The Future of The Brain (2014)

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Re: The Future of The Brain (2014)

Postby gmalivuk » Sun Nov 06, 2016 3:16 pm UTC

aph wrote:Oh, I'm trolling a bit
If you want to be banned, you could just ask.
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Re: The Future of The Brain (2014)

Postby The Great Hippo » Sun Nov 06, 2016 3:42 pm UTC

aph wrote:Like the chances of a monkey typing out the Odyssey. Sure. That is why the chances that someone has a new discovery isn't the relevant variable. And I'm not talking about me telling something new. The likelihood of a new discovery is very small, even for long-time neuroscientists. The relevant variable is just how well their prediction fits actual experimental data. I don't know if it would even be possible to calculate the likelihood that, say, a patent office clerk would discover something new about space, time and gravity.
Einstein was also a certified physics and mathematics teacher from Zürich Polytechnic. Out of those two datapoints -- 'patent clerk', and 'physicist' -- which strikes you as the most relevant regarding his discoveries in physics?
aph wrote:This was from Hamming's The Art of Doing Science and Engineering. According to him, many innovations come from outside fields. Though, you're probably right, innovations usually come from within the field.
Innovations come from new people joining the field with new ideas -- and becoming part of that field.

The bit you're citing from Hamming's book talks about technological innovations that occur in parallel, and allow fields to jump ahead. One example he gives is how radiocarbon dating (physics) helped revolutionize archaeology (history). But there's a big difference between physicists discovering a way to use radioactive decay in a certain type of carbon to determine age versus a computer programmer creating an abstraction that manages to simulate the enormously complex and emergent properties of human learning.

Tub linked this comic, and it's actually relevant in more than just an abstract sense: Our brains are a product of an aggressive optimization process running in every cell of every organism that has ever lived for the past 4 billion years. You think a computer programmer can replicate that?
aph wrote:Neuroscience is still a young field. There are very few neuroscience-specific undergrad programs, and a lot of people come in for PhDs or postdocs from other fields. There are people from many different backgrounds. Jeff Hawkins was an electrical engineer before turning to brains, to name a famous outsider. Other than various biologists, there are mathematicians, physicists, computer programmers, psychologists, roboticists.. with significant contributions. I definitely wouldn't be surprised if some new machine learning algorithm devised by a computer programmer turns out to closely resemble a biological learning process. Programmers are just better trained in thinking about algorithms.
The human brain does not contain a learning algorithm. The human brain is a learning algorithm. It's an emergent property. Trying to reproduce it would be like trying to reproduce a human being.

The funny thing is, most clever programmers probably wouldn't even bother trying. We didn't start flying until we stopped trying to copy birds; AI probably won't happen until we stop trying to copy the brain. Mother nature is an abhorrent programmer.

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Re: The Future of The Brain (2014)

Postby Izawwlgood » Sun Nov 06, 2016 4:48 pm UTC

Yes, you've made it abundantly clear that you have no interest in learning about neuroscience, defending your ideas like a mature, scientifically literate adult, or presenting the work you've done in pursuit/support of your pet theory. And the numerous people who have pointed this out to you have all gotten the same responses. This is pretty much how every crackpot thread around these parts go, frankly. You're another Steve Waterman, or treefid (treatid? I can't remember), etc. Oh! That guy who was insistent that microtubules explain consciousness because they're quantum wires! Man, good times all over the science forums.

Anyway -

GH wrote:The funny thing is, most clever programmers probably wouldn't even bother trying. We didn't start flying until we stopped trying to copy birds; AI probably won't happen until we stop trying to copy the brain. Mother nature is an abhorrent programmer.


Which is one of the reasons it's so hilarious when physicists or programmers who don't know much about neuroscience walk into it and say 'Huh, you're all doing this wrong, let me show you the way". 'physicists meeting a new problem' is something I've witnessed countless times, in settings from bar discussions to actual tenured professors and the sad thing is, they can be enormously helpful when they listen to what is actually being asked for. Reverse the order, and all that.
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Re: The Future of The Brain (2014)

Postby The Great Hippo » Sun Nov 06, 2016 8:25 pm UTC

(I should hasten to add that I don't believe the human brain has *nothing* to teach us about pattern recognition, and in fact, as I recall, there's been some really cool work done in neural networking that takes some inspiration from neuron functionality. But neural networks are not a case where programmers taught neurologists something new about the brain; they're a case where neurologists taught programmers something new about information processing.)

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Re: The Future of The Brain (2014)

Postby aph » Mon Nov 07, 2016 5:59 pm UTC

The Great Hippo wrote:Einstein was also a certified physics and mathematics teacher from Zürich Polytechnic. Out of those two datapoints -- 'patent clerk', and 'physicist' -- which strikes you as the most relevant regarding his discoveries in physics?

If you have no other datapoints, sure, you can look at someones background as a heuristic. If you have the experimental results, it doesn't really matter what the persons background is. I mean, it shouldn't matter, but scientists are people with biases, and sometimes genuine discoveries by outsiders are harder to publish and get recognized. That is just what happens in all fields of science.
The bit you're citing from Hamming's book talks about technological innovations that occur in parallel, and allow fields to jump ahead. One example he gives is how radiocarbon dating (physics) helped revolutionize archaeology (history). But there's a big difference between physicists discovering a way to use radioactive decay in a certain type of carbon to determine age versus a computer programmer creating an abstraction that manages to simulate the enormously complex and emergent properties of human learning.

Tub linked this comic, and it's actually relevant in more than just an abstract sense: Our brains are a product of an aggressive optimization process running in every cell of every organism that has ever lived for the past 4 billion years. You think a computer programmer can replicate that?

I suppose that depends on how you think about the complexities of human learning, and how strictly you apply the 'entrance to the field' criterion. Take Demmis Hassabis of Google's Deep Mind - he was first a programming prodigy, studied computer science, worked as a programmer, and went for a PhD in neuroscience after that. Arguably, he invented one of the best learning algorithms today, the 'deep reinforcement' method. While he does have official credentials of a neuroscientist, it is not hard to imagine someone without the credentials dramatically improving his invention; either in the direction of closer fit to biological brains or in the direction of being better in playing go or image recognition or whatever.

And it works the other way too; a neuroscientist, psychologist or biologist could learn how to program computers and make improvements or inventions based on his observations and theories of biological learning.
The human brain does not contain a learning algorithm. The human brain is a learning algorithm. It's an emergent property. Trying to reproduce it would be like trying to reproduce a human being.

The funny thing is, most clever programmers probably wouldn't even bother trying. We didn't start flying until we stopped trying to copy birds; AI probably won't happen until we stop trying to copy the brain. Mother nature is an abhorrent programmer.

There are simpler brains that need to be reproduced before the human brain.

There were always two currents in AI - one was going for 'building an airplane, because airplanes don't fly like birds, so artificial brains don't need to work like biological brains'; and the other was more interested in copying biological processes.

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Re: The Future of The Brain (2014)

Postby aph » Mon Nov 07, 2016 7:03 pm UTC

An interesting chapter is one by Matteo Carandini:
A fundamental mandate of neuroscience is to reveal how neural circuits lead to perception, thought, and ultimately behavior. The general public might think this goal is already achieved: when a news report says that a behavior is associated with some part of the brain, people tend to take that statement as an explanation. But neuroscientists know that most aspects of behavior result from neural circuits that are yet to established.
[...]
Can we indeed go directly from circuits to behavior, or might that be a bridge too far?
[...]
Imagine that instead of the brain we were trying to understand a laptop computer, but with the knowledge and tools available a hundred years ago. Physiologists might discover and characterize transistors, chips, buses, clocks, and hard drives. Anatomists might strive for a “connectome” of the wires across and within the chips. A furious debate, however, might divide them, as the details of wiring would differ across models (older versus newer) and across brands (AMD versus Intel). Psychologists might concentrate on general input- output properties of software applications, but those who study a business application would disagree with those studying video games. No theories, at this stage, would likely connect the hardware to the operation of the computer.
[...]
What discovery would bridge this gap between circuits and behavior? It would be the realization that there is an intermediate level: the level of computer languages and operating systems. This level neatly decouples the hardware from the software. Different models and brands have different circuits but perform exactly the same computations.

He goes on to acknowledge the imperfection of the analogy, but also gives examples of candidates discovered in the nervous system for these operating-system level computations: filtering, summing, divisive normalization, exponentiation, and so on. Computational processes on this level, he argues, might also be discovered starting from behavior of an animal or a particular segment of the nervous system, finding the mathematical model, and only then looking for implementation details. The main problem is the intermediate stage that connects behavior of the animal with the behavior of neurons.

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Re: The Future of The Brain (2014)

Postby The Great Hippo » Mon Nov 07, 2016 7:27 pm UTC

aph wrote:If you have no other datapoints, sure, you can look at someones background as a heuristic. If you have the experimental results, it doesn't really matter what the persons background is. I mean, it shouldn't matter, but scientists are people with biases, and sometimes genuine discoveries by outsiders are harder to publish and get recognized. That is just what happens in all fields of science.
Einstein was a physicist -- not an outsider. The patent clerk gig was just his day-job.

Anyone can submit a paper for peer review (depending on the journal; very rarely, some may require membership). The review process might look at your credentials, sure; if you don't have them, they're probably going to be more critical, and mistakes will lead to your paper being rejected rather than returned with requests for revision. But if you've got something worth looking at -- and you take the time to polish the paper, letting colleagues preview it to iron out any possible errors -- anyone can publish something in a field. Even if they don't have a background in that field.
aph wrote:I suppose that depends on how you think about the complexities of human learning, and how strictly you apply the 'entrance to the field' criterion. Take Demmis Hassabis of Google's Deep Mind - he was first a programming prodigy, studied computer science, worked as a programmer, and went for a PhD in neuroscience after that. Arguably, he invented one of the best learning algorithms today, the 'deep reinforcement' method. While he does have official credentials of a neuroscientist, it is not hard to imagine someone without the credentials dramatically improving his invention; either in the direction of closer fit to biological brains or in the direction of being better in playing go or image recognition or whatever.
If by 'credentials' we mean 'comprehension of the subject', then yes -- it is tremendously hard to imagine someone doing either of these things without understanding how to do either of these things.

However, if by 'credentials' we merely mean 'college diplomas', then sure -- it's not hard to imagine someone who comprehends a subject (but never achieved the diploma that indicates comprehension) producing interesting work within that subject.
aph wrote:There are simpler brains that need to be reproduced before the human brain.
Those simpler brains were also produced by 4 billion years of aggressive optimization running in parallel in every cell of every living organism that's ever existed.

Again: You think a computer programmer can simulate that?

I really don't know what your deal is. If all you're saying is that sometimes, something tremendously valuable to a field can originate from outside of that field -- then yes, that is (almost trivially) true. If you're saying that neuroscience should look to computer science for the next big thing in neurology -- then I think that's silly, and I think most neurologists (and computer scientists) would probably agree. Computer scientists should look to neuroscience for the next big thing in computer science, maybe (and, in fact, they already have?).

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Re: The Future of The Brain (2014)

Postby aph » Mon Nov 07, 2016 7:34 pm UTC

The Great Hippo wrote:Those simpler brains were also produced by 4 billion years of aggressive optimization running in parallel in every cell of every living organism that's ever existed.

Again: You think a computer programmer can simulate that?

Yes, definitely. In fact, we're pretty close to simulating the entire c. elegans nervous system, together with receptor and muscle cells. Maybe two-three years. (edit: that is the animal with ~300 neurons and ~7000 synapses)

If all you're saying is that sometimes, something tremendously valuable to a field can originate from outside of that field -- then yes, that is (almost trivially) true.

Cool. Yes, that is what I'm saying.

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Re: The Future of The Brain (2014)

Postby Izawwlgood » Mon Nov 07, 2016 7:57 pm UTC

Your capacity to cherry pick at this point is actually better than your ability to proffer pet theories. Perhaps look to the sentence AFTER the one you quoted?

And again, your point that sometimes innovation comes from outside a field to move a field forward is, as GH said, trivially true. Yes. Sometimes using tools from other fields moves a given field forward dramatically. No, that doesn't mean that it was because the outsiders came in to show them whats what. An easy example being biophysics, where physicists helped biologists explain some phenomena by applying materials science tools to both molecular and macroscale biological problems. When a system is oversimplified into a model, yes, you can learn interesting new things about the system and even make some predictions about it, but no biophysicist would say 'biologists were doing it all wrong until we came along and showed them how a cell really operates, why, it's just physics all the way down'.

I think I even brought this up for you in a previous thread of yours.
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Re: The Future of The Brain (2014)

Postby aph » Mon Nov 07, 2016 8:58 pm UTC

Izawwlgood wrote:Your capacity to cherry pick at this point is actually better than your ability to proffer pet theories. Perhaps look to the sentence AFTER the one you quoted?

The structure is: if you're saying A, that is trivial. If you're saying B, that is silly. I'm saying A, so I'm quoting A. It is a two choice question, not a list of evidence points. It is not that complicated. You're just trying to get my attention by provoking, aren't you, Mr. Actual?

About the rest of the comment, yes, but there are different scales of innovation. Some theories are smaller scoped, and represent building blocks of existing theories. There wasn't anything preexisting in biophysics that was invalidated by say, Hodgkin-Huxley. They added to existing knowledge without much controversy. I agree.

Thomas Kuhn, the guardian saint (*) of crackpots since the '60s, talks about deeper changes, shifts in scientific paradigms, which I'm sure you've heard of. In those cases, some of the existing knowledge turns out to be just wrong, or not even wrong, or barely containing something useful. In psychology, Freud is not really in the picture anymore, behaviorism is.. well... maybe still kicking a bit; there are various theories of personality present today.. The chemical imbalance theory of mental disorders is being questioned. Maybe it is not going to be an outsider, a physicist or mathematician who is going to bring some new paradigm into the wider field of neuroscience, but some things that are believed today are probably flat out wrong.

Also, maybe it is going to be a physicist. Look at this!

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Re: The Future of The Brain (2014)

Postby Izawwlgood » Mon Nov 07, 2016 9:43 pm UTC

Yeah, I really don't know why I'm still bothering - exchanges with you underline that you aren't capable of a scientific discourse, as if the last 4 threads didn't already make that point.

aph wrote:Thomas Kuhn
Not really outside of brief readings on the history of science - I was busy doing actual science, and reading about actual science pertaining to my field, y'know, as "Mr. Actual" and all.

aph wrote:Maybe it is not going to be an outsider, a physicist or mathematician who is going to bring some new paradigm into the wider field of neuroscience, but some things that are believed today are probably flat out wrong.
"If all you're saying is that sometimes, something tremendously valuable to a field can originate from outside of that field -- then yes, that is (almost trivially) true. "

Yes, of course some of the things we believe today are probably flat out wrong (though likely far fewer things than I'm sure you're hoping/claiming/asserting). Most aren't. Most/many/>0 of the gaps are recognized, and virtually all models will directly point at those gaps and say "Yeah, we have no idea". No one is doing work physically modelling actin dynamics and saying "We understand exactly how all actin binding proteins work on actin", etc.

aph wrote:Also, maybe it is going to be a physicist. Look at this!
I love it when science writers use terms like "...has remained largely a mystery despite more than a century of study." Though, did you happen to notice the point that indicates physicists aren't new to neurology ("The brain is, of course, not new ground for physicists")? EDIT: Oh, heh, did you also notice the point that physicists are good at tool development? I.e., "tell the physicists what needs answering, and ask them to develop a tool for it. Then do science using that tool. Thanks physicists!" not "The brilliant outsider comes along and shatters the stagnant field!"

Actually - what was your point linking this? That there are some research questions in the brain that physicists may have some applicable tools for better answering? I mean, yes, of course, perhaps that's why some research questions in the brain are tackled by physicists. That's not new. That's actually quite trivial - interdisciplinary research isn't new.
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Re: The Future of The Brain (2014)

Postby The Great Hippo » Mon Nov 07, 2016 9:49 pm UTC

aph wrote:Yes, definitely. In fact, we're pretty close to simulating the entire c. elegans nervous system, together with receptor and muscle cells. Maybe two-three years. (edit: that is the animal with ~300 neurons and ~7000 synapses)
Look, I'm not a neurologist, but I'm pretty certain any neurologist worth their salt will tell you there's more to a nervous system than the nervous system itself.

Do you have a background in computer science? If so, are you familiar with the idea of programs that work in isolation, but break down in the wild? Mother nature goes in the opposite direction: Her code operates in the wild, but breaks down in isolation. Because her code relies on pre-existing code in the wild.

You can't simulate a brain in a way that credibly reproduces a brain's functionality because a brain relies on all the stuff around it for its functionality. It can't be compressed, abstracted, or removed from its context and still *be* a functional brain; it's part of a genetic algorithm (our DNA) running with countless dependencies (mitochondria, local bacteria) and feedback responses (environment, epigenetics, body chemistry).

aph wrote:Thomas Kuhn, the guardian saint (*) of crackpots since the '60s, talks about deeper changes, shifts in scientific paradigms, which I'm sure you've heard of.
To which I reply by referring you to Isaac Asimov, patron saint of *responding* to crack pots: They were wrong when they said the earth was flat; they were also wrong when they said the earth was a sphere. But if you think the second belief is *just* as wrong as the first, then you're more wrong than both of them put together.

There's a reason the biggest scientific revolutions happened farther in the past. As science becomes more precise, the amount we're completely wrong about gets smaller and smaller. Yes, there's still room for tremendous shifts in scientific paradigms -- but probably not in the places you think.

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Re: The Future of The Brain (2014)

Postby Izawwlgood » Tue Nov 08, 2016 2:55 am UTC

The Great Hippo wrote:You can't simulate a brain in a way that credibly reproduces a brain's functionality because a brain relies on all the stuff around it for its functionality. It can't be compressed, abstracted, or removed from its context and still *be* a functional brain; it's part of a genetic algorithm (our DNA) running with countless dependencies (mitochondria, local bacteria) and feedback responses (environment, epigenetics, body chemistry).
Well, wait - I've been watching Westworld, surely you aren't suggesting that a simulated emotional response is different from an 'natural' emotional response? Because... I mean, whats the point of being a modern Prometheus if one's creations are mere simulacra of the real thing?

But in all seriousness, there have been some pretty cool models of nematodes - the entire connectome is mapped. Which is not remotely to say that we can fully simulate a nematode, because as with all simulations (heh) it's all about the scale of detail. I rotated in a lab that works almost exclusively on characterizing the nematode olfactory circuit, something like 6 neurons, as a model for cilia development. Truthfully, that's the most interesting part about most of these approximations - not that we can simulate an organism in a box, so to speak, but that we can use them as models to tell us something interesting about something else. And at just about every level of detail you're interested in, there are interesting questions that still need answers, and incredibly detailed answers we've discovered #Sciiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiience!
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Re: The Future of The Brain (2014)

Postby aph » Tue Nov 08, 2016 9:22 pm UTC

Izawwlgood wrote:Yeah, I really don't know why I'm still bothering - exchanges with you underline that you aren't capable of a scientific discourse, as if the last 4 threads didn't already make that point.

Females love aggressive scientific discourse. Is it your reproductive period? I think you may be displaying; that is why you're still bothering.

Yes, of course some of the things we believe today are probably flat out wrong (though likely far fewer things than I'm sure you're hoping/claiming/asserting). Most aren't. Most/many/>0 of the gaps are recognized, and virtually all models will directly point at those gaps and say "Yeah, we have no idea". No one is doing work physically modelling actin dynamics and saying "We understand exactly how all actin binding proteins work on actin", etc.

I'd say that problems are on a much higher scale of abstractions, the big-picture ideas, the large scope theories. Things that are known in biophysics and molecular biology of the nervous system are probably going to stay known; theories of perception, cognition, consciousness, learning, autonomous behavior... those will be changed.
Like Carandini, quoted up, says, we need bridging theories between the behavior of neurons and behavior of the whole animal. And we sort of understand a lot about neurons in isolation or small networks, we're missing the other parts.

Actually - what was your point linking this?

Neat call for help, cringy at places.
Though, yes, it is trivial that there is interdisciplinary research in neuroscience.

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Re: The Future of The Brain (2014)

Postby aph » Tue Nov 08, 2016 9:39 pm UTC

The Great Hippo wrote:Do you have a background in computer science? If so, are you familiar with the idea of programs that work in isolation, but break down in the wild? Mother nature goes in the opposite direction: Her code operates in the wild, but breaks down in isolation. Because her code relies on pre-existing code in the wild.

You can't simulate a brain in a way that credibly reproduces a brain's functionality because a brain relies on all the stuff around it for its functionality. It can't be compressed, abstracted, or removed from its context and still *be* a functional brain; it's part of a genetic algorithm (our DNA) running with countless dependencies (mitochondria, local bacteria) and feedback responses (environment, epigenetics, body chemistry).

I agree, and a lot of researchers are looking for a way around the problem of simulated brains behaving nicely in an idealized simulation, and poorly when interacting with a real environment (like when a neural network is connected to a robot).
There is an effort to simulate the environment at the same time; to simulate the randomness in the real world, the noise in most of the signal channels; and generally to create models that achieve the same outcomes in different environments, just like real animals do.

On the question of context, every modeler faces a problem of selecting the proper level of abstraction to simulate. Depends on the research question - sometimes you need to simulate the details of biochemistry, sometimes you can approximate the total effect of thousands of cells. The problem can be simplified.

To which I reply by referring you to Isaac Asimov, patron saint of *responding* to crack pots: They were wrong when they said the earth was flat; they were also wrong when they said the earth was a sphere. But if you think the second belief is *just* as wrong as the first, then you're more wrong than both of them put together.

There's a reason the biggest scientific revolutions happened farther in the past. As science becomes more precise, the amount we're completely wrong about gets smaller and smaller. Yes, there's still room for tremendous shifts in scientific paradigms -- but probably not in the places you think.

Patron saint, right!
And yes, I agree, as science becomes more precise, we get less dramatic shifts. Today, we are very bad at predicting behavior of animals, so that is where we might get the most shifts. Or maybe it will turn out they are fundamentally unpredictable, but there will still be a major shift.

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Re: The Future of The Brain (2014)

Postby gmalivuk » Wed Nov 09, 2016 12:00 am UTC

aph wrote:Females love aggressive scientific discourse. Is it your reproductive period? I think you may be displaying; that is why you're still bothering.

Yeah, we're done here.
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