Viena no mūsu jaunatnes lielākajām problēmām ir derīgas informācijas iegūšana. Informācija - šis vārds mūsu sabiedrībā kļuvis populārs un tiek lietots vietā un nevietā. Sakaru nozarē par informāciju sauc derīgo signālu, bet nederīgo, traucējošo - par troksni. Informācijas atdalīšanu no trokšņa sauc par filtrēšanu.

Ja pirms 100 gadiem jaunā zinātkārā cilvēka vienīgā literatūra bija Lauksaimnieka kalendārs, žurnāls 'Atpūta', Kurts-Māleres romāni vai Bībele, par šodienu mēdz teikt, ka mēs dzīvojam informācijas pārbagātības laikmetā. Augstāk dotās definīcijas skatījumā tas tā nav: mēs dzīvojam trokšņa laikmetā. Signālu ir daudz, derīgas informācijas - aptuveni desmit reizes mazāk, un to grūti atrast.

"90% from published in science is trash". Stephen Hawking, 'The Universe in a Nutshell'. (šī grāmata tulkota arī latviešu valodā - 'Universs rieksta čaumalā').

Daudzu jauno un zinātkāro cilvēku problēma šodien ir - kā atrast derīgas informācijas avotus. Šī interneta lapa dod lasītājam norādes, kur atrast materiālus, kurus lieto cilvēki, kas uzbūvēja datorus, mobilos tālruņus un kosmosa kuģus, izveidoja internetu un šodien strādā pie citām problēmām. Visa pamatā ir  viena prasība - mēs lietojam tikai to, ko var neierobežoti daudzas reizes pārbaudīt un pierādīt. Angliski to saka - everything based on scientific evidence.

Noam Chomsky on Where Artificial Intelligence Went Wrong

Nov 1 2012, 2:22 PM ET 51

An extended conversation with the legendary linguist,

http://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/?single_page=true

If one were to rank a list of civilization's greatest and most elusive intellectual challenges, the problem of "decoding" ourselves -- understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome -- would surely be at the top. Yet the diverse fields that took on this challenge, from philosophy and psychology to computer science and neuroscience, have been fraught with disagreement about the right approach.

In 1956, the computer scientist John McCarthy coined the term "Artificial Intelligence" (AI) to describe the study of intelligence by implementing its essential features on a computer. Instantiating an intelligent system using man-made hardware, rather than our own "biological hardware" of cells and tissues, would show ultimate understanding, and have obvious practical applications in the creation of intelligent devices or even robots.

Some of McCarthy's colleagues in neighboring departments, however, were more interested in how intelligence is implemented in humans (and other animals) first. Noam Chomsky and others worked on what became cognitive science, a field aimed at uncovering the mental representations and rules that underlie our perceptual and cognitive abilities. Chomsky and his colleagues had to overthrow the then-dominant paradigm of behaviorism, championed by Harvard psychologist B.F. Skinner, where animal behavior was reduced to a simple set of associations between an action and its subsequent reward or punishment. The undoing of Skinner's grip on psychology is commonly marked by Chomsky's 1967 critical review of Skinner's book Verbal Behavior, a book in which Skinner attempted to explain linguistic ability using behaviorist principles.

Skinner's approach stressed the historical associations between a stimulus and the animal's response -- an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past. Chomsky's conception of language, on the other hand, stressed the complexity of internal representations, encoded in the genome, and their maturation in light of the right data into a sophisticated computational system, one that cannot be usefully broken down into a set of associations. Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment. The "language faculty," as Chomsky referred to it, was part of the organism's genetic endowment, much like the visual system, the immune system and the circulatory system, and we ought to approach it just as we approach these other more down-to-earth biological systems.

The approach taken by Chomsky and Marr toward understanding how our minds achieve what they do is as different as can be from behaviorism. The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.

Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.

Chomsky acknowledged that the statistical approach might have practical value, just as in the example of a useful search engine, and is enabled by the advent of fast computers capable of processing massive data. But as far as a science goes, Chomsky would argue it is inadequate, or more harshly, kind of shallow. We wouldn't have taught the computer much about what the phrase "physicist Sir Isaac Newton" really means, even if we can build a search engine that returns sensible hits to users who type the phrase in.

The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else. If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.

...neuroscience for the last couple hundred years has been on the wrong track. ... neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.

So if you want to study, say, the neurology of an ant, you ask what does the ant do? It turns out the ants do pretty complicated things, like path integration, for example. If you look at bees, bee navigation involves quite complicated computations, involving position of the sun, and so on and so forth. But in general ... if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working.

So when you're studying vision, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything. There are many examples of this, even in the hard sciences, but certainly in the soft sciences. People tend to study what you know how to study, I mean that makes sense. You have certain experimental techniques, you have certain level of understanding, you try to push the envelope -- which is okay, I mean, it's not a criticism, but people do what you can do. On the other hand, it's worth thinking whether you're aiming in the right direction.

Well, there are much simpler questions. Like here at MIT, there's been an interdisciplinary program on the nematode C. elegans for decades, and as far as I understand, even with this miniscule animal, where you know the wiring diagram, I think there's 800 neurons or something ...Still, you can't predict what the thing [C. elegans nematode] is going to do. Maybe because you're looking in the wrong place.

So for example, take an extreme case, suppose that somebody says he wants to eliminate the physics department and do it the right way. The "right" way is to take endless numbers of videotapes of what's happening outside the video, and feed them into the biggest and fastest computer, gigabytes of data, and do complex statistical analysis -- you know, Bayesian this and that [Editor's note: A modern approach to analysis of data which makes heavy use of probability theory.] -- and you'll get some kind of prediction about what's gonna happen outside the window next. In fact, you get a much better prediction than the physics department will ever give. Well, if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

And that's done all over the place. Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is. In my own field, language fields, it's all over the place. Like computational cognitive science applied to language, the concept of success that's used is virtually always this. So if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

A very different approach, which I think is the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach. These are just two different concepts of science. The second one is what science has been since Galileo, that's modern science. The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability...

...But away from understanding. Yeah, maybe some effective engineering. And it's kind of interesting to see what happened to engineering. So like when I got to MIT, it was 1950s, this was an engineering school. There was a very good math department, physics department, but they were service departments. They were teaching the engineers tricks they could use. The electrical engineering department, you learned how to build a circuit. Well if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine. So in the past century, again for the first time, biology had something serious to tell to the practice of medicine, so you had to understand biology if you want to be a doctor, and technologies again will change. Well, I think that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.

Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.

But that's, sure, that's the way science works. Same with chemistry. Chemistry, until my childhood, not that long ago, was regarded as a calculating device. Because you couldn't reduce to physics. So it's just some way of calculating the result of experiments. The Bohr atom was treated that way. It's the way of calculating the results of experiments but it can't be real science, because you can't reduce it to physics, which incidentally turned out to be true, you couldn't reduce it to physics because physics was wrong. When quantum physics came along, you could unify it with virtually unchanged chemistry. So the project of reduction was just the wrong project. The right project was to see how these two ways of looking at the world could be unified. And it turned out to be a surprise -- they were unified by radically changing the underlying science. That could very well be the case with say, psychology and neuroscience. I mean, neuroscience is nowhere near as advanced as physics was a century ago.

But that's a fundamentally different activity from me adding up small numbers in my head, which surely does have some kind of algorithm.

Chomsky: Not necessarily. There's an algorithm for the process in both cases. But there's no algorithm for the system itself, it's kind of a category mistake.  The internal system that you have -- for that, the question of process doesn't arise. But for your using that internal system, it arises, and you may carry out multiplications all kinds of ways. Like maybe when you add 7 and 6, let's say, one algorithm is to say "I'll see how much it takes to get to 10" -- it takes 3, and now I've got 4 left, so I gotta go from 10 and add 4, I get 14. That's an algorithm for adding -- it's actually one I was taught in kindergarten. That's one way to add.

But there are other ways to add -- there's no kind of right algorithm. These are algorithms for carrying out the process the cognitive system that's in your head. And for that system, you don't ask about algorithms. You can ask about the computational level, you can ask about the mechanism level. But the algorithm level doesn't exist for that system. It's the same with language. Language is kind of like the arithmetical capacity. There's some system in there that determines the sound and meaning of an infinite array of possible sentences. But there's no question about what the algorithm is. Like there's no question about what a formal system of arithmetic tells you about proving theorems.

If you ask, what is the computational problem that the brain is solving, we have kind of an answer, it's sort of like a computer. But if you ask, what is the computational problem that's being solved by the lung, that's very difficult to even think -- it's not obviously an information-processing kind of problem.

Chomsky: No, but there's no reason to assume that all of biology is computational. There may be reasons to assume that cognition is. And in fact Gallistel is not saying that everything is in the body ought to be studied by finding read/write/address units.

So what would you think would be an adequate theory that is explanatory, rather than just predicting data, the statistical way, what would be an adequate theory of these systems that are not computing systems -- can we even understand them?

Chomsky: Sure. You can understand a lot about say, what makes an embryo turn into a chicken rather than a mouse, let's say. It's a very intricate system, involves all kinds of chemical interactions, all sorts of other things. Even the nematode, it's by no means obviously -- in fact there are reports from the study here -- that it's all just a matter of a neural net. You have to look into complex chemical interactions that take place in the brain, in the nervous system. You have to look into each system on its own. These chemical interactions might not be related to how your arithmetical capacity works -- probably aren't. But they might very well be related to whether you decide to raise your arm or lower it.

Galvenā doma: lai mašīnā izveidotu inteliģenci, nepietiks, ka iemācisim to izveidot ievadītajiem datiem, dotajai situācijai piemērotu, atbilstošu rīcību. Vēl vajadzēs, līdzīgi kā to dara mūsu smadzenes, saprast. Tas nozīmē izveidot mašīnā tūkstošiem ārējās pasaules situācijām atbilstošus modeļus, kuri tiks lietoti neapzināti, bet lielākajai daļai no kuriem apziņa var piekļūt un aprakstīt to darbību kādā citiem saprotamā valodā.

Ja mašīna būs iemācīta, piemēram, saskaitīt divus skaitļus, tad pēc neilga laika tā dos pareizas atbildes jebkuriem diviem iepriekšējā pieredzē esošiem skaitļiem. Bet, ja tajā nebūs izveidots universāls saskaitīšanas algoritms (modelis), tā nevarēs saskaitīt tādus divus skaitļus, kuri apmācības laikā nebija tās ieejā.

Līdzīgi ir ar valodu. Mašīnas pilnīgi valodu iemācisies tikai tad, kad katram vārdam tās atmiņā būs piesaistīts atbilstošs jēdziens, kas saturēs mašīnas-robota līdzšinējās dzīves vizuālo, dzirdes, taustes, ķermeņa sajūtu, kustību un emociju pieredzi.  I.V.

Raksts par to, kā katram vārdam apziņā atbilst jedziens, kas satur visu indivīda līdzšinējo dzīves pieredzi:

'Embodied Cognition: Our Inner Imaginings of the World around Us Make Us Who We Are' 

http://www.scientificamerican.com/article.cfm?id=embodied-cognition-our-inner-imaginings

 no  grāmatas  "Louder Than Words: The New Science of How the Mind Makes Meaning" by Benjamin K. Bergen (Basic Books, 2012)