The New AI: Where Neuroscience and Artificial Intelligence Meet

An anonymous reader writes "We're seeing a new revolution in artificial intelligence known as deep learning: algorithms modeled after the brain have made amazing strides and have been consistently winning both industrial and academic data competitions with minimal effort. 'Basically, it involves building neural networks — networks that mimic the behavior of the human brain. Much like the brain, these multi-layered computer networks can gather information and react to it. They can build up an understanding of what objects look or sound like. In an effort to recreate human vision, for example, you might build a basic layer of artificial neurons that can detect simple things like the edges of a particular shape. The next layer could then piece together these edges to identify the larger shape, and then the shapes could be strung together to understand an object. The key here is that the software does all this on its own — a big advantage over older AI models, which required engineers to massage the visual or auditory data so that it could be digested by the machine-learning algorithm.' Are we ready to blur the line between hardware and wetware?"

Comments

no

[Anonymous Coward]

Are we ready to blur the line between hardware and wetware?

No. You can't ask that every time you find a slightly better algorithm. Ask it when you think you understand how the mind works.

Saving everyone a few seconds on wiki

[Dorianny]

Drosophila melanogaster is commonly known as the fruit fly. Its brain has about 100,000 neurons. The human brain avarages 85,000,000,000.

Yes--But the Trend is Toward Biological Realism

[Slicker]

Neural Net's were traditionally based off old Hodgkins and Huxley models and then twisted for direct application for specific objectives, such as stock market prediction. In the process they veered from a only very vague notion of real neurons to something increasingly fictitious.

Hopefully, the AI world is on the edge of moving away from continuously beating their heads against the same brick walls in the same ways while giving themselves pats on the heads. Hopefully, we realize that human-like intelligence is not a logic engine and that conventional neural nets are not biologically valid and posses numerous fundamental flaws.

Rather--a neurons draws new correlating axons to itself when it cannot reach threshold (-55mv from a resting state of -70mv) and weakens and destroys them when over threshold. In living systems, neural potential is almost always very close to threshold--it bounces a tiny bit over and under. Furthermore, inhibitory connections are also drawn in from non-correlating axons. For example, if two neural pathways always excite when the other does not, then each will come to inhibit the other. This enables contexts to shut off irrelevant possible perceptions, e.g. If you are in the house, you are not going to get rained on. More likely, somebody is squirting you with a squirt gun.

Also--a neuron perpetually excited for too long shuts itself off for a while. We love a good song but hearing it too often makes us sick of it, at least for a while.. like Michael Jackson in the late 1980's.

And very importantly--signal streams that dissappear but recur after increasing time lapses stay potentiated longer.. their potentiation dissipates slower. After 5 pulses with a pause between a new receptor is brought in from the same axon as an existing one. This causes slower dissipation. It will happen again after another 5 pulses repeatedly, except that the time lapse between them must be increased. It falls in line with the scale found on the Wikipedia page for Graduated Interval Recall--exponentially increasing time lapses 5 times, each... take a look at it. Do the math. It matches what is seen in biology, even though this scale was developed in the 1920's.

I have a C++ neural modal that does this. I am mostly done also with a Javascript modal (employing techniques for vastly better performance), using Nodejs.

Re:Yes--But the Trend is Toward Biological Realism

[wierd_w]

I could give a number of clearly unsubstantiated, but seemingly reasonable answers here.

1) the assertion that because living neurons have deficits compared against an arbitrary and artificial standard of efficiency (it takes a whole 500ms for a neuron to cycle?! My diamond based crystal oscillator can drive 3 orders of magnitude faster!, et al.)that they are "faulted" is not substantiated: as pointed out earlier in the thread, no high level intelligence built using said "superior" crystal oscillators exists. Thus the "superior" offering is actually the inferior offering when researching an emergent phenomenon.

2) artificially excluding these principles (signal crosstalk, propogation delays, potentiation thresholds of organic systems, et al) completely *IGNORES* scientifically verified features of complex cognitative behaviors, like the role of mylein, and the mechanisms behind dentrite migration/culling.

In other words, asserting something foolish like "organic neurons are bulky, slow, and have a host of computationally costly habbits" wit the intent that "this makes them undesirable as a model for emergent high level intelligence" ignores a lot of verified information in biology, that shows that these "bad" behaviors directly contribute to intelligent behaviors.

Did you know that signal DELAY is essential in organic brains? That whole hosts of disorders with debilitating effects come from signals arriving too early? Did you stop to consider that thse faults may actually be features that are essential?

If you don't accurately model the biological reference sample, how can you riggorously identify which is which?

We have a sample implementation, with features we find dubious. Only buy building a faithful simulation that works, then experimentally removing the modeled faults do we really systematically break down the real requirements for self directed intelligences.

That is why modeling accurate neurons that faithfully smulate organic behavior is called for, and desirable. At least for now.

Re:fly brains

[AthanasiusKircher]

I say all of the following as a big fan of AI research. I just think we need to drop the rhetoric that we're somehow recreating brains -- why do we feel the need to claim that intelligent machines would need to be similar to or work like real brains?

Anyhow...

We can now almost convincingly partially recreate the wetware functions of Drosophila melanogaster.

Interesting wording. Let's take this apart:

• now: the present
• almost convincingly: not really "convincingly" then, right? since "convincingly" isn't really a partial thing -- evidence is usually enough to "convince" you or not, if I say study data "almost convinced me," I usually mean it had argument and fluff that made it appear to be good but it turned out to be crap in the end
• partially recreate: yeah, it's pretty "partial," and you have to read "recreate" as something more like "make a very inexact blackbox model that probably doesn't work at all the same but maybe outputs a few things in a similar fashion"
• functions: this word is chosen wisely, since the "neural net" models are really just algorithms, i.e., functions, which probably don't act anything like real "neurons" in the real world at all

In sum, we have a few algorithms that seem to take input and produce some usable output in a manner very vaguely like a few things that we've observed in the brains of fruit flies. Claiming that this at all "recreates" the "wetware" implies that we understand a lot more about brain function and that our algorithms ("artificial neurons"? hardly) are a lot more advanced and subtle than they are.