Ever heard of the "AI Day"? Me neither, but

That's quite true, and that qualifies my reply, too.

I responded under the assumption that variations in runtime (due to variations in rated clock rate, cache size, etc.) were assumed to be differences that weren't counted as differences in result.

Just to illustrate the assumptions under which I framed my response, by taking those assumptions to a hypothetical extreme, it's not fair to penalize a single 80386, with 4GiB of RAM connected to a humongous disk executing some AI algorithm, for the long runtime it takes to calculate a result, if the objective for such a system is to calculate only a single intelligent (or perhaps "intelligent") response according to some theoretical criteria (e.g., compute a theoretically optimal turn in a game of Civilization, no matter how long it takes to figure that out).

If the objective is to generate a timely response in realtime under dynamic conditions (that meets certain criteria of effectiveness), your hardware would be radically different than that, and you wouldn't be speaking just of calculations, as if that was all that mattered.

 

Sorry guys, I was so busy that I didn't go online in a while. Hence I try to reply to several posts all at once:

I start to get what you mean. I haven't encountered such a programming yet, though, and can't quite imagine what it would look like. Could you give me an example of such a code or is it too complicated and too long for a post?

A transistor is en excellent image of how synapses and motorical nerves work! The ones we need for thinking appear to behave differently and respond in finer gradients so that they might not work transistor-like, but afaik that's still being researched. Since that branch of research involves experiments on living brains and I am very strongly opposed to vivisection, it's something I am not really up-to-date with.


The needle prick example for different responses is perhaps not the best choice because such responses are reflexes that don't get controlled by the brain but by a shortcut in the spine. The signal rund along the nerve to your spinal cord, is marked there as "urgent! Injury! Might be life-threatening! Quick response needed!" and then a "withdraw immediately" signal is sent right back by a kind of feedback nerve. Hence the name re-flex: backwards-bend.

In this case, the reaction depends on the distance (faster in small people, because there's less of a way to cover), the age (older people's nerves react slower), the nourishment (as it's a chemical signal, there must be enough chemical to run it), muscle strength (a strong muscle retracts further) and also an individual pain threshold (some people bear more pain and react later than others). Also, if you repeat the experiment, the pain threshold will rise as the nerves need time to recover and pump the Ca++ back.

I don't think Stochastic Resonance has something to do with the better results in combination with a second signal (e.g. a vibration or music). I rather think it's a kind of Pavlov Effect: the specimen learns that the vibration or music means it'll get hurt any moment and begins to react a little sooner than without the combination. Very often, if we anticipate something, we react before the incidence actually occurs.

reaction time is indeed vital (most literally), but I nevertheless wouldn't consider it the result of the nerve's actions. The result -and point- of the whole action is to get your fingers out of the danger zone and prevent your body from potentially serious damage.

 

It's interesting that the point about how the need for timely responses places strong demands on hardware configuration is quite relevant with respect to the reflex arc. Thanks for that, Rhubarbodendron.

 

This is a pretty good source on what I am talking about. I've read some books on fuzzy control systems--it's a topic I've been fascinated with for years, and I think it's quite interesting. Essentially, it's about describing a problem in terms of states and responses, generally with the goal that the system needs to achieve a specific state, and is given considerable flexibility in doing so.

 

Talk about computing a bunch of variables, this is fucking amazing IMO

This Week

 

^interesting! And, frankly, rather scary. 1984-ish.
On the other hand, Google and the NSA are doing basically the same. You can find out a lot about people by simply following their traces on the web. In my country, we see that as a violation of personal privacy (like stalking) and the government tries to counteract that. For example, they advise not to use facebook and google but alternatives with better data security. Our ministry for data security recommends the use of proxies, mail & cell encryption and disposable email adresses (I use different mail accounts: a disposable one for first contacts which mighth be potentially risky, another account for reliable business partners, again another for business partners where I am not sure of their security, one very secure one for friends and a top secret heavily encrypted one only for closest friends and family). Sounds like an overkill but I'm rather safe than sorry and spied out.

Thanks for the link, Robert. Paradoxely, the explanation on the English page is easier to understand for me than that on the German one LOL, but the latter has better graphics and less abstract examples. Both lack an example for how the coding would look like, though. They only explain the general idea. I'll look for open source fuzzy logic programmes in the weekend to take one apart and have a look at the code

You're welcome It's something that stuck in my memory from way back at school, 40 years ago.
It's basically only a variation of a very old nervous system that insects and many worms have. They have 2 parallel nerve strands that are connected at regular intervals. At the crossings there are ganglia: knots of nervous tissue that work as little brains for the reflexes. In our spinal cord the 2 nerve strands are fused to one and these little "brains" are reduced to just a few cells, but the principle is quite the same. In an insect the whole thing looks rather like a rope ladder. Here's a pic of 3 Drosophila (fruitfly) larvae. The nervous tuissue is coloured. The left larva is normal, with the ganglions nicely visible (the dark zones where the rungs meet the stringers). The 6 front ganglia are especially big: they control legs, wings, mandibles, feelers and eyes. The other two larvae have different grades of genetically induced nerval damage.