10 ms·

Ray Kurzweil Responds to "Ray Kurzweil does not understand the brain"

125 points by ankeshk 16y ago

willfully_lost 16y ago
So to some up: "Yes, the brain is very complex, but you don't understand the power of exponential growth in information technology!" Surprising.
- adbge 16y ago
  That should be "to sum up".
- jon_hendry 16y ago
  Exponential growth in IT is great, until you run into a problem that is exponentially harder than you originally estimated.
wolfrom 16y ago
While I don't have the knowledge to dismiss Kurzweil's theories outright (I only have a gut feeling), I must say that this response did not achieve a refutation of the statement that "Ray Kurzweil does not understand the brain". I did not see anything in that response that indicated that Kurzweil is basing his beliefs on anything more than his 2001 adaptation of Moore's law to all things technological; to me, the brain's biology and particularly its physiology fall outside our current notions of technology.
- mechanical_fish 16y ago
  It sounds like it's time once again to link to Bruce Sterling's entertaining, comprehensive review of the Singularity movement: http://foratv.vo.llnwd.net/o33/rss/Long_Now_Podcasts/podcast-2004-06-11-sterling.mp3 http://foratv.vo.llnwd.net/o33/rss/Long_Now_Podcasts/podcast...
- raimondious 16y ago
  I agree — we can't simulate something we don't understand, and it's nearly impossible to estimate how long it will take to understand anything. We don't even know what consciousness is, so how can we even begin to simulate it?
  - Symmetry 16y ago
    Really? Generally I mostly simulate things whose macroscopic behaviour I don't understand, and I simulate them so that I can understand them better.
    - raimondious 16y ago
      It's true that we do that, but we at least understand the framework. Simulated models are the basis of a lot of neuro research, but we need to do so much more biology before we can begin to even try to simulate the brain in any useful way on a macro level.
- jon_hendry 16y ago
  It basically comes down to this: Kurzweil's paralyzing fear of death demands that the human brain be simulated soon, before he dies, so that his consciousness can be preserved in silicon. Add in Moore's Law and an underestimate of the complexity of the brain, and stir.
midnightmonster 16y ago
What I got from the previous article (admittedly as my own synthesis, not afaict from the original text as such) was, sure, the program to build a brain is only X Megabytes, but the computer that runs that program is the human body growing in the physical universe. If you want to be able to run that program, you're going to need to emulate at least the relevant instruction subset of the physical universe. It's hard to know how large is the relevant subset or if computers will ever plausibly get there. If the subset turns out to be quite large, you'd need a computer that could model its own subatomic physics.
- Unseelie 16y ago
  I want to stress that we have the math to do that...and on regular chipsets, its just rather slow..which is inherent to the key to kurzwiel's points, that we're building faster and faster computers, and that brains (and likely the universe) are not the simplest possible expressions of themselves.
- felxh 16y ago
  That is what I got from the previous article as well. However, from reading Kurzweil's article get that this point is irrelevant, because he never suggested to reverse engineer the brain in that way. He used the genome argument as an estimate on how complex the brain is, basically viewing the genome as a data compression of the brain. Myers argument only shows that the decompression algorithm, i.e. the route from the genome to brain, is insanely complex, but it doesn't say anything about the actual complexity of the brain. So in essence, yes, if we would choose to model the brain in a highly compressed form like the human genome we would potentially need a computer that could model it's own subatomic physics, but that's not likely the way we would want to approach this. Anyways, this doesn't mean that the brain isn't very complex and impossible for us to model at the moment. They main question is how long it will take for us until this complexity is manageable (if ever).
  - ntoshev 16y ago
    > the decompression algorithm, i.e. the route from the genome to brain, is insanely complex, but it doesn't say anything about the actual complexity of the brain This is where both Kurzweil and you are wrong. A very complex compression/decompression scheme can achieve much better compression. Intuitively, you can encode some of the information in the decompressor itself, even if it is in a very abstract form. This is the reason why compression competitions include size of the decompressor code, e.g. http://prize.hutter1.net/ http://prize.hutter1.net/
nkassis 16y ago
I at least agree with him that you don't need a trillion lines of code. A couple of lisp macros should do it.
knowtheory 16y ago
Amusingly i think that Kurzweil demonstrates his ignorance all the more clearly in this post. The brain is a product of not just the genome but the environment in which it develops. You are not just the product of your genome. You are the product of your genome and the womb in which you were incubated, and the environmental stressors on your mother. You can't go from genome => organism without a WHOOOOOLE lot of Ceteris Paribus to fill in the gaps. Information theory is really pretty irrelevant to the subject of developing a biological model of the brain. To clarify (having read Kurzweil's post more carefully), a lot of the interesting features of the brain are specified in the configuration and connections of the brain. The base building blocks and types of neurons might be specified in the genome (i'm not positive about that, i'm not a geneticist), but even if that were to be the case, Kurzwiel would have to demonstrate that environmental factors were not critical in the development of the structures of the brain that make us thinking beings. I don't think he can do that. Biologists and computer scientists have spent a long time doing research on the building blocks of neural networks, and not only do the fields still face some difficult and fundamental challenges, but for the time being, it's very clear that we do not have the tools necessary to interrogate the brain in the manner we would need in order to be able to map interesting things out. Perhaps things have changed in the 5 years since i graduated university, but... i haven't heard anything earth shattering that would indicate we do have the level of sophistication to computationally model the brain in an accurate manner, or even promising starts to such an endeavor. Additional Edit: Kurzweils assertion that we can't predict how technology will change is correct. Neither can he. Progress is a discontinuous non-linear process. It may be that we'll be able to track all the particles in the brain and watch them as the develop over time, but... then again it very well be that we won't be able to. Tinkering with brains in invasive ways is difficult, let alone non-invasive ways.
- illumin8 16y ago
  You're mis-characterizing his post: "It is true that the brain gains a great deal of information by interacting with its environment – it is an adaptive learning system." Where does he say that the brain is only the product of its genome?
  - 16y ago
terra_t 16y ago
I've made an "all in" bet that AGI is going to be attained by the "human memome project" long before Kurzweil's biomimetic boondoggle achieves anything -- the biomimetic boondoggle is appealing to many, however, because the whole society is accustomed to flushing hundreds of billions a year down the drain on biomedical technology without any accountability.
- arethuza 16y ago
  "human memome project" - do you mean top down symbolic AI projects like CYC?
  - terra_t 16y ago
    CYC is an important development in that direction, but a modern memomic approach doesn't privilege top-down over bottom-up approaches. Like the human genome, the human memome is already available (in natural language) and the challenge is interpreting it. I don't believe the state of the art is good enough to create an upper ontology that can capture human experience, but sectors of ontology that capture chunks of it can be built from the bottom up now and merged as necessary. Think of it this way. I know something about quantum physics and I know something about french lit crit. I don't need a general theory that encompasses both of them until I actually need that general theory -- at that point I'm going to build out as much of that theory as I need by an appeal to thought, experience and experiment. The immediate term strategy is to find areas in which "unreasonably effective" strategies make it possible to extract facts and partially solve the "grounding problem". It's important to recognize that this is essentially a finite task: like the Earth's atmosphere, the Earth's noosphere has no perfectly defined 'edge'... However, there's a principle of interconnection and attraction of concepts that causes it to form a 'main body' that is essentially finite. The game of 20 questions shows that, more or less, the scale of human shared reality is 10^6 or so terms.
megaman821 16y ago
I believe that when it comes to things in nature that look impossibly complex, that they are really constructed using a series of simple patterns. Understanding the strategies nature employs to create a thinking human brain will yield much more usable results than understanding the bio-mechanical processes which transform genes into a fully functioning brain.
mcantor 16y ago
I wish bloggers could have these kinds of debates without all of the butthurt jabs like "... which so-and-so clearly does not understand..." or "I'm surprised anyone actually pays attention to that kook." If we are so very intelligent, then shouldn't we be smart enough to see that ad hominem attacks result in emotional responses overriding the rational desire to open our minds and learn, instead replacing those instincts with the knee-jerk defensive reaction of tightening our hold on our existing beliefs, right or wrong? I understand that we're all passionately devoted to truth and understanding, and we take umbrage when we perceive that someone is damaging those virtues. But the kind of thinly-veiled high school drama aired in these two posts is what disappoints me more than anything.
- kiba 16y ago
  If we are so very intelligent, then shouldn't we be smart enough to see that ad hominem attacks result in emotional responses overriding the rational desire to open our minds and learn, instead replacing those instincts with the knee-jerk defensive reaction of tightening our hold on our existing beliefs, right or wrong? Human rationality and human intelligence does not alway meet each other. Human that are selected by evolution tend to use cognitive shortcuts as a matter of survival. It is not so useful for finding the truth, however. So we're going to trip and make mistake. That's OK. As long as somebody point it out.
  - mcantor 16y ago
    It's funny how much we demonize prejudice (racism) while simultaneously relying on it for survival (don't stick your hand in the fire). Whoever designed this whole "evolution" nonsense clearly wasn't thinking about social communication at the outset... ;-)
    - shadowfox 16y ago
      I am not sure you can classify "prejudice" as biologically useful in such a general sense as to include both racism and not sticking your hand in the fire
ulvund 16y ago
"Something amazing will happen and it will resemble the human brain". I imagine this is ridiculous to anyone working in Machine Learning, Applied Statistics, AI or what it is called at the moment. Point me to the algorithms that have the potential of resembling the human brain, and I will have a look. Talk a lot about "computers are becoming smarter" and namedrop some brain region names and a lot of technically minded people will zone out.
- cryptoz 16y ago
  Where does that quote come from? Anyway, note the future tense! You then ask for the algorithms that perform those tasks. Well, I think the point is that none of this exists yet but in the future we have a good chance at building it. I'm a technically minded person, and I definitely don't zone out when people are talking about the future. Sure, some kook might suggest we already have those algorithms and then I'd zone out. But that's not what's happening.
- emzo 16y ago
  There is strong evidence that the neocortex works on a common algorithm; vision, hearing, touch, language, behavior, and most everything else the neocortex does are manifestations of a single algorithm applied to different modalities of sensory input. http://en.wikipedia.org/wiki/Hierarchical_temporal_memory http://en.wikipedia.org/wiki/Hierarchical_temporal_memory http://onintelligence.org/ http://onintelligence.org/ http://www.numenta.com/Numenta_HTM_Concepts.pdf http://www.numenta.com/Numenta_HTM_Concepts.pdf
DanielBMarkham 16y ago
I'm a singularian, but much more long-term that Kurzweil. Like 500 years, instead of 20. I think the much more interesting question here isn't "Can hardware simulate the brain or not?" We'll figure that one out eventually. The interesting question is "As hardware and software begin simulating the brain (already happening), and integrating with it (already happening), what are the implications for the species?" What's a half-singularity look like? Because that's very well how this century might turn out, and instead of arguing at the extremes, it's probably much better to focus on the immediate practical implications of what's already happening.
- nkassis 16y ago
  The borg is one example of a half half. Star trek truly invented the future ;p
koeselitz 16y ago
http://scienceblogs.com/pharyngula/upload/2010/08/thinkingmeat.jpeg http://scienceblogs.com/pharyngula/upload/2010/08/thinkingme...
arohner 16y ago
To make the argument more clear: Kurzweil says "The genome can be compressed into X bytes, so that's an upper limit on the complexity needed to simulate the human brain." Meyers says: "No. The genome says 'make a protein with this shape'. We don't understand the full complexity of the brain until we understand all of the physics (including potential quantum effects) that go into protein folding + all the different environmental effects. Further, the information in the physics and protein folding stuff is much much greater than information in the genome".
postfuturist 16y ago
Given the idea that consciousness is related to complex quantum interactions at the molecular level, and the fact that computing power hits hard limits with regard to physical complexity at nano-scale, it seems unlikely that we'll be modeling human brains well enough in 20 years to reproduce conscious human thought.
- Tichy 16y ago
  That quantum-mumbo-jumbo brain thing was just that: an idea. I don't think Penrose has many serious followers with that one.
  - jacquesm 16y ago
    He may have actually lost some credibility there.
- ewjordan 16y ago
  Given the idea that consciousness is related to complex quantum interactions at the molecular level Yes, that's an idea; it just so happens to be (at least outside the world of philosophy, where it's still easy to find people that don't believe in special relativity...) a fringe, unpopular one with pretty much no evidence whatsoever to back it up, because there's still no consensus on what "consciousness" even means, or whether it's definable even in principle, let alone measurable. But in any case, consciousness has absolutely nothing to do with strong AI, which is defined in terms of what the AI can do, not whether its internal states are conscious or not. it seems unlikely that we'll be modeling human brains well enough in 20 years to reproduce conscious human thought Sure thing, I can agree with that, practically as a matter of definition. Luckily, in the practical AI community "conscious human thought" is not what anyone's trying to create. They'd be perfectly happy with non-conscious, non-human thought that can learn a wide variety of things, regardless of how it's implemented.
  - postfuturist 16y ago
    Fair enough. I guess I don't understand what non-conscious thought is supposed to be, exactly.
dasht 16y ago
Kurzweil snipes "It is an argument from information theory, which Myers obviously does not understand." With some delight, I would like to explain how Kurzweil incorrectly uses information theory and arrives at a false conclusion. In fairness, I'll also explain why Kurzweil's main thesis appears to be unassailably correct - albeit mainly because, in the end, he makes only quite weak and uncontroversial claims. (For biological criticisms of Kurzweil, see "knowtheory"'s comment.) Kurzweil seeks to establish an upper bound on a quantity he calls "the amount of information in the brain prior to the brain’s interaction with its environment." He is not terribly precise about how that quantity is to be defined. He does tell us that his upper bound will show that the "design" of the brain isn't very complicated and that it will not require "trillions of lines of code to create a comparable system". To establish his "amount of information" upper bound he looks at the number of "bits" in a complete human genome. A human genome contains about three billion base pairs. Each base pair position can have one of four possible values (e.g., contains two bits of information). Thus he comes up 6 billion bits overall, around 715 Megabytes (he says 800), and he points out that genomes are far from random and asserts that the whole thing can be compressed down to, perhaps, 50 Megabytes. Apparently we are to believe that the amount of "code" needed to build a system "comparable" to the brain can not possibly be "trillions of lines" because the genome does it in less than 50 megabytes. A zygote, in other words is a machine. It runs the code in the DNA. The code tells how to build a brain. Our simulator will be some kind of programmable system. We'll supply it with code. The code will tell it how to make or simulate a brain-like thing. If a zygote does it with a mere 50MB of code, our machine will as well. It is just there, in that last step, that Kurzweil invokes a fallacy. In general, if you have two different kinds of machines, and you want to program each to compute the same result -- upper bounds on the program size on machine tell you nothing about the the upper bounds on the other machine. That is why, for example, when Chaitin lectures about Omega he is always mumbling "relative to some choice of turing machine" (at least initially, until it is then understood to apply throughout the talk). If all you know is that the zygote machine and our simulator machine are two machines - program size on the zygote tells you nothing about program size on the simulator. There is, in other words, no abstract quantity that describes "the complexity of the design of the brain". Information theory does not recognize any such concept. There is no such thing as the irreducible complexity of a program other than relative to a particular machine. The upper bound from the zygote could actually translate to our simulation machine if we agree that the simulator will operate on principles essentially the same as the zygote. Alas, Kurzweil says the opposite: "I did not present studying the genome as even part of the strategy for reverse-engineering the brain. [....] It is not a proposed strategy for accomplishing reverse-engineering [....]" Kurzweil has to give up either the relevancy of the size of the genome, or his denial that we'll build a machine that operates on similar principles to a zygote (or on principles that can be proved computationally similar to a zygote). Either way, he should not be looking down at Myers' understanding of information theory. All of that said, if you strip out his hype his only substantial claims seem to be that we'll build hardware that can run neural network software very efficiently (perhaps in real time for brain-scale networks) and that we'll get clues about useful network topologies from looking at brains. That, my friends, is a perfect message for a "futurist" to deliver to a dazzled audience because, well, to many of us it is what you call "very old, somewhat boringly obvious news".
k0n2ad 16y ago
Kurzweil's retort falls apart in several places: "It is true that the brain gains a great deal of information by interacting with its environment – it is an adaptive learning system. But we should not confuse the information that is learned with the innate design of the brain." He is misunderstanding Myers here - Myers is talking about the physical ontogenesis of the brain during development (in utero), proteins interacting with proteins (and the environment and such) during its development, not the development of the brain through "adaptive learning" "But we can take a much more direct route to understanding the amount of information in the brain’s innate design, which I also discussed: to look at the brain itself. There, we also see massive redundancy. Yes there are trillions of connections, but they follow massively repeated patterns." "Yes, the system learns and adapts to its environment..." Again, Kurzweil is failing to address the crux of Myers argument, that the design of the brain is not only in the genetic code, but in the intricate "playing out" of cells during brain development. Myers did not talk about the brain adapting to a system in the holistic or psychological sense, but on a much more fine-grained biological level.
lancerp 16y ago
There may be only 1 bit of information describing the brain, it doesn't matter, the bits of information do not have a one to one correlation with logical gates or anything else for that matter. It is silly to compare genes with "lines of code", you also need a machine to parse, and eval those lines of code so you should also include the instructions for that machine as well.
10ren 16y ago
I'm writing this to try to clarify my understanding. I think this is the essence of Kurzeil's argument on estimating complexity: Let's take the genome (DNA) as a program + data, and the phenome (the organism) as an output (and assume the mother is in adequate health, and development proceeds normally.) Then the number of different possible phenomes is limited to the number of different genomes (it could be fewer, if there are non-significant regions of the genome, because then more than one genomes could produce the same phenome. That is, the function G->P is not necessarily injective.) While this doesn't directly describe the complexity of a phenome, the argument is that the complexity of a thing is no greater than the means of defining it, and that any additional complexity in the resulting thing must contain redundancies (perhaps very hidden) that can be eliminated. A simple case is a program to print hello 1000 times. The program is short, and though the output is long, it contains redundancies. What about from chaotic systems and fractals, or 'normal' numbers like pi, where great complexity arises from simple rules? The argument is that this is merely apparent complexity, and in reality contains great redundancy. Big output changes from small input changes doesn't disprove this; consider changing 1000 to 2000 in the above. While a particular sequence of pi digits, or a particular fractal frame, might seem complex, there is also the input to consider of the specification of that part (eg which digits) also takes information. --- Here's the theoretical flaw: considering a genome as a program, what if the entire program isn't really listed in the genome, but it calls library functions? Obviously, it becomes much shorter, but we're not measuring the library code, so it's cheating. Or, what if the program is written in a highlevel language rather than a lowlevel one (like assembly) - this is equivalent, if you consider the syntax of the language as causing in function calls. Clearly, we still have the same mapping of program->results, and the number of different results is still limited, but those results are much more complex than the program; they programs don't represent the complexity of the output. How does this apply to the development of a phenome from a genome? Does anything add information, like standard libraries? One might say that the mother is like a standard library - but little information seems to be input in this way (consider development of a chicken egg); and fundamentally, the mother is also a phenome that can be specified by the very genome in question: it's self-hosting. Does protein folding add information? It is very complex, but is that just how it operates (the way that the implementation runs), or does it also add information to the output? Remarks on this seem to say only that it's complex and unknown and therefore hard to simulate. I'm talking about whether the complexity of action adds information to the output. If so, how much complexity? Is it significant, comparable to a standard library, or is it more like a trivial macro? I don't know how much information is added to the phenome by protein folding; this is a question for the biologists. I think addressing it squaring in these terms would defuse much of the emotion in the discussion. But I'll guess: If we assume protein folding is dictated by quite a long sequence (ie. it's not a context of one, like G->down, U->up, A->left, C->right, but a function of say 100s of bases -- and of course the fold direction is not always 90 degrees), then there is scope for an (almost) arbitrarily complex function, from sequence->fold. The complexity is limited by the input (number of bases involved) and output (the actual fold). If we then assume that the shape of the protein is the crucial thing for its interaction with raw materials and other proteins (eg. as an enzyme), then complexity of protein folding does directly translate into complexity of results - even at this, the finest-grain level of operation. Although protein folding potentially adds complexity, I find it hard to imagine that it would add information comparable to a standard library, such as, say 50 bases specifying an eye or a liver (which a standard library might do, like Python's SimpleHttpServer). That would be miraculous, if the laws of physics were so favourable to the particular needs of organisms (like a programming language that is customized to a particular application, as modern libraries contain code for TCP/IP and HTTP.) I find it easy to believe that it's more like the variation in syntax between (say) lisp, java and assembly. So, I don't think protein folding adds much complexity to the result; it's more like a general purpose programming language than a set of specialized library functions (I think this question is the crucial issue in the debate.) --- To conclude, I think the complexity of the genome does estimate the complexity of the phenome: I agree with Kurzeil that the brain is (roughly) as complex as the sections of the genome necessary for its development.
mrpsbrk 16y ago
I have this kinda particular interpretation that "the best science is the one that makes fewer assumptions", or that, at least, is better equipped to access and deal with its own assumptions. In that vein, what bothers me about Kurzweil is that he seems to be just taking some assumptions and running with them. Specifically, i believe that he assumes that intelligence and computation are the same thing. At least, he asserts that both are equimaterial --- that a sufficient powerful calculating machine is bound to be able to generate intelligence. I do not know if intelligence == computation. I do accept that, in many domains, the two things are interchangeable. Like, for example, if you are interviewing for a programming job and you can't do division in your head, that is a bad sign. But if we are looking to build intelligence from computation, then i think we will bump into any differences that exist. If computation and intelligence are equal or at least similar, that would be an interesting fact. It would teach us a lot about ourselves. To my taste it is a very interesting line of questioning. But it is not proven. Really, we can't even really define intelligence! (As a sidenote, i think that is exactly the point of the "Turing Test", not to prove AI, but to show that intelligence is not clearly defined.) If intelligence is a kind of computation, then Moore's Law means AI, definitely. And in that case, Kurzweil estimate of 2 decades is as good as any. If intelligence != computation, then some completely unrelated discovery has to intervene. There is one thing that makes me doubt the assumption of equality, though. Namely, computers are already extremely better at computation than we are. I am 30yo and i can't recall a time when i didn't have available calculators way more powerful and fast than myself. If the translation from computation to intelligence was straightforward, my feeling is that the exponential nature of Moore's should have already made AI a reality before i went out of college.
scotty79 16y ago
> [...] It is true that the information in the genome goes through a complex route to create a brain, but the information in the genome constrains the amount of information in the brain [...] This is false. Genotype-phenotype is not general purpose compression algorithm so there is no limit. You could theoretically compress all information about the universe in single bit. Algorithm for that would be: If you see one then you should return full information about universe and if zero then you should throw exception: "Error in input data." Not every phenotype can be compressed to genotype. There is no DNA for animal on 17 wheels or for a cheese-cake. It is more similar to fractal compression or procedural generation where huge (even infinite) amount of data can be "compressed" into simple rule. If simple math can do something like that then complex machine of physical interactions can do much more.
erikpukinskis 16y ago
This whole argument seems to be about whether you need to include the design of the factory in the specifications for the chip.
mkramlich 16y ago
I'd be more impressed if Ray Kurzweil's AI responded to it.
abecedarius 16y ago
Did that 50MB figure derive from just the protein-coding part of the genome? I just tried out the sequence from http://hgdownload.cse.ucsc.edu/downloads.html#human http://hgdownload.cse.ucsc.edu/downloads.html#human. After converting it to binary, 2 bits per base pair, bzip2 compresses it down to about 650MB (from 715MB uncompressed). I'd like to know what assumptions give you an order of magnitude greater compression, and how probable they are. I suppose the biggest factor has got to be junk DNA, since species can vary so much in genome length -- but I get the impression there's a lot of uncertainty still about functional noncoding DNA. (The other number estimated was how many lines of source code the compressed 50MB or 25MB might correspond to. In the last thread I couldn't get this number to work either, though it came closer; and I actually know something about programs.)

125 points by ankeshk 16y ago

So to some up: "Yes, the brain is very complex, but you don't understand the power of exponential growth in information technology!" Surprising.

adbge 16y ago
That should be "to sum up".
jon_hendry 16y ago
Exponential growth in IT is great, until you run into a problem that is exponentially harder than you originally estimated.

wolfrom 16y ago

While I don't have the knowledge to dismiss Kurzweil's theories outright (I only have a gut feeling), I must say that this response did not achieve a refutation of the statement that "Ray Kurzweil does not understand the brain". I did not see anything in that response that indicated that Kurzweil is basing his beliefs on anything more than his 2001 adaptation of Moore's law to all things technological; to me, the brain's biology and particularly its physiology fall outside our current notions of technology.

mechanical_fish 16y ago
It sounds like it's time once again to link to Bruce Sterling's entertaining, comprehensive review of the Singularity movement: http://foratv.vo.llnwd.net/o33/rss/Long_Now_Podcasts/podcast-2004-06-11-sterling.mp3 http://foratv.vo.llnwd.net/o33/rss/Long_Now_Podcasts/podcast...
raimondious 16y ago
I agree — we can't simulate something we don't understand, and it's nearly impossible to estimate how long it will take to understand anything. We don't even know what consciousness is, so how can we even begin to simulate it?
- Symmetry 16y ago
  Really? Generally I mostly simulate things whose macroscopic behaviour I don't understand, and I simulate them so that I can understand them better.
  - raimondious 16y ago
    It's true that we do that, but we at least understand the framework. Simulated models are the basis of a lot of neuro research, but we need to do so much more biology before we can begin to even try to simulate the brain in any useful way on a macro level.
jon_hendry 16y ago
It basically comes down to this: Kurzweil's paralyzing fear of death demands that the human brain be simulated soon, before he dies, so that his consciousness can be preserved in silicon. Add in Moore's Law and an underestimate of the complexity of the brain, and stir.

midnightmonster 16y ago

What I got from the previous article (admittedly as my own synthesis, not afaict from the original text as such) was, sure, the program to build a brain is only X Megabytes, but the computer that runs that program is the human body growing in the physical universe. If you want to be able to run that program, you're going to need to emulate at least the relevant instruction subset of the physical universe. It's hard to know how large is the relevant subset or if computers will ever plausibly get there. If the subset turns out to be quite large, you'd need a computer that could model its own subatomic physics.

Unseelie 16y ago
I want to stress that we have the math to do that...and on regular chipsets, its just rather slow..which is inherent to the key to kurzwiel's points, that we're building faster and faster computers, and that brains (and likely the universe) are not the simplest possible expressions of themselves.
felxh 16y ago
That is what I got from the previous article as well. However, from reading Kurzweil's article get that this point is irrelevant, because he never suggested to reverse engineer the brain in that way. He used the genome argument as an estimate on how complex the brain is, basically viewing the genome as a data compression of the brain. Myers argument only shows that the decompression algorithm, i.e. the route from the genome to brain, is insanely complex, but it doesn't say anything about the actual complexity of the brain. So in essence, yes, if we would choose to model the brain in a highly compressed form like the human genome we would potentially need a computer that could model it's own subatomic physics, but that's not likely the way we would want to approach this. Anyways, this doesn't mean that the brain isn't very complex and impossible for us to model at the moment. They main question is how long it will take for us until this complexity is manageable (if ever).
- ntoshev 16y ago
  > the decompression algorithm, i.e. the route from the genome to brain, is insanely complex, but it doesn't say anything about the actual complexity of the brain This is where both Kurzweil and you are wrong. A very complex compression/decompression scheme can achieve much better compression. Intuitively, you can encode some of the information in the decompressor itself, even if it is in a very abstract form. This is the reason why compression competitions include size of the decompressor code, e.g. http://prize.hutter1.net/ http://prize.hutter1.net/

nkassis 16y ago

I at least agree with him that you don't need a trillion lines of code. A couple of lisp macros should do it.

knowtheory 16y ago

Amusingly i think that Kurzweil demonstrates his ignorance all the more clearly in this post. The brain is a product of not just the genome but the environment in which it develops. You are not just the product of your genome. You are the product of your genome and the womb in which you were incubated, and the environmental stressors on your mother. You can't go from genome => organism without a WHOOOOOLE lot of Ceteris Paribus to fill in the gaps. Information theory is really pretty irrelevant to the subject of developing a biological model of the brain. To clarify (having read Kurzweil's post more carefully), a lot of the interesting features of the brain are specified in the configuration and connections of the brain. The base building blocks and types of neurons might be specified in the genome (i'm not positive about that, i'm not a geneticist), but even if that were to be the case, Kurzwiel would have to demonstrate that environmental factors were not critical in the development of the structures of the brain that make us thinking beings. I don't think he can do that. Biologists and computer scientists have spent a long time doing research on the building blocks of neural networks, and not only do the fields still face some difficult and fundamental challenges, but for the time being, it's very clear that we do not have the tools necessary to interrogate the brain in the manner we would need in order to be able to map interesting things out. Perhaps things have changed in the 5 years since i graduated university, but... i haven't heard anything earth shattering that would indicate we do have the level of sophistication to computationally model the brain in an accurate manner, or even promising starts to such an endeavor. Additional Edit: Kurzweils assertion that we can't predict how technology will change is correct. Neither can he. Progress is a discontinuous non-linear process. It may be that we'll be able to track all the particles in the brain and watch them as the develop over time, but... then again it very well be that we won't be able to. Tinkering with brains in invasive ways is difficult, let alone non-invasive ways.

illumin8 16y ago
You're mis-characterizing his post: "It is true that the brain gains a great deal of information by interacting with its environment – it is an adaptive learning system." Where does he say that the brain is only the product of its genome?
- 16y ago

terra_t 16y ago

I've made an "all in" bet that AGI is going to be attained by the "human memome project" long before Kurzweil's biomimetic boondoggle achieves anything -- the biomimetic boondoggle is appealing to many, however, because the whole society is accustomed to flushing hundreds of billions a year down the drain on biomedical technology without any accountability.

arethuza 16y ago
"human memome project" - do you mean top down symbolic AI projects like CYC?
- terra_t 16y ago
  CYC is an important development in that direction, but a modern memomic approach doesn't privilege top-down over bottom-up approaches. Like the human genome, the human memome is already available (in natural language) and the challenge is interpreting it. I don't believe the state of the art is good enough to create an upper ontology that can capture human experience, but sectors of ontology that capture chunks of it can be built from the bottom up now and merged as necessary. Think of it this way. I know something about quantum physics and I know something about french lit crit. I don't need a general theory that encompasses both of them until I actually need that general theory -- at that point I'm going to build out as much of that theory as I need by an appeal to thought, experience and experiment. The immediate term strategy is to find areas in which "unreasonably effective" strategies make it possible to extract facts and partially solve the "grounding problem". It's important to recognize that this is essentially a finite task: like the Earth's atmosphere, the Earth's noosphere has no perfectly defined 'edge'... However, there's a principle of interconnection and attraction of concepts that causes it to form a 'main body' that is essentially finite. The game of 20 questions shows that, more or less, the scale of human shared reality is 10^6 or so terms.

megaman821 16y ago

I believe that when it comes to things in nature that look impossibly complex, that they are really constructed using a series of simple patterns. Understanding the strategies nature employs to create a thinking human brain will yield much more usable results than understanding the bio-mechanical processes which transform genes into a fully functioning brain.

mcantor 16y ago

I wish bloggers could have these kinds of debates without all of the butthurt jabs like "... which so-and-so clearly does not understand..." or "I'm surprised anyone actually pays attention to that kook." If we are so very intelligent, then shouldn't we be smart enough to see that ad hominem attacks result in emotional responses overriding the rational desire to open our minds and learn, instead replacing those instincts with the knee-jerk defensive reaction of tightening our hold on our existing beliefs, right or wrong? I understand that we're all passionately devoted to truth and understanding, and we take umbrage when we perceive that someone is damaging those virtues. But the kind of thinly-veiled high school drama aired in these two posts is what disappoints me more than anything.

kiba 16y ago
If we are so very intelligent, then shouldn't we be smart enough to see that ad hominem attacks result in emotional responses overriding the rational desire to open our minds and learn, instead replacing those instincts with the knee-jerk defensive reaction of tightening our hold on our existing beliefs, right or wrong? Human rationality and human intelligence does not alway meet each other. Human that are selected by evolution tend to use cognitive shortcuts as a matter of survival. It is not so useful for finding the truth, however. So we're going to trip and make mistake. That's OK. As long as somebody point it out.
- mcantor 16y ago
  It's funny how much we demonize prejudice (racism) while simultaneously relying on it for survival (don't stick your hand in the fire). Whoever designed this whole "evolution" nonsense clearly wasn't thinking about social communication at the outset... ;-)
  - shadowfox 16y ago
    I am not sure you can classify "prejudice" as biologically useful in such a general sense as to include both racism and not sticking your hand in the fire

ulvund 16y ago

"Something amazing will happen and it will resemble the human brain". I imagine this is ridiculous to anyone working in Machine Learning, Applied Statistics, AI or what it is called at the moment. Point me to the algorithms that have the potential of resembling the human brain, and I will have a look. Talk a lot about "computers are becoming smarter" and namedrop some brain region names and a lot of technically minded people will zone out.

cryptoz 16y ago
Where does that quote come from? Anyway, note the future tense! You then ask for the algorithms that perform those tasks. Well, I think the point is that none of this exists yet but in the future we have a good chance at building it. I'm a technically minded person, and I definitely don't zone out when people are talking about the future. Sure, some kook might suggest we already have those algorithms and then I'd zone out. But that's not what's happening.
emzo 16y ago
There is strong evidence that the neocortex works on a common algorithm; vision, hearing, touch, language, behavior, and most everything else the neocortex does are manifestations of a single algorithm applied to different modalities of sensory input. http://en.wikipedia.org/wiki/Hierarchical_temporal_memory http://en.wikipedia.org/wiki/Hierarchical_temporal_memory http://onintelligence.org/ http://onintelligence.org/ http://www.numenta.com/Numenta_HTM_Concepts.pdf http://www.numenta.com/Numenta_HTM_Concepts.pdf

DanielBMarkham 16y ago

I'm a singularian, but much more long-term that Kurzweil. Like 500 years, instead of 20. I think the much more interesting question here isn't "Can hardware simulate the brain or not?" We'll figure that one out eventually. The interesting question is "As hardware and software begin simulating the brain (already happening), and integrating with it (already happening), what are the implications for the species?" What's a half-singularity look like? Because that's very well how this century might turn out, and instead of arguing at the extremes, it's probably much better to focus on the immediate practical implications of what's already happening.

nkassis 16y ago
The borg is one example of a half half. Star trek truly invented the future ;p

koeselitz 16y ago

http://scienceblogs.com/pharyngula/upload/2010/08/thinkingmeat.jpeg http://scienceblogs.com/pharyngula/upload/2010/08/thinkingme...

arohner 16y ago

To make the argument more clear: Kurzweil says "The genome can be compressed into X bytes, so that's an upper limit on the complexity needed to simulate the human brain." Meyers says: "No. The genome says 'make a protein with this shape'. We don't understand the full complexity of the brain until we understand all of the physics (including potential quantum effects) that go into protein folding + all the different environmental effects. Further, the information in the physics and protein folding stuff is much much greater than information in the genome".

postfuturist 16y ago

Given the idea that consciousness is related to complex quantum interactions at the molecular level, and the fact that computing power hits hard limits with regard to physical complexity at nano-scale, it seems unlikely that we'll be modeling human brains well enough in 20 years to reproduce conscious human thought.

Tichy 16y ago
That quantum-mumbo-jumbo brain thing was just that: an idea. I don't think Penrose has many serious followers with that one.
- jacquesm 16y ago
  He may have actually lost some credibility there.
ewjordan 16y ago
Given the idea that consciousness is related to complex quantum interactions at the molecular level Yes, that's an idea; it just so happens to be (at least outside the world of philosophy, where it's still easy to find people that don't believe in special relativity...) a fringe, unpopular one with pretty much no evidence whatsoever to back it up, because there's still no consensus on what "consciousness" even means, or whether it's definable even in principle, let alone measurable. But in any case, consciousness has absolutely nothing to do with strong AI, which is defined in terms of what the AI can do, not whether its internal states are conscious or not. it seems unlikely that we'll be modeling human brains well enough in 20 years to reproduce conscious human thought Sure thing, I can agree with that, practically as a matter of definition. Luckily, in the practical AI community "conscious human thought" is not what anyone's trying to create. They'd be perfectly happy with non-conscious, non-human thought that can learn a wide variety of things, regardless of how it's implemented.
- postfuturist 16y ago
  Fair enough. I guess I don't understand what non-conscious thought is supposed to be, exactly.

dasht 16y ago

Kurzweil snipes "It is an argument from information theory, which Myers obviously does not understand." With some delight, I would like to explain how Kurzweil incorrectly uses information theory and arrives at a false conclusion. In fairness, I'll also explain why Kurzweil's main thesis appears to be unassailably correct - albeit mainly because, in the end, he makes only quite weak and uncontroversial claims. (For biological criticisms of Kurzweil, see "knowtheory"'s comment.) Kurzweil seeks to establish an upper bound on a quantity he calls "the amount of information in the brain prior to the brain’s interaction with its environment." He is not terribly precise about how that quantity is to be defined. He does tell us that his upper bound will show that the "design" of the brain isn't very complicated and that it will not require "trillions of lines of code to create a comparable system". To establish his "amount of information" upper bound he looks at the number of "bits" in a complete human genome. A human genome contains about three billion base pairs. Each base pair position can have one of four possible values (e.g., contains two bits of information). Thus he comes up 6 billion bits overall, around 715 Megabytes (he says 800), and he points out that genomes are far from random and asserts that the whole thing can be compressed down to, perhaps, 50 Megabytes. Apparently we are to believe that the amount of "code" needed to build a system "comparable" to the brain can not possibly be "trillions of lines" because the genome does it in less than 50 megabytes. A zygote, in other words is a machine. It runs the code in the DNA. The code tells how to build a brain. Our simulator will be some kind of programmable system. We'll supply it with code. The code will tell it how to make or simulate a brain-like thing. If a zygote does it with a mere 50MB of code, our machine will as well. It is just there, in that last step, that Kurzweil invokes a fallacy. In general, if you have two different kinds of machines, and you want to program each to compute the same result -- upper bounds on the program size on machine tell you nothing about the the upper bounds on the other machine. That is why, for example, when Chaitin lectures about Omega he is always mumbling "relative to some choice of turing machine" (at least initially, until it is then understood to apply throughout the talk). If all you know is that the zygote machine and our simulator machine are two machines - program size on the zygote tells you nothing about program size on the simulator. There is, in other words, no abstract quantity that describes "the complexity of the design of the brain". Information theory does not recognize any such concept. There is no such thing as the irreducible complexity of a program other than relative to a particular machine. The upper bound from the zygote could actually translate to our simulation machine if we agree that the simulator will operate on principles essentially the same as the zygote. Alas, Kurzweil says the opposite: "I did not present studying the genome as even part of the strategy for reverse-engineering the brain. [....] It is not a proposed strategy for accomplishing reverse-engineering [....]" Kurzweil has to give up either the relevancy of the size of the genome, or his denial that we'll build a machine that operates on similar principles to a zygote (or on principles that can be proved computationally similar to a zygote). Either way, he should not be looking down at Myers' understanding of information theory. All of that said, if you strip out his hype his only substantial claims seem to be that we'll build hardware that can run neural network software very efficiently (perhaps in real time for brain-scale networks) and that we'll get clues about useful network topologies from looking at brains. That, my friends, is a perfect message for a "futurist" to deliver to a dazzled audience because, well, to many of us it is what you call "very old, somewhat boringly obvious news".

k0n2ad 16y ago

Kurzweil's retort falls apart in several places: "It is true that the brain gains a great deal of information by interacting with its environment – it is an adaptive learning system. But we should not confuse the information that is learned with the innate design of the brain." He is misunderstanding Myers here - Myers is talking about the physical ontogenesis of the brain during development (in utero), proteins interacting with proteins (and the environment and such) during its development, not the development of the brain through "adaptive learning" "But we can take a much more direct route to understanding the amount of information in the brain’s innate design, which I also discussed: to look at the brain itself. There, we also see massive redundancy. Yes there are trillions of connections, but they follow massively repeated patterns." "Yes, the system learns and adapts to its environment..." Again, Kurzweil is failing to address the crux of Myers argument, that the design of the brain is not only in the genetic code, but in the intricate "playing out" of cells during brain development. Myers did not talk about the brain adapting to a system in the holistic or psychological sense, but on a much more fine-grained biological level.

lancerp 16y ago

There may be only 1 bit of information describing the brain, it doesn't matter, the bits of information do not have a one to one correlation with logical gates or anything else for that matter. It is silly to compare genes with "lines of code", you also need a machine to parse, and eval those lines of code so you should also include the instructions for that machine as well.

10ren 16y ago

I'm writing this to try to clarify my understanding. I think this is the essence of Kurzeil's argument on estimating complexity: Let's take the genome (DNA) as a program + data, and the phenome (the organism) as an output (and assume the mother is in adequate health, and development proceeds normally.) Then the number of different possible phenomes is limited to the number of different genomes (it could be fewer, if there are non-significant regions of the genome, because then more than one genomes could produce the same phenome. That is, the function G->P is not necessarily injective.) While this doesn't directly describe the complexity of a phenome, the argument is that the complexity of a thing is no greater than the means of defining it, and that any additional complexity in the resulting thing must contain redundancies (perhaps very hidden) that can be eliminated. A simple case is a program to print hello 1000 times. The program is short, and though the output is long, it contains redundancies. What about from chaotic systems and fractals, or 'normal' numbers like pi, where great complexity arises from simple rules? The argument is that this is merely apparent complexity, and in reality contains great redundancy. Big output changes from small input changes doesn't disprove this; consider changing 1000 to 2000 in the above. While a particular sequence of pi digits, or a particular fractal frame, might seem complex, there is also the input to consider of the specification of that part (eg which digits) also takes information. --- Here's the theoretical flaw: considering a genome as a program, what if the entire program isn't really listed in the genome, but it calls library functions? Obviously, it becomes much shorter, but we're not measuring the library code, so it's cheating. Or, what if the program is written in a highlevel language rather than a lowlevel one (like assembly) - this is equivalent, if you consider the syntax of the language as causing in function calls. Clearly, we still have the same mapping of program->results, and the number of different results is still limited, but those results are much more complex than the program; they programs don't represent the complexity of the output. How does this apply to the development of a phenome from a genome? Does anything add information, like standard libraries? One might say that the mother is like a standard library - but little information seems to be input in this way (consider development of a chicken egg); and fundamentally, the mother is also a phenome that can be specified by the very genome in question: it's self-hosting. Does protein folding add information? It is very complex, but is that just how it operates (the way that the implementation runs), or does it also add information to the output? Remarks on this seem to say only that it's complex and unknown and therefore hard to simulate. I'm talking about whether the complexity of action adds information to the output. If so, how much complexity? Is it significant, comparable to a standard library, or is it more like a trivial macro? I don't know how much information is added to the phenome by protein folding; this is a question for the biologists. I think addressing it squaring in these terms would defuse much of the emotion in the discussion. But I'll guess: If we assume protein folding is dictated by quite a long sequence (ie. it's not a context of one, like G->down, U->up, A->left, C->right, but a function of say 100s of bases -- and of course the fold direction is not always 90 degrees), then there is scope for an (almost) arbitrarily complex function, from sequence->fold. The complexity is limited by the input (number of bases involved) and output (the actual fold). If we then assume that the shape of the protein is the crucial thing for its interaction with raw materials and other proteins (eg. as an enzyme), then complexity of protein folding does directly translate into complexity of results - even at this, the finest-grain level of operation. Although protein folding potentially adds complexity, I find it hard to imagine that it would add information comparable to a standard library, such as, say 50 bases specifying an eye or a liver (which a standard library might do, like Python's SimpleHttpServer). That would be miraculous, if the laws of physics were so favourable to the particular needs of organisms (like a programming language that is customized to a particular application, as modern libraries contain code for TCP/IP and HTTP.) I find it easy to believe that it's more like the variation in syntax between (say) lisp, java and assembly. So, I don't think protein folding adds much complexity to the result; it's more like a general purpose programming language than a set of specialized library functions (I think this question is the crucial issue in the debate.) --- To conclude, I think the complexity of the genome does estimate the complexity of the phenome: I agree with Kurzeil that the brain is (roughly) as complex as the sections of the genome necessary for its development.

mrpsbrk 16y ago

I have this kinda particular interpretation that "the best science is the one that makes fewer assumptions", or that, at least, is better equipped to access and deal with its own assumptions. In that vein, what bothers me about Kurzweil is that he seems to be just taking some assumptions and running with them. Specifically, i believe that he assumes that intelligence and computation are the same thing. At least, he asserts that both are equimaterial --- that a sufficient powerful calculating machine is bound to be able to generate intelligence. I do not know if intelligence == computation. I do accept that, in many domains, the two things are interchangeable. Like, for example, if you are interviewing for a programming job and you can't do division in your head, that is a bad sign. But if we are looking to build intelligence from computation, then i think we will bump into any differences that exist. If computation and intelligence are equal or at least similar, that would be an interesting fact. It would teach us a lot about ourselves. To my taste it is a very interesting line of questioning. But it is not proven. Really, we can't even really define intelligence! (As a sidenote, i think that is exactly the point of the "Turing Test", not to prove AI, but to show that intelligence is not clearly defined.) If intelligence is a kind of computation, then Moore's Law means AI, definitely. And in that case, Kurzweil estimate of 2 decades is as good as any. If intelligence != computation, then some completely unrelated discovery has to intervene. There is one thing that makes me doubt the assumption of equality, though. Namely, computers are already extremely better at computation than we are. I am 30yo and i can't recall a time when i didn't have available calculators way more powerful and fast than myself. If the translation from computation to intelligence was straightforward, my feeling is that the exponential nature of Moore's should have already made AI a reality before i went out of college.

scotty79 16y ago

> [...] It is true that the information in the genome goes through a complex route to create a brain, but the information in the genome constrains the amount of information in the brain [...] This is false. Genotype-phenotype is not general purpose compression algorithm so there is no limit. You could theoretically compress all information about the universe in single bit. Algorithm for that would be: If you see one then you should return full information about universe and if zero then you should throw exception: "Error in input data." Not every phenotype can be compressed to genotype. There is no DNA for animal on 17 wheels or for a cheese-cake. It is more similar to fractal compression or procedural generation where huge (even infinite) amount of data can be "compressed" into simple rule. If simple math can do something like that then complex machine of physical interactions can do much more.

erikpukinskis 16y ago

This whole argument seems to be about whether you need to include the design of the factory in the specifications for the chip.

mkramlich 16y ago

I'd be more impressed if Ray Kurzweil's AI responded to it.

abecedarius 16y ago

Did that 50MB figure derive from just the protein-coding part of the genome? I just tried out the sequence from http://hgdownload.cse.ucsc.edu/downloads.html#human http://hgdownload.cse.ucsc.edu/downloads.html#human. After converting it to binary, 2 bits per base pair, bzip2 compresses it down to about 650MB (from 715MB uncompressed). I'd like to know what assumptions give you an order of magnitude greater compression, and how probable they are. I suppose the biggest factor has got to be junk DNA, since species can vary so much in genome length -- but I get the impression there's a lot of uncertainty still about functional noncoding DNA. (The other number estimated was how many lines of source code the compressed 50MB or 25MB might correspond to. In the last thread I couldn't get this number to work either, though it came closer; and I actually know something about programs.)