calepayson

The only case I've experienced where it wasn’t was when someone in our area was actively listening in in emergency channels and trying to preempt ambulances. The issue was that they had training in the basics but often went past that in care they provided. Something that I believe is not covered by Good Samaritan laws.

I’m much more worried about folks like that than people who find themselves in an emergency and are trying to help.

I have shown up at scenes where people have googled what to do though and, you know what, it was super helpful.

If someone is dumb enough to perform a tracheotomy because an llm, google, or a passerby told them to. The issue isn’t any of those factors. That person is just so incredibly dumb as to be a danger to everyone around them.

Same goes for the others, if you have the means I think you should get a tutor or an editor as well.

However, if you’re choosing between nothing and an llm then the llm starts to become a great option.

I used to work rescue and if I had caught one of my coworkers asking an llm how to treat a patient I would have flipped.

But if I had rolled up to an incident and some Good Samaritan was trying to help out by using an llm that would be awesome!

I think my sense is that the zeitgeist around AI (at least in business circles) is much more “The only way to ensure our continued survival is by embracing ai in all our core competencies” than “your tire company is going to have some adequate hr for a great price.”

An example that springs to mind is the arms race between tech CEOs over who can have more of their code base written by llms.

It’s amazing tech and it seems like it’s being marketed for all the wrong things based off of some future promise of super intelligence.

I really liked the article posted on here a week or two back along the lines of AI is a normal technology. Imo, the most sane narrative I’ve read about where this tech is at.

But I think current AI (not where it might be in a few months or years) is absolutely amazing for disadvantaged people. Access to someone who's average is so freaking cool if you don't already have it. Used correctly it's a free math tutor, a free editor for any papers you write, a free advice nurse.

This sucks in a business setting but I could see it being incredible in a charitable setting. When businesses try to replace someone great with something average it sucks. But if you're replacing something non-existent with something average, that can be life changing.

I'm an AI skeptic and I can empathize with his AI enthusiasm given the problems he's trying to address (or at least professes to be trying to address).

No one wants to be evil but losing a job is hard. Most people will try to push back against something that seems wrong and, when faced with the choice of being Morally Right or Financially Secure, are going to chose the path that keeps food on the table and ensures their kids can keep going to the same school.

Still I can’t recommend it highly enough. Act I is enough to grock the concept and you can crush it in an evening.

I think you're pointing out a bit of a chicken vs. the egg situation here.

We have no idea how intelligence works and I expect this will be the case until we create it artificially. Because we have no idea how it works, we put out a variety of metrics that don't measure intelligence but approximate something that only an intelligent thing could do (we think). Then engineers optimize their ML systems for that task, we blow by the metric, and everyone is left feeling a bit disappointed by the fact that it still doesn't feel intelligent.

Neuroscience has plenty of theories for how the brain works but lacks the ability to validate them. It's incredibly difficult to look into a working brain (not to mention deeply unethical) with the necessary spatial and temporal resolution.

I suspect we'll solve the chicken vs. egg situation when someone builds an architecture around a neuroscience theory and it feels right or neuroscientists are able to find evidence for some specific ML architecture within the brain.

Love this.

> I think the clinical shift to focus on 'the individual' instead of the ensemble has overlooked the importance of inter-brain synchronisation

My sense is this is also the case for our cultural story of neuroscience. We can run massive analysis of the brain and what brain regions or neurons or transmitters are being used/activated and it all boils down to something we can't conceptualize. Even the principal components seem to be too complex for us to grock.

With this barrage of complexity I feel like we have a (bad but understandable) habit of reducing to the individual. We talk about the grandmother neuron or what region x is responsible for. We harp on the cog sci of individuals and we run experiments where we try to isolate them for "confounding factors" (other people).

I keep coming back to evolutionary theory as the middle ground between this over and under simplification of cognition.

On a high level, I think Calvin’s theory is in the exact same vein as Dennett’s. Seems like we could sum them both up by saying the brain is paralleled and there is competition between thoughts?

What grabs me about Calvin’s work is he gets much closer to how this might work than anything else I’ve seen. Even cooler, I think he provides enough depth that we could potentially design a new architecture around his theory.

It’s the only theory of how the brain works that I’ve come across that seems like it could be valid. Unfortunately, like other posters have already mentioned, neuroscience is incredibly complex and we just don’t have the tools to test it.

Even if it ends up being completely wrong, it’s a beautiful theory and well worth checking out!

I'd recommend just banging out chapters 1-4 of the book (~60 pages). Lot's of diagrams and I think you'll get the meat of the idea.

Thanks for the feedback!

I think we agree and would love to dive a bit deeper with you here. My background is in biology and I'm very much an enthusiastic amateur when it comes to CS.

When I first read about Darwin Machines, I looked up "evolutionary algorithms in AI", thought to myself "Oh hell ya, these CS folks are on it" and then was shocked to learn that "evolutionary algorithms" seemed to be based on an old school conception of evolution.

First, evolution is on your team, it hates random search. In biology point mutations are the equivalent of random search, and organisms do everything in their power to minimize them.

As I said in the article, If we were building a skyscraper and someone told us they wanted to place some bricks at random angles "so that we might accidentally stumble upon a better design" we would call them crazy. And rightfully so.

Evolution still needs variation though, and it gets it through recombination. Recombination is when we take traits that we know work, and shuffle them to get something new. It provides much more variation with a much smaller chance of producing something that decreases fitness.

It took me a while to grok how recombination produces anything novel, if we're shuffling existing traits how do we get a new trait? I still don't have a "silver-bullet" answer for this but I find that I usually visualize these concepts too far up the hierarchy. When I think of traits I think of eye color or hair color (and I suspect you do to). A trait is really just a protein (sometimes not even that) and those examples are the outliers where a single protein is responsible.

It might be better to think of cancer suppression systems, which can be made up of thousands of proteins and pathways. They're like a large code base that proofreads. Imagine this code base has tons of different functions for different scenarios.

Point mutations, what evolution hates, is like going into that code base and randomizing some individual characters. You're probably going to break the relevant function.

Recombination, what evolution loves, is like going in and swapping two functions that take the same input, produce the same output, but are implemented differently. You can see how this blind shuffling might lead to improvements.

How evolution creates new functions is a much more difficult topic. If you're interested, I recommend "The Selfish Gene". It's the best book I've ever read.

>Gradient descent and other gradient-based optimizers are way way faster where we can apply them

The second point is based on my (limited) understanding of non-biology things. Please point me in the right direction if you see me making a mistake.

Gradient descent etc. are way faster when we can apply them. But I don't think we can apply them to these problems.

My understanding of modern machine learning is that it can be creative in constrained environments. I hear move 37 is a great example but I don't know enough about go to feel any sort of way about it. My sense is: if you limit the problem space gradient decent can find creative solutions.

But intelligence like you or I's operates in an unconstrained problem space. I don't think you can apply gradient descent because, how the heck could you possibly score a behavior?

This is where evolution excels as an algorithm. It can take an infinite problem space and consistently come up with "valid" solutions to it.

>the brain probably can't do proper backprop for architectural reasons but I am confident it uses something much smarter than blind evolutionary search.

I think Darwin Machines might be able to explain "animal intelligence". But human intelligence is a whole other deal. There's some incredible research on it that is (as far as I can tell) largely undiscovered by AI engineers that I can share if you're interested.

I agree that training data is hugely important but I think it does matter how the brain is made. Structures in the brain are remarkably well preserved between species. Despite the fact that evolution loves to try different methods, if it can get away with it.

> Searching the environment provides the data brain is trained on. I don't believe we can understand the brain in isolation without its data engine and the problem space where it develops.

I completely agree and suspect we might be on the same page. What I find most compelling about the idea of Darwin Machines is the fact that it relies on evolution. In my opinion, true Dawkinsian evolution, is the most efficient search algorithm.

I'd love to hear you go deeper on what you mean by data engine and problem space. To (possibly) abuse those terms, I think evolution is the data engine. The problem space is fun and I love David Eagleman's description of the brain as sitting in a warm bath in a dark room trying to figure out what to do with all these electric shocks.

> Neural nets showed that given a dataset, you can obtain similar results with very different architectures, like transformer and diffusion models, or transformer vs Mamba. The essential ingredient is data, architecture only needs to pass some minimal bar for learning.

My understanding of neural nets, and please correct me if I'm wrong, is that they solve system-one thinking, intuition. As of yet, they haven't been able to do much more than produce an average of their training data (which is incredible). With a brute force approach they can innovate in constrained environments, e.g. move 37 (or so I'm told, I haven't played go :)). I haven't seen evidence that they might be able to innovate in open-ended environments. In other words, there's no suggestion they can do system-two thinking where time spent on a problem correlates with the quality of the answer.

> Studying just the brain misses the essential - we are search processes, the whole life is search for optimal actions, and evolution itself is search for environment fitness.

I completely agree. I even suspect that, in a few years, we'll see "life" and "intelligence" as synonymous concepts, just implemented in different mediums. At the same time, studying those mediums can be a blast.

I'm with you here. I wrote this because I wanted to drive people towards the book. It's incredible and I did it little justice.

> "cortical activity produces spatial patterns which somehow 'compete' and the 'winner' is chosen which is then reinforced through a 'reward'"

A slight modification: spatio-temporal patterns*. Otherwise you're dead on.

> 'Compete', 'winner', and 'reward' are all left undefined in the article.

You're right. I left these undefined because I don't believe I have a firm understanding of how they work. Here's some speculation that might help clarify.

Compete - The field of minicolumns is an environment. A spatio-temporal pattern "survives" when a minicolumn is firing in that pattern. It's "fit" if it's able to effectively spread to other minicolumns. Eventually, as different firing patterns spread across the surface area of the neocortex, a border will form between two distinct firing patterns. They "Compete" insofar as each firing pattern tries to "convert" minicolumns to fire in their specific pattern instead of another.

Winner - This has two levels. First, an individual firing pattern could "win" the competition by spreading to a new minicolumn. Second, amalgamations of firing patterns, the overall firing pattern of a cortical column, could match reality better than others. This is a very hand-wavy answer, because I have no intuition for how this might happen. At a high level, the winning thought is likely the one that best matches perception. How this works seems like a bit of a paradox as these thoughts are perception. I suspect this is done through prediction. E.g. "If that person is my grandmother, she'll probably smile and call my name". Again, super hand-wavy, questions like this are why I posted this hoping to get in touch with people who have spent more time studying this.

Reward - I'm an interested amateur when it comes to ML, and folks have been great about pointing out areas that I should go deeper. I have only a basic understanding of how reward functions work. I imagine the minicolumns as small neural networks and alluded to "reward" in the same sense. I have no idea what that reward algorithm is or if NNs are even a good analogy. Again, I really recommend the book if you're interested in a deeper explanation of this.

> the theory is not new information and seems incredibly analogous to Hebbian learning which is a long-standing theory in neuroscience.

I disagree with you here. Hebbian learning is very much a component of this theory, but not the whole. The last two constraints were inspired by it and, in hindsight, I should have been more explicit about that. But, Hebbian learning describes a tendency to average, "cells that fire together wire together". Please feel free to push back here but, the concept of Darwin Machines fits the constraints of Hebbian learning while still offering a seemingly valid description of how creative thought might occur. Something that, if I'm not misunderstanding, is undoubtedly new information.

> I don't see any evidence that the brain will always produce several candidate activity patterns before judging a winner based on consensus.

That's probably my fault in the retelling, check out the book: http://williamcalvin.com/bk9/index.htm

I think if you read Chapters 1-4 (about 60 pages and with plenty of awesome diagrams) you'd have a sense for why Calvin believes this (whether you agree or not would be a fun conversation).

> The tangent of cortical columns ignores key deep brain structures and is also almost irrelevant, the brain could use the proposed 'evolutionary' process with any architecture.

I disagree here. A common mistake I think we to make is assuming evolution and natural selection are equivalent. Some examples of natural selection: A diversified portfolio, or a beach with large grains of sand due to some intricacy of the currents. Dawkinsian evolution is much much rarer. I can only think of three examples of architectures that have pulled it off. Genes, and their architecture, are one. Memes (imitated behavior) are another. Many animals imitate, but only one species has been able to build architecture to allow those behaviors to undergo an evolutionary process. Humans. And finally, if this theory is right, spatiotemporal patterns and the columnar architecture of the brain is the third.

Ignoring Darwin Machines, there are only two architectures that have led to an evolutionary process. Saying we could use "any architecture" seems a bit optimistic.

I appreciate the thoughtful response.

Thanks for the info. Is there anything you would recommend to dive deeper into this? Books/papers/courses/etc.

> recommend not to oversimplify structure here. what you describing is only high-level structure of single part of brain (neocortex).

Nice suggestion. I added a bit to make it clear that I'm talking about the neocortex.

> 1 & 2

Totally. I don't think AI is a simple as building a Darwin Machine, much like it's not as simple as building a neural net. But I think the concept of a Darwin Machine is an interesting, and possibly important, component.

My goal with this post was to introduce folks who hadn't heard of this concept and, hopefully, get in contact with folks who had. I left out the other so I could try to focus on what matters.

> temporal dimension is important. your article is very ML-like focusing on information processing devoid of temporal dimension. if you want to draw parallels to real neurons in brain, need to explain how it fits into temporal dynamics (oscillations in neurons and circuits).

Correct me if I misunderstand, but I believe I did. The spatio-temporal firing patterns of minicolumns contain the temporal dimension. I touched on the song analogy but we can go deeper here.

Let's imagine the firing pattern of a minicolumn as a melody that fits within the period of some internal clock (I doubt there's actually a clock but I think it's a useful analogy). Each minicolumn starts "singing" its melody over and over, in time with the clock. Each clock cycle, every minicolumn is influenced by its neighbors within the network and they begin to sync up. Eventually they're all harmonizing to the same melody.

A network might propagate a bunch of different melodies at once. When they meet, the melodies "compete". Each tries to propagate to a new minicolumn and fitness is judged by other inputs to that minicolumn (think sensory) and the tendencies of that minicolumn (think memory).

I think the evolution is an incredible algorithm is because it relies as much as it does on time.

> is this competition in realm of abeyant (what you can think in principle) or current (what you think now) representations? what's the timescales and neurological basis for this?

I'm not familiar with these ideas but let me give it a shot. Feel free to jump in with more questions to help clarify.

Neural Darwinism points to structures - minicolumns, cortical columns, and interesting features of their connections - and describes one possibility for how those structures might lead to thought. In your words, I think the structures are the realm of abeyant representations while the theory describes current representations.

The neurological basis for this, the description of the abeyant representation (hope I'm getting that right), is Calvin's observations of the structure of the brain. Observations based on his and other's research.

To a large extent, neuroscience doesn't have a great through-line-story of how the brain works. For example the idea of regions of the brain responsible for specific functions - like the hippocampus for memory - doesn't exactly play nice with Karl Lashley's experimental work on memory.

What I liked most about this book is how Calvin tried to relate his theory to both structure and experimental results.

> overall, my take it is a bit ML-like talk. if it describes real neurological networks it got to be closer and stronger neurological footing.

If, by ML-like talk, you mean a bit woo-woo and hand wavy. Ya, I agree. Ideally I'd be a better writer. But I'm not, so I highly recommend the book.

It's written by an incredible neuroscientist and, so far, none of the neuroscience researchers I've given it to have expressed anything other than excitement about it. And I explicitly told them to keep an eye out for places they might disagree. One of them is currently reading it a second time right now with the goal verifying everything. If it all checks out, he plans on presenting the ideas to his lab. I'll update the post if he, or anyone in his lab, finds something that doesn't check out.

> here is some good material, if you want to dive into neuroscience. "Principles of Neurobiology", Liqun Luo, 2020 and "Fundamental Neuroscience", McGraw Hill.

Why these two textbooks? I got my B.S. in neuroscience so I feel good about the foundations. Happy to check these out if you believe they add something that many other textbooks are missing.