Making Sense of AI. Anthony Elliott. Читать онлайн. Newlib. NEWLIB.NET

Автор: Anthony Elliott
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: Математика
Год издания: 0
isbn: 9781509548910
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Rossum’s Universal Robots, reality was to be shaped, thought about and interpreted with reference to automatons, cyborgs and androids. At the dawn of the twentieth century, the dream of automated machines was brought finally and firmly inside the territory where empirical testing is done, most notably with a tide-predicting mechanical computer – commonly known as Old Brass Brains – developed by E. G. Fischer and Rolin Harris.7 The world had, at long last, shifted away from the ‘natural order of things’ towards something altogether more magical: the ‘artificial order of mechanical brains’.

      There has been, then, a wide and widening gamut of automated technological advances, symptomatic of the shift from thinking machines that may equal the intelligence of humans to thinking machines that may exceed the intelligence of humans, but all of which have been and remain highly contested. Whether automated intelligent machines are likely to surpass human intelligence not only in practical applications but in a more general sense figures prominently among the major issues of our times and our lives in these times. Notwithstanding the notoriously overoptimistic claims of various AI researchers and futurists, there has been an overwhelming sense of crisis confronted by scientists, philosophers and theorists of technology alike, in greater or smaller measure, that the feverish ambition to establish whether AI could ever really be smarter than humans has resulted in a new structure of feeling where humanity is ‘living at the crossroads’. There have been, it should be noted, some very vocal and often devastating critiques of AI developed in this connection. The philosopher Hubert Dreyfus was an important early critic. In his book What Computers Can’t Do, Dreyfus argued that the equation mark put between machine and human intelligence in AI was fundamentally flawed. To the question of whether we might eventually regard computers as ‘more intelligent’ than humans, Dreyfus answered that the structure of the human mind (both its conscious and unconscious architectures) could not be reduced to the mathematical precepts which guide AI. Computers, as Dreyfus put it, altogether lack the human ability to understand context or grasp situated meaning. Essentially reliant on a simple set of mathematical rules, AI is unable, Dreyfus argued, to grasp the ‘systems of reference’ of which it is a part.

      Imagine a native English speaker who knows no Chinese locked in a room full of boxes of Chinese symbols (a data base) together with a book of instructions for manipulating symbols (the program). Imagine that people outside the room send in other Chinese symbols which, unknown to the person in the room, are questions in Chinese (the input). And imagine that by following the instructions in the program the man in the room is able to pass out Chinese symbols which are correct answers to the questions (the output). The program enables the person in the room to pass the Turing Test for understanding Chinese but he does not understand a word of Chinese.9

      The upshot of Searle’s arguments is clear. Machine and human intelligence might mirror each other in chiasmic juxtaposition, but AI is not able to capture the human ability of constantly connecting words, phrases and talk within practical contexts of action. Meaning and reference are, in short, not reducible to a form of information processing. It was Wittgenstein that pointed out that a dog may know its name, but not in the same way that her master does. Searle demonstrates this is similarly true for computers. It is this human ability to understand context, situation and purpose within modalities of day-to-day experience that Searle, powerfully and provocatively, asserts in the face of comparisons between human and machine intelligence.

      So, AI is also all about galaxy-wide movement and especially the automated global movement of software, symbols, simulations, ideas, information and intelligent agents. AI-powered information societies involve a relentless automation of economic, social and political life. This point is an important one to register, as many commentators invoke the spectre of globalization to capture the economic transformations of manufacturing, industry and enterprise as a consequence of AI technology and its deployment in offshore business models. Certainly, a great deal of academic and policy thinking has emphasized how the global digital economy has become ‘borderless’, with many frontiers now automated and regulated through the operations of intelligent machines. The rise of AI is intricately interwoven with globalization, it is often said. This is surely the case, though it is vital to see that globalization links together people, intelligent machines and automation in complex, contradictory and uneven ways. Understanding that AI is both condition and consequence of globalization has to be properly contextualized.