The Kind of Project To Watch

Blue Brain is an ambitious attempt to model brains.  It may fail, but my guess is that within a half century some project like it will success so spectacularly as to completely remake society, and turn well-placed pioneers into multi-trillionaires.  From Seed:

In the basement of a university in Lausanne, Switzerland sit four black boxes, each about the size of a refrigerator, and filled with 2,000 IBM microchips stacked in repeating rows. Together they form the processing core of a machine that can handle 22.8 trillion operations per second. … This is Blue Brain. …

When he launched the project in the summer of 2005, as a joint venture with IBM, there was still no shortage of skepticism. Scientists criticized the project as an expensive pipedream, a blatant waste of money and talent. … The first phase of the project – “the feasibility phase” – is coming to a close. The skeptics, for the most part, have been proven wrong. It took less than two years for the Blue Brain supercomputer to accurately simulate a neocortical column, which is a tiny slice of brain containing approximately 10,000 neurons, with about 30 million synaptic connections between them. …

“What is holding us back now are the computers.” … In order to accurately simulate the trillion synapses in the human brain, you’d need to be able to process about 500 petabytes of data … That’s about 200 times more information than is stored on all of Google’s servers. … But if computing speeds continue to develop at their current exponential pace, and energy efficiency improves, Markram believes that he’ll be able to model a complete human brain on a single machine in ten years or less. …

Once the team is able to model a complete rat brain – that should happen in the next two years – Markram will download the simulation into a robotic rat, so that the brain has a body. He’s already talking to a Japanese company about constructing the mechanical animal. “The only way to really know what the model is capable of is to give it legs,” he says.

Anders Sandberg tells me this is actually the most impressive project to date.  Details from the whitepaper:

The so far (2006) largest simulation of a full Hodgkin-Huxley neuron network was performed on the IBM Watson Research Blue Gene supercomputer using the simulator SPLIT … It was a model of cortical minicolumns, consisting of 22 million 6-compartment neurons with 11 billion synapses, with spatial delays corresponding to a 16 cm^2 cortex surface and a simulation length of one second real-time. Most of the computational load was due to the synapses, each holding 3 state variables.  The overall nominal computational capacity used was 11.5 TFLOPS, giving 0.5 MFLOPS per neuron or 1045 FLOPS per synapse. Simulating one second of neural activity took 5942 s. The simulation showed linear scaling in performance with the number of processors up to 4096 but began to show some (23%) overhead for 8192 processors.

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  • Ian C.

    I wonder if they could do it faster with specialist hardware? (Not that the IBM super computer isn’t special, but I mean specially designed for them),

  • PK

    I am fairly skeptical of attempts to build brains by simply putting lots of neurons together. The brain will not simply become alive and start acting smart. “neuron-iness” is a superficial property of brains and isn’t the only thing that makes them smart. At some point the information for the goals/instincts/drives has to enter the system at witch point the builders are right back at square one. Encoding goals into a neural network might end up being harder than making an AI from scratch in C++ encoded with goals.

    In conclusion, despite having “neuron-iness” in it, I don’t think this project is any closer to solving the AI problem.

  • Latanius

    Intelligence is the Emergent Property of computers which have computing power at least an order of magnitude bigger than the ones we have access to currently, especially if we use them to exactly simulate the Mysterious and Hidden Secrets of the Brain. Everybody knows that.

  • Nick Tarleton

    This isn’t modeling some random system of neurons, it’s modeling structures specifically based on mammalian brains. There may be some emergence mysticism, but there’s substance too.

  • Douglas Knight

    If they can simulate 10,000 neurons, why not work on simulating the complete nervous system of an organism with fewer neurons than that? Over-ambitious goals like rats seem designed to keep tests out in the future and to optimize the amount of funding they ask for.

  • http://profile.typekey.com/halfinney/ Hal Finney

    I wonder how realistic the goal is to model a complete rat brain in two years. Not only would they need a computer big enough and fast enough to hold a rat brain, they would also need a fully detailed neural map of a rat brain to put into it. Are we that close to having such a map?

    If and when we are ready to do such tests, one has to think about the ethical implications. Scientists seem willing to inflict hardship on rats for the greater good, so perhaps simulating rat brains doesn’t raise ethical concerns. But we should consider the possibility that the simulated rat is conscious, and that it could be in a state of pain and suffering during the simulation.

  • http://n8o.r30.net/ Nato Welch

    “some project like it will success so spectacularly as to completely remake society, and turn well-placed pioneers into multi-trillionaires.”

    Pardon me if I don’t break out my pom poms. Who will control the technology, and what will they use it for? Fewer, richer people are not exactly exciting anymore.

  • http://michaelgr.com Michael G.R.

    “Once the team is able to model a complete rat brain – that should happen in the next two years – Markram will download the simulation into a robotic rat, so that the brain has a body. He’s already talking to a Japanese company about constructing the mechanical animal. “The only way to really know what the model is capable of is to give it legs,” he says.”

    I wonder if it would be easier and just as useful to use a virtual environment first. Robotics just adds a layer of problem that isn’t necessarily useful to learn about brain.

    “I wonder if they could do it faster with specialist hardware? (Not that the IBM super computer isn’t special, but I mean specially designed for them)”

    Faster, and much more expensive.

    ” Fewer, richer people are not exactly exciting anymore. ”

    If someone makes a lot of money without stealing it (via the threat of violence or fraud, or via legal taxation and political connections), that means that the person has created value that others were willing to pay for. I want to live in a world with as many of these people as possible, because the only thing they’re taking away from me is what I’m willing to give them (unlike governments), and and these technologies have a chance of greatly improving my life.

  • Grant

    Michael, I think there is a very real danger of emerging technologies such as AI giving certain groups of people huge amounts of wealth and power. Even if that power is granted through voluntary exchange, it can still be abused. People in power (i.e., in control of vast AIs) might not have many incentives to engage to trade with normal, less-powerful humans when other options (conquest of a sort?) are available to them. Trading with humans might become pointless for anything but manual labor if AIs became too intelligent.

    I’m mostly afraid of what would happen if AIs of human intelligence or greater were created their distribution significantly restricted through regulation, patents, or outright nationalization. Instead of becoming a technology for the masses, AI could be a wielded by those already in power, who have few incentives to share the technology with others.

    Can you imagine the anti-AI rhetoric politicians might spout in the future? I wonder if they’d think it unsafe to use for their own (Skynet?) purposes.

  • http://www.frankhirsch.net Frank Hirsch

    While I don’t think that throwing mips at biophysically detailed models of natural brains is a particularly elegant solution for practical AI, it could still help the understanding of how natural brains work. This understanding might help in the construction of equally powerful but less ressource-hungry models.
    In the end it will be the ones who simulate the relevant mechanisms at the “right” level of abstraction that will give the most bangs per buck, but projects like this might provide valuable insights what more abstract brain models could look like.