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"Revival of AI" - no more than expensive iron and advertising, thrown on the implementation of the old idea

There is no spirit in the car

3r3143.  3r3151. "Revival of AI" - no more than expensive iron and advertising, thrown on the implementation of the old idea " Yuval Noi Harari
describes the influence of AI on democracy. The most interesting aspect of the article is Harari’s excessive belief in the capabilities of modern AI technologies. He describes Google's companion, a chess program from DeepMind 3r3136. as “creative”, “possessing imagination” and “genius instincts”. 3r3143.  3r3151.
3r3143.  3r3151. In the documentary Air Force "Joy AI" Professor Jim Al-Khalili and the founder of DeepMind Demis Hassabis describe how the AI ​​system "made a real discovery," "is able to really generate a new idea" and developed "strategies designed independently." 3r3143.  3r3151. 3r3143.  3r3151. And if such a stream of exaggerations and anthropomorphisms is used to describe blunt and mechanistic systems, then it is time to test reality with a return to basics. 3r3143.  3r3151. 3r3143.  3r3151. Discussion of computer technology often occurs through myths, metaphors and human interpretations of what appears on the screen. Metaphors of the “intuition”, “creative beginning” and newer “strategies” are part of the emerging mythology. Experts on AI find regularities in the game moves of AI and call them "strategies", but the neural network has no idea what a strategy is. If there is any creative beginning, it belongs to the researchers from DeepMind, developing and managing the processes of training systems. 3r3143.  3r3151. 3r3143.  3r3151. Today's AI systems are trained on the basis of a huge amount of automated trial and error; at each stage, the technology is used to transmit information about errors and adjust the system in order to reduce the number of errors in the future. back propagation 3r3136. - and this gradually improves the effectiveness of AI in performing a certain task, such as playing chess. 3r3143.  3r3151. 3r3143.  3r3151. The ongoing increase in the effectiveness of AI, MO and so-called. “Depth learning” (GO) is mostly based on the use of this back-propagation technique. It was first invented in the 1960s, and applied to neural networks in the 1980s by Joffrey Hinton. In other words, for more than 30 years there has been no significant conceptual progress in the field of AI. Most of the results of research in the field of AI and articles in the media show what happens when the execution of the old idea is thrown into mountains of expensive computing equipment and ingenious advertising campaign. 3r3143.  3r3151. 3r3143.  3r3151. And it cannot be said that DeepMind does not do valuable work. Auxiliary work of machines when creating new strategies and ideas is an interesting thing, especially if the work of this machine is difficult to understand because of its complexity. In our secular culture, the magic and mystery of technology attracts people, and giving a mysterious image of a mostly dry and rational engineering field is only good. But in the car, a friend of Google, there is no spirit. 3r3143.  3r3151. 3r3143.  3r3151.

Iron versus software, analog versus digital, Thompson versus Hassabis

3r3143.  3r3151. 3r3365. All the hype around the DeepMind machines reminds me of the joyful excitement that arose a couple of decades ago on a completely different wave, and, perhaps, of a deeper machine learning system. 3r3143.  3r3151. 3r3143.  3r3151. In November 199? 3–3–370. work
Adrian Thompson, a researcher at the Center for Computational Neuroscience and Robotics at the University of Sussex, was put on the cover of New Scientist magazine along with 3r372. Article 3r3136. “Creations from prehistoric silicon - we release Darwinism in the electronics laboratory and see what it creates. A tough car that nobody understands. ” 3r3143.  3r3151. 3r3143.  3r3151. The work of Thompson caused a slight sensation, since he rejected the customs and launched the evolution of the MO system in electronic equipment, instead of using the program approach, like everything else. He decided to do this because he realized that digital software is limited by the binary on /off nature of the switches that make up the signals processing brain of any digital computer. 3r3143.  3r3151. 3r3143.  3r3151. The neurons of the human brain, on the contrary, have evolved so as to participate in various subtle, sometimes inconceivably complex physical and biochemical r3r382. processes
. Thompson suggested that the development of computing equipment using the automatic process of natural selection can take advantage of all the analog (infinitely diverse) physical properties of the real world, inherent in silicon, of which the simplest digital switches of computers are made - which may lead to something resembling effective analog work of the components of the human brain. And he was right. 3r3143.  3r3151. 3r3143.  3r3151. In his laboratory, Thompson conducted an evolution of the FPGA configuration (such as a digital silicon chip, in which the connections between its digital switches can be constantly reconfigured) in order to teach him to separate two different audio signals. Looking after the inside of the chip to see how the evolutionary process set up the connections between the switches, he noted an impressively effective scheme of work - she used only 37 components. 3r3143.  3r3151. 3r3143.  3r3151. In addition, the resulting evolution scheme has ceased to be understandable to digital engineers. Some of the 37 components were not related to others, but when they were removed from the circuit, the entire system stopped working. The only reasonable explanation for this strange situation was that the system used some mysterious electromagnetic connections between its kind of digital components. In other words, the evolutionary process has adopted the analog characteristics of the components and materials of the system from the real world in order to carry out its “calculations”. 3r3143.  3r3151. 3r3143.  3r3151. It was a brain explosion. I was a young researcher in the 1990s, I had work experience like in the field of research 3r-396. electronic equipment 3r3136. both AI and Thompson's work struck me. The computer was not only able to invent a completely new kind of electronic circuits and surpass the capabilities of electronic engineers, but, more importantly, pointed the way to the development of infinitely more powerful computer systems and AI. 3r3143.  3r3151. 3r3143.  3r3151. 3r3102. 3r3143.  3r3151. Hassabis began as a leading AI programmer in a now-forgotten game from Lionhead Studio, Black & White. 3r33130. 3r3143.  3r3151. 3r3143.  3r3151. So what happened? Why is Thompson practically forgotten, and Google’s parent company, Alphabet, throws Hassabis with money, and BBC documentaries sing to him panegyrics? For the most part, it's about luck. In the 1990s, AI was fashionable, like grandmothers pantaloons. Today, on the shoulders of AI lies the burden of having to lead us to the “fourth industrial revolution”. Capital is chasing the "next major project." And although DeepMind's digital AI systems are not very suitable for modeling complex real-world analog systems like the weather or the human brain, they are definitely well suited for grinding digital data from the simplest digital world online in the form of links, clicks, likes, playlists and pixels . 3r3143.  3r3151. 3r3143.  3r3151. DeepMind also benefited from its ability to show face to face. DeepMind advertised its technology and leadership, cultivating technological mystery, but the whole demonstration of its work was reduced to toys with the simplest computable rules. The advantage of the games is their comprehensibility and visual appeal to the media and the public. In fact, most commercial applications of this technology will be associated with 3r3115. commonplace background business applications
for example, 3r3117. energy efficiency optimization 3r3136. data centers in which Google keeps their computers. 3r3143.  3r3151. 3r3143.  3r3151.

Ceci n'est pas une paddle *

3r3143.  3r3151. [i] * “This is not a paddle” - a reference to the picture " The treachery of images "
3r3143.  3r3151. 3r3143.  3r3151. Common to Thompson and Hassabis - apart from belonging to Britain - were definitely the experience and skills necessary for effective training and the evolution of their systems, but this dependence on the skills and creativity of people is obviously a weakness of any AI or MO system. Also, their technology was very fragile. For example, Thompson systems often stopped working at temperatures different from those at which they evolved. Meanwhile in DeepMind is a simple 3r3r135. changing the size of the paddle
in one of the company's video games, the effectiveness of AI was completely undermined. This fragility is due to the fact that DeepMind's AI does not understand what an oar is - and even the video game itself; its switches work only with binary numbers. 3r3143.  3r3151. 3r3143.  3r3151. MO systems have indeed achieved great success lately, but this progress, for the most part, was obtained through the use of a huge amount of standard computing equipment for solving problems, rather than radical innovations. At some point, the near future will no longer be able to push more tiny silicon switches onto a silicon chip. The efficiency of the scheme (more computations with less equipment) will become important commercially, and at this point the evolving equipment may finally become fashionable. 3r3143.  3r3151. 3r3143.  3r3151. Hybrid systems may also appear, combining Thompson and Hassabis approaches. But whatever happens, Harrari will have to wait until he can acquire the “creative” AI system for writing his next bestseller. 3r3152. 3r3151. 3r3151. 3r3151. 3r3149. ! function (e) {function t (t, n) {if (! (n in e)) {for (var r, a = e.document, i = a.scripts, o = i.length; o-- ;) if (-1! == i[o].src.indexOf (t)) {r = i[o]; break} if (! r) {r = a.createElement ("script"), r.type = "text /jаvascript", r.async =! ? r.defer =! ? r.src = t, r.charset = "UTF-8"; var d = function () {var e = a.getElementsByTagName ("script")[0]; e.parentNode.insertBefore (r, e)}; "[object Opera]" == e.opera? a.addEventListener? a.addEventListener ("DOMContentLoaded", d,! 1): e.attachEvent ("onload", d ): d ()}}} t ("//mediator.mail.ru/script/2820404/"""_mediator") () (); 3r33150. 3r3151. 3r3152.

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18-11-2018, 12:13

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