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I'm a life-long programmer who presently works in software, but I studied biology in college... mostly because I wanted to learn about learning by studying how

• jasongullickson 16y ago
  Getting close would take an advancement in computer technology of the magnitude of the transition from vacuum tubes in individual boxes to a 32nm Core i7. ...so about 35 years? ;)
  • clistctrl 16y ago
    well, if you're going to use that as a baseline you should also factor in that the rate of advancement is exponential. so maybe 10?
    • jonhendry 16y ago
      That probably helps more if the problem complexity is increasing linearly. If it's going up exponentially with problem size, especially if complexity increases at a faster rate than technology, then you're not going to make much progress. ie, if emulating a human brain is only N times as hard as emulating a flatworm, Moore's Law might do the trick. But if emulating a human brain is more like (flatworm complexity)^(number of cells in human brain - number of cells in flatworm brain) then Moore's Law is unlikely to help for a very long time indeed.
  • api 16y ago
    Maybe, though I am concerned about processing power stagnating. Advancement is governed by economics as well as technical capability. There must be demand for new technology, or a field stagnates. Witness aviation as an example... utterly stagnant outside of military niche applications. People seem to no longer want faster and faster computers, and the market seems to be moving toward lighter-weight lower-power portable devices like netbooks, the iPad, etc. Those have slower CPUs than current-generation desktops. I suppose the extreme gamer and server/datacenter markets are still driving performance, but for how long? One problem is that programmers are not using the capabilities of current-generation processors, partly because the dominant OS (cough Windows cough) makes it horrifically painful to deploy desktop apps. This drives all development to the web and turns desktops into thin clients. In the end this kills demand for performance outside the datacenter market.
    • troystribling 16y ago
      You are correct for consumer electronics. But commercial and scientific computing are driving increases computational power and density that do not seem to be letting up. In the 4 years I have worked in hosting the amount of server you can get in a 4u box has gone from 8 2.5GHz cores to 24 2.5GHz cores and from 32GB to 256GB. The storage requirements and compute requirements for applications are also increasing substantially.
• Retric 16y ago
  I think DNA is actually best described as a 1970's era binary in a running mainframe. It rewrites its own source code depending on outside information. And in order to understand what happens you need to look at a core dump that includes all the interesting bit's floating around and attacking/enveloping the DNA. PS: Don't forget this source code has been hot patched (http://en.wikipedia.org/wiki/Patch_(computing)#Hot_patching http://en.wikipedia.org/wiki/Patch_(computing)#Hot_patching) over 3+ Billion years. Edit: To continue the analogy the boot loader has been lost to time (or sacrificed to free up memory), so getting a working system requires copying not just source code, but also much of the current state of the system.
  • api 16y ago
    Not too bad, but sort of like trying to describe an elephant as a bunch of balloons and a water hose glued onto a bail of hay on a table. I also dislike those sorts of analogies because they are loaded with aesthetic value judgements that do not apply. Programming code that looked like that would be ugly. The genetic system is beautiful and elegant.
    • sliverstorm 16y ago
      > The genetic system is beautiful and elegant. Only because you don't have to maintain it
      • api 16y ago
        That's what's so beautiful and elegant about it. When "executed" it evolves, so it maintains itself.
        • Retric 16y ago
          The better you understand the way DNA works the less elegant it looks. It's a steaming pile of Hacks on top of Hacks and frighteningly buggy. This encodes protean X, however it only folds up correctly 15% of the time. However, Y bumps things up to 70% and Z get's you to 90%. The other 10% well that depends on the shape some of these are used by Z to do... Why do we know this? Well both Y and Z are defective 2-3 percent of the time resulting in... etc. PS: Don't get me wrong the happy path works well most of the time and when it fails early it's just a non viable embryo so no problem. However, saying it's elegant is like saying all airplanes in the sky must be easy to maintain because you never see anyone outside fixing them.
• SMrF 16y ago
  "I think it's now possible with a big rack of multi-core machines in an MPI cluster to approach the capabilities of a fruit fly's brain." This seems so unlikely. Isn't it more likely that it takes a rack of multi-core machines to simulate a fruit fly's brain using our extremely primitive algorithms for approximating intelligence? In other words, it's a software problem.
  • api 16y ago
    You're right. It's a software problem and a hardware problem. Our hardware is not up to the task, and even if it was we wouldn't know how to program it. Evolutionary computation and non-von-Neumann architectures such as stochastic data flow architectures might be where to start. We would not write the code. We would build the right kind of architecture and then evolve the code within that architecture.
    • jacquesm 16y ago
      > We would not write the code. We would build the right kind of architecture and then evolve the code within that architecture. That sounds more like an approach that I think would produce results. The key here is 'right' though, what is right?
• ewjordan 16y ago
  DNA is not a program-- it is a molecule, and one that may very well do things at the quantum level that are biologically important. Sure, but Kurzweil's claim is merely that if DNA can encode everything the brain does with N bits of information, then regardless of how the DNA behaves, we have at least a loose estimate of the level of complexity in the brain. I don't necessarily think that he's right that AI will first be achieved in that manner, but I think I have a bit more faith in human ingenuity than he does, and I think we'll get the software there "by hand" before brain scanning hardware can actually do what he needs it to do. Of course it's true that the decoding scheme and the dynamics of the end result could provide a whole lot of additional complexity, just like it's possible to build a decompression algorithm that allows us to compress a Linux distribution into 1 byte (put the whole program in the decompressor). But quantum effects or no, it seems extremely unlikely that the physics, chemistry, and biology behind all of this are somehow conspiring, magically tuned to provide an awesomely efficient set of basis functions to encode the algorithms that the brain needs to apply - sure, some parts of the brain's work probably exploit physical coincidences that make the overall job easier, but as far as we know, quantum chemistry doesn't offer a "do intelligence" utility function, that will have to be built up from much smaller sub-units. Far more likely is that the bulk of the brain's function is more or less explicitly coded for somewhere within our DNA. The brain is not a neural network. It is an interconnected colony of living cells of a variety of types, and it has been shown that all types of cells in the brain are involved in cognition. "Involved" is a serious weasel word when it comes to the brain, especially when AI is under discussion; everything in your skull is "involved" in cognition, but that doesn't mean it's doing anything particularly important, and it definitely does not mean that every detail of the dynamics is an absolute necessity to obtain Cognition. But I think we might be arguing different things... On a related tangent, I've found that many engineers are sympathetic to intelligent design type arguments against evolution. This is because they try to think about biology like engineers and take these machine analogies literally. [Aside: I don't know too many engineers that buy into intelligent design, but I guess YMMV...] The mistake that biologists make is that they always assume we're thinking about biology, and that our goal is to understand biological systems. But when we're talking about AI, the goal is not to understand the brain, it's to figure out how to do something pretty close to what the brain does. Chances are, that's a much simpler goal than figuring out the dirty details about what every cell in the thing does. But it means that we have to be very careful not to get lost in the muck, worrying about the form of the brain's computations rather than the function. This is tricky, because it may be that the way the brain gets things done is not very well abstracted or comprehensible, so it's possible that we'll need to pick the level from which to draw inspiration from the brain very carefully (I think the apparent uselessness of neural nets in strong AI is a good indication that we might need to abstract at some level other than "neuron"). Again, though, Kurzweil has a very specific (and I'd argue, peculiar, at least relative to most people in AI) view on this, and thinks that full scale detailed brain simulations are the way that we need to do this (or at least that this will be the quickest route to the goal). I suspect even he doesn't think we'll simulate the physics of every neuron in detail, though, preferring to instead abstract away the most important bits of functionality.
• jberryman 16y ago
  relevant to your comment is POSIWID!: the Purpose Of a System Is What It Does
  • randallsquared 16y ago
    That makes the term "purpose" rather useless, doesn't it?
    • orborde 16y ago
      If you think about it, that's evolutionary theory in a nutshell. Organisms don't evolve due to any desire or goal to change; rather, random processes cause mutations, and traits that happen to result in more copies of themselves being made tend to become more common, and that's it. There is no purpose to the changes; they just are, and "better" ones happen to become more common because they duplicate more.
      • randallsquared 16y ago
        I would argue that this just means that some things and processes, like evolution, do not have a purpose, rather than that their purpose is whatever actually happens. If purpose just means result, then you've defined purpose such that it no longer applies to lots of systems we actually already understand the purpose for.
• zoomzoom 16y ago
  Look - all the talk of the brain as a "quantum computer" seems to be more full of "bull" than the talk of mind uploading and 700 year lifespans. Quantum interactions happen in all matter that we observe, so they happen in the brain. There is not that much more to it. Neural computation depends on neurons and synapses, communicating with hormones and electrical impulses. If we can model the significant algorithms and feedback loops that determine these interactions, we can model the brain. No quantum computing is needed. Kurzweil himself would admit that we do not know the details of how all of our technology works. Humans at this point understand very little about atomic physics (we still account for the majority of mass by calling it dark matter and hiding it in a formula constant) yet we can produce atomic explosions. I think that one thing alot of people are missing here is that you only need very little theory before you can apply it. Understanding WHY the theory works is a much harder problem, unfortunately.
  • Estragon 16y ago
    As far as I know, Roger Penrose first proposed the idea that quantum effects on neural processes will make computational modeling of said processes impossible. It is highly speculative, and the strongest evidence it's got going for it is that an eminent physicist wrote a book based on it (The Emperor's New Mind.)
    • zoomzoom 16y ago
      Penrose certainly popularized the idea. Kurzweil addresses this issue in his book "The Singularity is Near."
    • jacquesm 16y ago
      The Emperors New Mind is an excellent primer in physics but it failed to sell the quantum idea of the brain in a convincing way. I expected a rather stronger argument, especially from someone as high up in the hierarchy of science as Roger Penrose, and after laying such a huge foundation. Maybe there is one but if there is I haven't found it in that book.
    • ewjordan 16y ago
      The Emperor's New Mind was (IMO) abysmal, and I was really embarrassed to see someone with such indisputable genius as Penrose put out such drivel. You're very right, the strongest evidence for his theory there is that an eminent physicist wrote on it. That's not a good thing. Please, if you've subjected yourself to that nonsense, cleanse your palette with The Road To Reality - math/physics is where Penrose shines, and that book is him at his brightest.
• jongraehl 16y ago
  "the brain may very well be a quantum computer" This is the minority view in neuroscience. > In quantum terms each neuron is an essentially classical object. Consequently quantum noise in the brain is at such a low level that it probably doesn't often alter, except very rarely, the critical mechanistic behaviour of sufficient neurons to cause a decision to be different than we might otherwise expect... —Michael Clive Price
  • api 16y ago
    You may be right about that, and I don't think the quantum point is essential to what I wrote above. But... I do tend to sympathize with the minority view here. The problem is that I don't think the majority are looking at the complete system. Yes, the macroscopic activation and conduction behavior of neurons probably can be modeled classically. But that behavior, as well as things like where the axons and dendrites connect, is governed at the meta level by the genetic regulatory networks and metabolic machinery of neurons and their support cells. All that involves at least thousands of genes and a lot of interactions that may very well extend down to the quantum level. It's a living cell that grows and changes over time, not a simple gate that can be modeled by an equation. Modeling a neuron like that is like modeling a star as a single point source of light because it looks like that from far away. You can model the way stars look through a telescope like that, but that does not accurately describe what a star is.
• deleted 16y ago
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• borisk 16y ago
  So much nonsense in one place ... >>Biological systems are nothing like anything we would ever engineer You really believe, you can predict what human technology will look like in 50, 100, 10000000 years? >>DNA is not a program A hard disk platter is not a program. >>Nature has had billions of years, and it is way ahead of us. That's why birds are so much faster than planes. >>Cells are not machines..."stochastic quantum probability field device." All modern computers are quantum machines. >>Biology is quantum-scale nanotechnology And so is today electronics.
  • loewenskind 16y ago
    >That's why birds are so much faster than planes. wtf? As if the only goal for flying is to go as fast as possible. A bird can land on a branch that can hardly support its weight while the wind is blowing said branch.
• yters 16y ago
  Your post is remarkably obtuse. By your same logic I can say a program is not executing code, it is a series of electrons moving down different circuit paths. Also true, but such behavior can be described at a higher level as "executing code." Take another example, your post could be considered a message, or it could just be considered electrons emanating from an LCD monitor and getting received by a brain, which in turn generates certain behaviors in a large meat machine. Also true, but your post can also be described as a message about the need for thinking about reality literally instead of abstractly in order to understand truth. However, if we can only access truth by talking about the world literally, then no one, ever, has access to truth. Even today we cannot describe the world literally, at all. An electron is merely an abstract representation of even more fundamental quantum physic dynamics. And, who's to claim quantum physics is really at the bottom of the reality stack? For instance, we have no idea why waveforms collapse to their particular determinate. There is something even more fundamental behind this phenomena we currently have no clue about. Therefore, by your criterion for truth, everything discussed and thought about throughout history has been nothing but gibberish, including your own post. So, clearly you do not even agree with your own claim, since you seem to think you are communicating something to us. I call BS.
• joe_the_user 16y ago
  I recommend reading "The Singularity Is Near". Sure, maybe Kurzweil doesn't understand the brain on any deep level and indeed maybe even those who understand the better than him don't understand it well enough at this point. But Kurzweil's basic argument really isn't about that, it's about the exponential advance of tools and technologies and understanding on multiple level. Will the blue brain project succeed? Will some lesser known project succeed? Will the process take twenty instead of ten years? All unknown but not crucial to the implications of exponential change. When you have tool that are improve exponentially, what you can do tends to improve also. And then the whole process builds on itself. Do I know where this will go? No but I don't you do either.