Scientists to Build 'Brain Box'

lee1 writes "Researchers at the University of Manchester are constructing a 'brain box' using large numbers of microprocessors to model the way networks of neurons interact. They hope to learn how to engineer fail-safe electronics. Professor Steve Furber, of the university school of computer science, hopes that biology will teach them how to build computer systems. He said: 'Our brains keep working despite frequent failures of their component neurons, and this "fault-tolerant" characteristic is of great interest to engineers who wish to make computers more reliable. [...] Our aim is to use the computer to understand better how the brain works [...] and to see if biology can help us see how to build computer systems that continue functioning despite component failures.'"

Comments

Two Separate Goals

[Anonymous Coward]

Continuing to function is one thing, but continuing to produce correct answers with high reliability is another. And under stress, I'd say biological brains aren't particularly good at any of this.

Hardware?

[CosmeticLobotamy]

I don't mean to be one of those people that craps on a chunk of science without knowing exactly what's going on, but I would think there would be some large advantages to building the research version in software. There's less soldering when you realize it's not quite right.

They'll find out when they stop using Windows

[guruevi]

I don't know what level of redundancy they want, but if they have to build a brain box to figure that out:

There are a bunch of tools and specs out to get a fully (multiple) redundant system. You can have >1 server in any type of configuration, sharing any type of resource and when one fails, the other takes over, fully redundant.

it took long enough

[sepharious]

who else besides me thinks this one should have been obvious from the getgo? it makes no sense to try and build a single processor that could function similarly to a brain. by utilizing mulitple processors you also have the option to design different types of processors to work together similar to the various types of neurons found in biological systems. this will hopefully be a huge step forward in developing possible AI systems.

Downside of biological computing

[QuantumFTL]

While I was an intern at the Jet Propulsion Laboratory, back when I was an undergraduate, I was very gung-ho about biologically inspired computing - I implemented an automatic flowchart positioning system using a genetic algorithm that would "evolve" a correct solution to the problem. While this certainly worked to some extent, the instability and sheer unpredictable nature of using such a stochastic algorithm made it impossible to use in a mission-critical setting. Many biologically inspired algorithms solve problems through methods that cannot be proven correct (unlike, say, the mathematics circuitry in a CPU), but merely empirically observed to "do a good job."

One of the main drawbacks of human engineering is the need for certainty, which often prohibits the use of many high-efficiency stochastic algorithms (especially for things like mesh communication) in conservative industries, like the US defense industry. This is also a significant problem in other areas, however, and many biologically inspired algorithms have properties that we cannot, so far, completely explain - they are treated like "black boxes" with many unknowns for engineering purposes.

I think that in certain circles, the tremendous success that is evolution on this planet has overshadowed its enherent weaknesses - that it is a greedy, local optimizer which cannot reach a large amount of the possible biological search space due to being stuck in local optima, and the added constraint that everything must be constructed out of self-replicating units (these two factors are why something useful, like, say, a Colt 45, will never emerge without the pre-existence of an intelligence). Biological examples are fascinating and often practical, but the biological approach is almost always "brute force" and/or "sub-optimal but still alive."

I think biologically-inspired algorithms will continue to gain prominence, but in my estimation, it is likely that there will be harsh limits imposed on how far guarantees of performance from empirical tests and symbolic analysis will actually hold.

Researcher is outside his field of expertise

[giafly]

Our brains keep working despite frequent failures of their component neurons

Can you remember everything you did ten years ago today? No. In fact you probably don't remember anything about that day. Are you as intelligent? Again probably no. And the cliche that you never forget how to ride a bike? Also not true. I went thirty years without riding a bike and found I had completely forgotten how to (it took 3 months to relearn and get good). So conditioned reflexes don't keep working either.

We just accept that many (most?) brain functions don't "keep working", fortunately without worrying about it too much.