Can The Human Brain Project be saved, and should it be?
chronicle.com
chronicle.com
I've seen a good number of boondoggles. At the center of each was a personality of such magnetism that people simply fell in line behind them. There are people who have the ability to spout utter nonsense, but to do so with such alpha authority that nearly everyone in the room just glazes over and nods. Whatever it is that our brains take as a signal of alpha status, these people know how to crank it to 11.
When are people going to learn that primate dominance gestures are not a reliable proxy for much else?
I will say that when actual merit and this level of dominance actually align in the same individual, amazing things can happen. But I see no sign that this is more common than would be predicted by the random assortment of traits.
However, I don't think they (Markram et al.) are that naive. In reality, the project is composed of a lot of different subprojects, in simulation, neuromorphic computing, mapping/characterizing mouse and human brains and doing actual neuroscience, theory, applications to human disease, even philosophy and ethics, etc. see: https://www.humanbrainproject.eu/discover/the-project/sub-pr... and their list of publications so far https://www.humanbrainproject.eu/science/publications
It's a huge umbrella for neuroscience funding that they marketed to the EU under the grand vision of simulating the brain. Whether or not this kind of marketing/funding mechanism is an optimal way to do the best science is a very legitimate issue to debate. But the implication (which the HBP brought upon themselves..) that a bunch of crackpots have been given a billion dollars to buy supercomputers and run nonsensical simulations is an oversimplification.
I was curious why there was only neuroscientists and biologists quoted so I went looking for another source. I think this link gives a different, broader overview of the situation and doesn't only focus on personal attacks.
Specifically neuroscience subproject funding was removed... This seems like angry academics that lost funding are trying to derail it through the media to my uneducated observance...
Moreover while neuroscience is able to show pretty pictures of brain activity, they have made insufficient progress in understanding it. On the other hand the physicists/mathematicians that have invaded the field have a background in computational methods, simulation and hardware development. So for example a group at the University I studied had previous experience designing analog chips for feature detection in High Energy Physics, a number of years ago they re-branded themselves as a neurophysics group and recently landed a ~100 million euro+ grant in the context of the Human Brain Project.
It is also much easier to learn the jargon and read some of the softer phenomenological articles than to develop a sound understanding of the underlying mathematics and physics.
I think the overall problem is that the physicists and the biologists don't have respect for each other's skills and knowledge.
In the case of neurons you can model the membrane of the neuron and the interior and exterior to various degrees of accuracy as an EM problem, if you wish you can even add surface defects. Since the temperature is high, all quantum effects are essentially washed out, as are the need to model individual calcium ions etc., at least to first approximation. After the dust settles you can come up with a few effective parameters that model one neuron and an ODE that models its dynamics. As it turns out the ODE indeed exhibits dynamics similar to real world neurons in response to electric stimuli.
If you then couple those ODEs into larger system and try different coupling configurations (the coupling coefficients model the synapses between neurons), you have come up with a simplified model for parts of a brain.
Psychologists answer only to Biologists.
Biologists answer only to Chemists.
Chemists answer only to Physicists.
Physicists answer only to Mathematicians.
Mathematicians answer only to Philosophers.
I would add that Software Engineers answer only to Mathematicians.
Here's an article about the Human Genome Project from 1990: http://www.nytimes.com/1990/06/05/science/great-15-year-proj...
"The critics argue that the human genome project has been sold on hype and glitter, rather than its scientific merits, and that it will drain talent, money and life from smaller, worthier biomedical efforts."
"They also doubt that the project can be completed in anything close to its original deadline and budget."
"it will have generated enormous reams of uninterpretable and often useless data"
"it's hyped science"
"Everybody I talk to thinks this is an incredibly bad idea"
"Some critics have begun aggressive letter-writing campaigns"
The exact same arguments that were used 25 years ago to discredit the HGP are now resurfacing to criticize the HBP. And with genome sequencing now below 1000$, that article has become almost laughable.
https://twitter.com/michael_nielsen/status/56599887195723366...
To say it another way, I think that playing the lotto with the goal of earning money is a mistake. The fact that some people win it and in fact make money doesn't change the fact that they were wrong to play it in the first place if their goal was net gain of money.
That said I have no idea what the expected value of the HGP was so I have no idea whether it was a good decision. I just want to chime in in support of thinking about the probabilities involved in the right way.
If we want the big advances, we have to push the boundaries of what's known, but it's unclear where, exactly, the big breaks will happen. But doing these kind of mega-projects is, I think, basically a good thing, as it gets us out of the day-to-day of publishing easy papers, and into attacking the 'big' problems in new ways.
Project A wants to investigate in as much depth as possible (given their budget) the effects of nail-biting on arthritis.
Project B wants to investigate in as much depth as possible (given their budget) the effects of learning a programming language on Alzheimer's disease.
I know which of these seems like a better choice for funding, even though I don't really know for certain what the long-term impact of either will be.
(But I should note that I deliberately chose projects where it seemed obvious which one was better, so that I didn't need to do any calculations or even think very hard about which one to prefer. In reality, sometimes it's not obvious, and you should think hard and do calculations and it still might not be obvious, and the specific project being discussed is probably one of those cases.)
In support of project A: fibromyalgia, which has arthritic-type symptoms, is partly linked to anxiety, of which one of the common manifestation is nail-biting. Could evidence of nail-biting be a useful tool as part of differential diagnosis, or a predictor?
In support of project B: the idea of a 'cognitive reserve' in preventing the onset of Alzheimer's disease is fairly well established, even though it has no disease-modifying therapy once the symptoms of decline are already apparent. To what extent can learning a programming language aid that cognitive reserve?
I see your point though, but I'm not sure how well it applies to funding large-scale data collection work like the HGP. Or the LHC, to use an example in another field.
That said, it's an immensely valuable resource that pays back in many ways (it's a map which can be used for discovery science).
It's not entirely clear it drained talent and money from other projects in a way that was a net negative for science and society.
The very same arguments were made against the Human Genome Project, and yet look at where we are.
One good thing coming from HGP for the general public and journalists, I have noticed, is that they have more discernment regarding scientists' promises. Trust, but verify. There is a snowball's chance in hell that NIH will see a substantial budget increase in this decade, or the next for that matter.
BTW, that $1000 human genome sequencing claim is not believed by most scientists. Lawyers can argue it, especially Illumina's, but not according to common sense.
http://www.genome.gov/images/content/cost_genome.jpg
It's around 5k for whole genome sequencing atm and some googling showed that you can do exome sequencing for a lot cheaper (it's a very small fraction of human DNA).
This guy seems to think that the flattening of the tail on that graph is due to Illumina not having enough competition rather than being due to technical limitations at the moment.
http://www.synthesis.cc/2014/02/time-for-new-cost-curves-201...
Full Disclosure: I hold shares in Illumina.
The main problem with the project though is not the missing scientific data - that just makes it unfeasible for now. What makes it unfeasible in any context is the detail level at which the simulation is supposed to be carried out. This quote from the article expresses it better than I could have done:
Eero Simoncelli, a neuroscientist at New York University. "Would you try to understand the universe by simulating every molecule? What would you have achieved? It’s going to be just as complicated as the real thing and you won’t understand it any better."
We can edit, directly observe, and record/playback simulated brains. We can test ten million different models on top of a recorded simulation and see which one fits best. Eero doesn't think we could learn about how the brain works by simulating a trillion slightly different permutations on a brain, or (maybe more ethically) small subsystems of a brain, and observing how each behaves?
Sweet god, the economic implications! http://mason.gmu.edu/~rhanson/uploads.html
Horrifying, maybe, but I don't see how people can get away with suggesting this will never be valuable to anyone.
> I'm a little amazed people aren't seeing the uses of this.
That's not the issue - I imagine everyone here would like to see these goals reached. Nobody has to sell anyone on the rewards of neuro research. The question is merely whether this specific project can deliver them.
> We can edit, directly observe, and record/playback simulated brains.
The idea itself is a good one. However, the key issue becomes choosing an appropriate level of detail for the simulation. I believe a blanket choice of "let's just do the entire brain" is computationally infeasible right now, plus we don't have good enough models to actually program the thing - but most importantly even in a future where these problems are solved the device seems like a blunt and unwieldy instrument that won't give up its data easily.
> or (maybe more ethically) small subsystems of a brain
That's what's already happening all over the world right now, in thousands of independently scoped simulations and experiments.
>That [simulating small subsystems of a brain] is what's already happening all over the world right now, in thousands of independently scoped simulations and experiments.
Woah! Links? The searches I can come up with aren't turning up anything besides that simulation of a rat cortical column and the various attempts at nematode uploading.
Never is a long time ;)
> Woah! Links?
Any university with a neuroscience department does this. Go to your local university's website and browse what they're doing in that area: more likely than not you'll find something interesting. Basic research on neurons has become very common, and computer-based modeling is a fundamental part of it. The perception problem here is that, say, modeling the signaling behavior of locust neurons seems like a very inconsequential piece of the puzzle - but in reality it's what we need to do to figure this stuff out.
The fundamental problem in neuroscience research is not a lack of complexity, for now we need to move away from complexity in order to observe the behavior of basic building blocks. It may seem embarrassing how we're still at that stage, but it's where we stand.
http://www.math.pitt.edu/~bdoiron/
http://neurotheory.columbia.edu/~larry/
http://www.cns.nyu.edu/wanglab/
http://alleninstitute.org/our-research/modeling-analysis-the... (modeling group)
There are also some companies that are led by people with academic experience doing spiking neural network simulation and seem to be trying to commercialize it:
That said, the one benefit I could see is that it's a billion+ motivation for folks to think about a hard problem. They likely won't accomplish a 100 billion neuron sim, but 100's to 1000's of people will hopefully be thinking about that goal, trying to decompose it, and producing useful sub-advances. Maybe like the HGP or the LHC or space flight, intense money will lead to enabling technologies that accelerate what is possible.
The other problem is that humans are horrible at predicting scientific progress, even in fields they are comfortable / experts in. Edge scientists may actually suffer from this the worst. So much of life has been devoted to becoming an expert in the field that it becomes more challenging to consider large changes. Even while most major breakthroughs are built from analogies to other fields, and more "simple" every day phenomena. Ex: Relativity, one of the weirdest advances ever, came from thought about trains.
Maybe a billion+ could be spent more wisely, but at least they're spending it. These are the same folks who decry our governments whenever they reduce funding and malign the sciences. Stop complaining and find a way to write the grant, or meet the right folks, or whatever's necessary so you can be a part of the money train and get something useful done.
And there are many-many other differences. Note that our task is not to solve problems but to figure out how the brain works.
Also, recurrent neural network simulation cannot really be scaled right now, we don't have the hardware. It is not parallelizable with our current tools because of the huge number of connections.
(Disclaimer: I was involved in a project trying to model real neural networks. It wasn't a huge success, but we learned a lot.)
Nonetheless, I look forward to seeing more simple rNNs being created over time (besides the C. elegans one that was modeled recently). Who knows what strange organizational rules or structures we will discover from this strand of research?
It defies understanding why one wouldn't start with a much simpler organism rather than trying to go directly to the human brain. For one thing, you can actually experiment on, e.g., a rat brain in a way that you can't on humans.
More great stuff about the robotic embodiment of OpenWorm:
http://www.i-programmer.info/news/105-artificial-intelligenc...
https://news.ycombinator.com/item?id=8745639
Eyewire.org is also really cool -- for example, they have already made an important discovery into how our eyes detect motion: http://www.scientificamerican.com/article/online-gamers-help...
The "the next step is the human brain" people have a poor track record. Rodney Brooks tried that once. He'd done a good reactive-controller insect, and then immediately tried to jump to human-brain level with Cog.[2] When he gave a talk at Stanford proposing Cog some years ago, I asked him "Why not try for a mouse next? That might be within reach." He said "I don't want to go down in history as the man who created the world's best artificial mouse". Cog was an embarrassing failure.
The Human Brain Project should be put on hold until OpenWorm works and that technology has been advanced to at least the lizard level. The Human Brain Project is likely to turn into an expensive supercomputer boondoggle.
[1] http://www.openworm.org/ [2] http://www.ai.mit.edu/projects/humanoid-robotics-group/cog/c...
If we ever get to a mouse brain, it's just scaling from there - all the mammals have very similar DNA. We don't know how to make even a good lizard brain yet. Or even a full insect nervous system.
The Human Genome Project is cited as one such example: simultaneously not meeting the hyped goals whilst also being more successful than imagined in other ways. Perhaps what people are missing here is that the value may not be in simulating the brain but rather having the infrastructure on which to run simulations. This likely starts in parts and on a small scale but the infrastructure alone could be incredibly useful for generating new ideas and shortening feedback time.
I'm pretty sure cosmologists would love to make simulations to a molecular precision. They currently simulate gas clouds with supercomputers in order to study the formation of galaxies and the more precise the better.
I'd be very surprised if they come anywhere near reaching their goal of a comprehensive whole-brain simulation, but even if the overall project aim falls short, many of the sub-projects are likely to provide worthwhile outcomes.
For example, structural and functional data generation projects described in there (SP1, SP2 and SP3) sound reasonable and reflect the kinds of topics and techniques that are being researched already. The neuroinformatics (SP5) and medical informatics platforms (SP8), while very ambitious, seem like they could be a tremendously useful resource for linking disparate data sets together into a single, more easily-accessible database.
I can see why many neuroscientists are scoffing at the rather over-hyped grand aim of the project, but that doesn't mean the entire thing should be scrapped. Personally I think that funding comparatively open-ended, long-term, risky research is a good thing. Even if it 'fails', that failure is informative in itself in helping to provide scope for future projects. And it increases the opportunity for serendipitous discoveries.
"Would you try to understand the universe by simulating every molecule? What would you have achieved? It’s going to be just as complicated as the real thing and you won’t understand it any better."
At the very least you would have achieved the ability to rewind and play the universe forward again, which is something that we most certainly can't do with our real universe. You would also have achieved the ability to experiment with and measure the universe with potentially greater precision than we can in the physical world.
Simulation is a tool to help you understand, not understanding itself. I don't think Markram ever said anything to the contrary.
Typically we deal with it by having a waiting period of 18 years from birth (balanced by death) or a green card to citizenship process that takes a while and is limited in number.
On the other hand, would simulated humans even need much of a vote? I think their primary needs would be satisfied by simulated satisfaction. The only thing they they would need-need in the physical world would be for us humans to not pull the plug on them and to service their hardware when it fails. Much easier to deal with than real live humans. Just a few real humans might be needed to maintain entire cities of these people.
It sounds like a great sci-fi story to grant a real-world franchise to a few elected officials in the sim-city and keep the truth hidden from the sim-citizens.
How are you going to prevent abuse? I understand all innovations have risk, but I think the potential for abuse is so incredibly bad, that it's not even worth going in this direction. Assuming there's any way to stop it, but there's no sense in not trying either.
In any case, the current proposal is mostly a funding grab, you won't see an actual brain simulation come out of it. Even a nematode worm is ambitious right now. See http://www.openworm.org/
I accept that's a consequentialist argument though. If we did manage to create cheap, easy to run brain simulations, I think we're starting to run into the question of the nature of personhood and the nature of consciousness (something I hope to contribute to some day). Do AIs deserve to be free from slavery if they are not experiencing anything consciously but still have desires and creativity and can convincingly pretend that they do experience something (which a good simulation would do)? I have a feeling that regardless of the "correct" answer to that question, whatever it is, human beings' emotions are very hackable and we'll grant them rights based on emotional appeals if they get to that point. ;)
That would open an interesting technical issue though. If a human simulation was cheap and easy to run, and we wanted to legislate the conditions under which it was run (e.g. must be given reasonable sensory inputs and have hunger, sexual desire, and other wants set to near 0), we'll run into the DRM / TPM debate again, but this time with serious "life or death" consequences.
But if it is, imagine what happens when, in 50 years, it becomes cheap to reproduce. Any bozo can get a copy of it. The potential for mistreatment is beyond anything seen yet on Earth, as bad as that's been so far. Check out the Christmas special of Black Mirror, for just one example.
Well, you don't want that in science. The point of startups (from VC's point of view) is to make money for VC - startups themselves and their survival is irrelevant. The point of research is to gain knowledge and not to make money. That's two completely different kinds of thinking.
Hahahahahahaha.
Joke aside, I haven't seen "science just for the sake of science" ever.
You could then argue that it's not as efficient as the VC model, but:
- I haven't seen any study confirming such thing
- It doesn't seem easy to achieve a reasonable compromise between open research and R&D protectionism unless you are a big company
- Academia, even with all its politics and problems, is still a fast and active environment for scientific research
EDIT: typesetting