

  • If the cortical algorithm is the same across all mammals, why do only humans develop complex language? Do you think that the human neocortex is specialized for language in some way, or do you think that other mammal's neocortices would be up to the task if sufficiently scaled up? What about our subcortex—do we get special language-based rewards? How would the subcortex implement those?
  • Furthermore, there are lots of commonalities across human languages—e.g. word order patterns and grammar similarities, see e.g. linguistic universals—how does that make sense if language is neocortical and the neocortex is a blank slate? Do linguistic commonalities come from the subcortex, from our shared environment, or from some way in which our neocortex is predisposed to learn language?
  • Also, on a completely different note, in asking “how does the subcortex steer the neocortex?” you seem to presuppose that the subcortex actually succeeds in steering the neocortex—how confident in that should we be? It seems like there are lots of things that people do that go against a naive interpretation of the subcortical reward algorithm—abstaining from sex, for example, or pursuing complex moral theories like utilitarianism. If the way that the subcortex steers the neocortex is terrible and just breaks down off-distribution, then that sort of cuts into your argument that we should be focusing on understanding how the subcortex steers the neocortex, since if it's not doing a very good job then there's little reason for us to try and copy it.
comment by steve2152 · 2020-09-15T02:23:40.342Z · score: 10 (6 votes) · LW(p) · GW(p)

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 [LW · GW] 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" [EA(p) · GW(p)] 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...)

comment by Adam Scholl (adam_scholl) · 2020-09-16T07:16:56.812Z · score: 18 (8 votes) · LW(p) · GW(p)

Your posts about the neocortex have been a plurality of the posts I've been most excited reading this year. I am super interested in the questions you're asking, and it has long driven me nuts that I don't find these questions asked often in the neuroscience literature.

But there's an aspect of these posts I've found frustrating, which is something like the ratio of "listing candidate answers" to "explaining why you think those candidate answers are promising, relative to nearby alternatives."

Interestingly, I also have this gripe when reading Friston and Hawkins. And I feel like I also have this gripe about my own reasoning, when I think about this stuff—it feels phenomenologically like the only way I know how to generate hypotheses in this domain is by inducing a particular sort of temporary overconfidence.

I don't feel incentivized to do this nearly as much in other domains, and I'm not sure what's going on. My lead hypothesis is that in neuroscience, data is so abundant, and theories/frameworks so relatively scarce, that it's unusually helpful to ignore lots of things—e.g. via the "take as given x, y, z, and p" motion—in order to make conceptual progress. And maybe there's just so much available data here that it would be terribly sisiphean to try to justify all the things one takes as given when forming or presenting intuitions about underlying frameworks. (Indeed, my lead hypothesis for why so many neuroscientists seem to employ strategies like, "contribute to the 'understanding road systems' project by spending their career measuring the angles of stop-sign poles relative to the road," is that they feel it's professionally irresponsible, or something, to theorize about underlying frameworks without first trying to concretely falsify a sisiphean-rock-sized mountain of assumptions).

Still, I think some amount of this motion is clearly necessary to avoid accidentally deluding yourself, and the references in your posts make me think you do at least some of it already. So I guess I just want to politely—and super gratefully, I'm really glad you write these posts regardless! If trying to do this would turn you into a stop sign person, don't do it!—suggest that explicating these more might make it easier for readers to understand and come to share your intuitions.

I have more proto-questions about your model than I have time to flesh them out well enough to describe, but here are some that currently feel top-of-mind:

  • Say there exist genes that confer advantage in math-ey reasoning. By what mechanism is this advantage mediated, if the neocortex is uniform? One story, popular among the "stereotypes of early 2000s cognitive scientists" section of my models, is that brains have an "especially suitable for maths" module, and that genes induce various architectural changes which can improve or degrade its quality. What would a neocortical uniformist's story be here—that genes induce architectural changes which alter the quality of the One Learning Algorithm in general? 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?
    • 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? Are the objections mostly similar to those of Marcus et al. [LW · GW]?
  • Do you have the intuition that aspects of the neocortical algorithm itself (or the subcortical algorithms themselves) might be safety-relevant? Or is your safety-relevance intuition mostly about the subcortical steering mechanism? (Fwiw, I have the former intuition—i.e., I'm suspicious that some of the features of the neocortical algorithm that cause humans to differ from "optimizers" exist for safety-relevant reasons).
  • In general I feel intensely frustrated with the focus in neuroscience on the implementational Marr Level, relative to the computational and algorithmic levels. I liked the mostly-computational overview here, and the algorithmic sketch in your Predictive Coding = RL + SL + Bayes + MPC [LW · GW] post, but I feel bursting with implementational questions. For example:
    • As I understand it, you mention "PGM-type message-passing" as a candidate class of algorithm that might perform the "select the best from a population of models" function. 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?
    • I don't currently know whether the neuroscience lit contains good descriptions of how credit assignment is implemented. Do you? Do you feel like you have a decent guess, or know whether someone else does?
      • I have the same question about whatever mechanism approximates Bayesian priors—I keep encountering vague descriptions of it being encoded in dopamine distributions, but I haven't found a good explanation of how that might actually work.
  • Are you sure PP deemphasizes the "multiple simultaneous generative models" frame? I understood the references to e.g. the "cognitive economy" in Surfing Uncertainty to be drawing an analogy between populations of individuals exchanging resources in a market, and populations of models exchanging prediction error in the brain.
  • Have you thought much about whether there are parts of this research you shouldn't publish? I notice feeling slightly nervous every time I see you've made a new post, I think because I basically buy the "safety and capabilities are in something of a race" hypothesis, and fear that succeeding at your goal and publishing about it might shorten timelines.
comment by steve2152 · 2020-09-17T04:44:19.112Z · score: 12 (4 votes) · LW(p) · GW(p)

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 [LW(p) · GW(p)]...

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.

comment by steve2152 · 2020-09-16T19:26:17.017Z · score: 8 (4 votes) · LW(p) · GW(p)

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

comment by romeostevensit · 2020-09-14T21:40:32.384Z · score: 15 (8 votes) · LW(p) · GW(p)

Trying to summarize your current beliefs (harder than it looks) is one of the best way to have very novel new thoughts IME.

comment by avturchin · 2020-09-15T14:02:57.055Z · score: 7 (4 votes) · LW(p) · GW(p)

I have several questions:

Where are qualia and consciousness in this model?

Is this model address difference between two hemispheres?

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

How this model explain the phenomenon of night dreams?

comment by steve2152 · 2020-09-15T15:24:43.205Z · score: 5 (3 votes) · LW(p) · GW(p)

Good questions!!!

Where are qualia and consciousness in this model?

See my Book Review: Rethinking Consciousness [LW · GW].

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.

comment by avturchin · 2020-09-15T17:41:53.809Z · score: 5 (3 votes) · LW(p) · GW(p)

Thanks. I think that a plausible explanation of dreaming is generating of virtual training environments where an agent is training to behave in the edge cases, on which it is too costly to train in real life or in real world games. That is why the generic form of the dreams is nightmare: like, a lion attack me, or I am on stage and forget my speech.

From "technical" point view, dream generation seems rather simple: if the brain has world-model generation engine, it could generate predictions without any inputs, and it will look like an dream.

comment by avturchin · 2020-09-16T22:02:05.902Z · score: 4 (2 votes) · LW(p) · GW(p)

I reread the post and have some more questions:

  • Where is "human values" in this model? If we give this model to an AI which wants to learn human values and have full access to human brain, where it should search for human values?
  • If cortical algorithm will be replaced with GPT-N in some human mind model, will the whole system work?
comment by steve2152 · 2020-09-17T12:52:15.332Z · score: 4 (2 votes) · LW(p) · GW(p)

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 [LW(p) · GW(p)]), 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 [LW · GW]) 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.