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This passage really distills it down to the essence of what happened: "Despite skepticism in the neuroscience community, Markram won over the people who really

This passage really distills it down to the essence of what happened: "Despite skepticism in the neuroscience community, Markram won over the people who really mattered: funders at the European Commission, who seem to have looked less closely at the proposal's scientific feasibility than at its potential economic and political payoff."

rndn 11y ago
There is a great recent Omega Tau episode on the reasons why mega projects often fail: http://omegataupodcast.net/2015/09/181-why-megaprojects-fail-and-what-to-do-about-it/ http://omegataupodcast.net/2015/09/181-why-megaprojects-fail...
- Houshalter 11y ago
  Most ambitious projects fail, period. Most startups don't succeed. I don't know if size is the issue. I don't think a high failure rate even matters. If only 10% of billion dollar projects succeed, then spending $10 billion should get you something equivalent to a working human brain simulation. And hopefully at least something is learned from the failures. Even they don't succeed at building a human brain doesn't mean they don't learn anything.
  - wpietri 11y ago
    The interesting question for me is how to buy those 9/10 failures more cheaply. Can we get our $1 billion failures for only $100m each? Or even $10 million? Then we can buy a lot more successes. I think the main barrier there, though, is that funders have to be in favor of interesting failure. With the described mega-science funding project, there's a big incentive to come up with dramatic plans that are novel and promise certain success. I think the tech industry isn't perfect at this; we can all name companies that spent way too much money for what they learned. But our FFF/seed/A/B funding model gets us a good discount on interesting failures.
  - rndn 11y ago
    You've got no clue what you are talking about, do you? The thing is that many mega projects fail because of ridiculous reasons; mostly because high ranking people make dicisions outside the range of their expertise. Startup projects have very different characteristics and different stakeholders (i.e. mostly private). If 90% of the money could be saved then that's pretty bad.
joe_the_user 11y ago
Given the limited understanding of neuroscience and the problems of Markram's vision, the whole affair had the earmarks of a doomed project. One can see similar problems in smaller effort by Palm Founder Jeff Hawkin's efforts to jump-start algorithmically oriented neuroscience research with his book On Intelligence and his company Numenta. With all the failures pretty visible now, I'd like to say I think there's some validity in seeing if one can extract a broad vision out of all the disparate threads of neuroscience research. There have been events in the history of science when outsiders walked in and saw a forest instead of a lot of trees - see Craig Venter, Alfred Wegener and others (and there have been many more cranks who claimed to the same of course). Making some effort to see if that kind of approach can gel is worth some effort in the study of the brain, a field whose complexity is so huge that gradual, iterative research could continue in places for a thousand years and not answer basic questions.
- plonh 11y ago
  It is interesting that after Numenta's flop we have Google Brain and the academic neural net community deploying amazing successes.
  - p1esk 11y ago
    Actually, Numenta is doing well. They keep working towards understanding how a human neocortex works. It took academic neural net community more than 50 years to achieve "amazing successes". Compared to that timeline, Numenta is moving pretty fast.
  - astazangasta 11y ago
    These two things have nothing to do with each other. Deep learning, despite the use of the world "neural", has nothing to do with neuroscience, and mapping the human brain is not the same as making an artificial neural network.

rndn 11y ago

There is a great recent Omega Tau episode on the reasons why mega projects often fail: http://omegataupodcast.net/2015/09/181-why-megaprojects-fail-and-what-to-do-about-it/ http://omegataupodcast.net/2015/09/181-why-megaprojects-fail...

Houshalter 11y ago
Most ambitious projects fail, period. Most startups don't succeed. I don't know if size is the issue. I don't think a high failure rate even matters. If only 10% of billion dollar projects succeed, then spending $10 billion should get you something equivalent to a working human brain simulation. And hopefully at least something is learned from the failures. Even they don't succeed at building a human brain doesn't mean they don't learn anything.
- wpietri 11y ago
  The interesting question for me is how to buy those 9/10 failures more cheaply. Can we get our $1 billion failures for only $100m each? Or even $10 million? Then we can buy a lot more successes. I think the main barrier there, though, is that funders have to be in favor of interesting failure. With the described mega-science funding project, there's a big incentive to come up with dramatic plans that are novel and promise certain success. I think the tech industry isn't perfect at this; we can all name companies that spent way too much money for what they learned. But our FFF/seed/A/B funding model gets us a good discount on interesting failures.
- rndn 11y ago
  You've got no clue what you are talking about, do you? The thing is that many mega projects fail because of ridiculous reasons; mostly because high ranking people make dicisions outside the range of their expertise. Startup projects have very different characteristics and different stakeholders (i.e. mostly private). If 90% of the money could be saved then that's pretty bad.

joe_the_user 11y ago

Given the limited understanding of neuroscience and the problems of Markram's vision, the whole affair had the earmarks of a doomed project. One can see similar problems in smaller effort by Palm Founder Jeff Hawkin's efforts to jump-start algorithmically oriented neuroscience research with his book On Intelligence and his company Numenta. With all the failures pretty visible now, I'd like to say I think there's some validity in seeing if one can extract a broad vision out of all the disparate threads of neuroscience research. There have been events in the history of science when outsiders walked in and saw a forest instead of a lot of trees - see Craig Venter, Alfred Wegener and others (and there have been many more cranks who claimed to the same of course). Making some effort to see if that kind of approach can gel is worth some effort in the study of the brain, a field whose complexity is so huge that gradual, iterative research could continue in places for a thousand years and not answer basic questions.

plonh 11y ago
It is interesting that after Numenta's flop we have Google Brain and the academic neural net community deploying amazing successes.
- p1esk 11y ago
  Actually, Numenta is doing well. They keep working towards understanding how a human neocortex works. It took academic neural net community more than 50 years to achieve "amazing successes". Compared to that timeline, Numenta is moving pretty fast.
- astazangasta 11y ago
  These two things have nothing to do with each other. Deep learning, despite the use of the world "neural", has nothing to do with neuroscience, and mapping the human brain is not the same as making an artificial neural network.