“In 2008, the National Academy of Engineering chose as one of its grand challenges to reverse-engineer the human brain. When will this happen? Some are predicting that the first wave of results will arrive within the decade, propelled by rapid advances in both brain science and computer science. This sounds astonishing, but it’s becoming increasingly plausible. So plausible, in fact, that the great race to reverse-engineer the brain is already triggering a dispute over historic firsts. The backdrop for the debate is one of dramatic progress.”
You can find the whole article here. For those who don’t know, Sejnowski is one of the most respected neuroscientists in the world.
Last words from the article:
Intelligence will inherit the earth.
Intelligence isn’t always evolutionarily adaptive.
And what happened to Socrates, Protagoras, Aesop?
Why the heck are they studying rat and cat-size brains? Sounds like bad engineering to me. Entirely too ambitious. The path of engineering is to start small, and build incrementally. Have they mastered the nematode and I missed it?
Basically, my question is this: In what way does simulating a cat-size brain improve the task of understanding the brain’s function, that a half-cat-size brain does not?
Scale seems like the easy path, not the relevant one.
@Aron
People are attacking the problem on all scales, from detailed molecular models up to very abstract mathematical models that have no neurons at all. The work of Markram and Modha is a tiny speck on the mountain of neuroscience being done. They do however get much of the media coverage due to making the most noise.
(To put some numbers on this: there are around 60,000 neuroscience researchers in the world, of which about 25 are working on Markram’s Blue Brain project. I believe that Modha’s team is significantly smaller.)
As for the right way to go about building an intelligent system using ideas from the brain, that’s a complex discussion. In my view the most likely path to success is more abstract that what Modha and Markram are doing, using methods in machine learning that are similar to what the brain seems to be doing, but less abstract than what people like Goertzel are doing.
From the article:
“When physicists puzzle out the workings of some new part of nature, that knowledge can be used to build devices that do amazing things — airplanes that fly, radios that reach millions of listeners. When we come to understand how brains function, we should become able to build amazing devices with cognitive abilities — such as cognitive cars that are better at driving than we are because they communicate with other cars and share knowledge on road conditions.”
Why does this supposedly smart person not understand the implications of this research? Is it really that hard to come up with the idea of intelligence as being more than just another trick, but rather as the master trick, the trick that makes all the other tricks work? Perhaps.
@Roko:
How do you know that he doesn’t?
Well, it is possible that he gives a deliberately misleading example here, kind of like saying
“if we develop an understanding of nuclear chain reactions, we’ll be able to make glow-in-the-dark watches 20% brighter!”
(possibly) true, but misleading because it omits the much more relevant fact that we’ll have nuclear weapons.
Do you think that this is the case? Have you spoken to him?
@Roko:
I haven’t spoken to him and so I don’t presume to know what he really thinks. One thing I do know is that many academics have thoughts on such matters that they do not make public. For example, I know of highly successful academics having their job applications vetoed when it was discovered that they were merely interested in AGI.
I know of highly successful academics having their job applications vetoed when it was discovered that they were merely interested in AGI.
Quite interesting.
Did they happen to know the rationale for the veto?