U.S. Plan For "Thinking Machines" Repository

An anonymous reader writes "Information scientists organized by the US's NIST say they will create a "concept bank" that programmers can use to build thinking machines that reason about complex problems at the frontiers of knowledge — from advanced manufacturing to biomedicine. The agreement by ontologists — experts in word meanings and in using appropriate words to build actionable machine commands — outlines the critical functions of the Open Ontology Repository (OOR). More on the summit that produced the agreement here."

Comments

Awesome

[geekoid]

If computer history tells us anything, they will create more data then we can understand in a short amount of time.

Re:What is this "thinking"?

[mrbluze]

Now most people would argue that a fly does not think, but it is clearly able to perform some sort of precessing.

Not wanting to labour the point too much, but...

It's no different to a script that moves a clickable picture away from the mouse cursor once it approaches a critical distance such that you can never click on the picture (unless you're faster than the script).

A fly's compound eye is a highly sensitive movement sensor and the fly will move at anything big that moves, but if you don't move the fly doesn't see you (its brain wouldn't cope with that much information).

Flies can learn a limited amount but it's limited and I would argue a computer could well behave as a fly and perform a fly's functions. But is the fly thinking? I don't think the fly is consciously deciding anything except that repeated stimuli that 'scare' it result in temporary sensitization to any other movement.

Bacteria show similar memory behaviour but I wouldn't go so far as to call it 'thought'.

Intelligence vs. Appropriate Formal Logic

[TRAyres]

Lots of people are making posts about this vs. skynet, terminator, etc. But there are some problems with that (overly simplistic and totally misguided) comment.

There are numerous formal logic solvers, that are able to come to either the correct answer (in the case of deterministic systems, for instance) or to the answer with the highest degree of success. The difference between the two should be made clear: Say if I give the computer that:

A)All Italians are human. B)All humans are lightbulbs.

What is the logical conclusion? The answer is that all Italians are lightbulbs. Of course, the premises of such an argument are false, but a computer could work out the formally correct conclusion.

The problem these people seem to be solving is that there needs to be a unified way to input such propositions, and a properly robust and advanced solver that is generic and agreed upon. Basically this is EXACTLY what is needed in order to move beyond a research stage, where each lab uses its own pet language.

I mentioned determinism, because the example I gave contained the solution in the premises. What if I said, "My chest hurts. What is the most likely cause of my pain?" An expert system (http://en.wikipedia.org/wiki/Expert_system) can take a probability function and return that the most likely cause is... (whatever, I'm not a doctor!). But what if I had multiple systems? The logic becomes more fuzzy! So there needs to be an efficient way to implement it, AND draw worthwhile conclusions. Such conclusions can be wrong, but they are the best guess (the difference between omniscient and rational, or bounded rational).

None of these things are relating to some kind of 'skynet' intelligence.

IF you DID want to get skynet like intelligence, having a useful logic system (like what is planned here) would be the first step, and would allow you to do things like planning, for instance. If I told a robot, "Careful about crossing the street." it would be too costly to try to train it to replicate human thought exactly. But it records and understands language well (at this point), so what can we extract from that language?

Essentially, this is from the school of thought that we need to play to computer's strengths when thinking about designing human like intelligence, rather than replicating the human thought processes from the ground up (which will happen eventually, either through artificial neurons, or through simulation of increasingly large batches of neurons). On the other hand, if such simulations lead to the conclusion that human level consciousness requires more than the model we have, it will lead to a revolution in neuroscience, because we will require a more complex model.

I really can't wait to get more into this, and really hope it isn't just bluster.

Also:

'Thinking Machines' title is inflammatory and incorrect, if we use the traditional human as the gauge for the term 'thought'. It is a highly formalized and rigorous machine interpretation of human thought that is taking place, and it will not breed human level intelligence.

Tagging your links doesn't make you an ontologist

[idlemachine]

I'm really over this current misuse of "ontology", which is "the branch of metaphysics that addresses the nature or essential characteristics of being and of things that exist; the study of being qua being". Even if you accept the more recent usage of "a structure of concepts or entities within a domain, organized by relationships; a system model" (which I don't), there's still a lot more involved than knowing "appropriate words to build actionable machine commands".

Putting tags on your del.icio.us links doesn't make you an ontologist any more than using object oriented methodologies makes you a platonist. I think the correct label for those who misappropriate terminology from other domains (for no other seeming reason than to make them sound clever) is "wanker". Hell, call yourselves "wankologists" for all I care, just don't steal from other domains because "tagger" sounds so lame.