Apart from Haugeland's claim that processors understand programinstructions, Searle's critics can agree that computers no moreunderstand syntax than they understand semantics, although, like allcausal engines, a computer has syntactic descriptions. And while it isoften useful to programmers to treat the machine as if it performedsyntactic operations, it is not always so: sometimes the charactersprogrammers use are just switches that make the machine do something,for example, make a given pixel on the computer display turn red, ormake a car transmission shift gears. Thus it is not clear that Searleis correct when he says a digital computer is just “a devicewhich manipulates symbols”. Computers are complex causalengines, and syntactic descriptions are useful in order to structurethe causal interconnections in the machine. AI programmers face manytough problems, but one can hold that they do not have to getsemantics from syntax. If they are to get semantics, they must get itfrom causality.
This isn’t to say the technology isn’t useful, or that it is inferior to its human counterpart – it is just the early stages, and the full capabilities are only just starting to be explored. Scriptbook, a US startup, is using the technology to sift through the thousands of scripts they receive and pick out winners to turn into movies. The Republican National Committee are using an AI bot to sift through the hundreds of hours of footage, press articles, and photos of Clinton to find her most weird, distorted and unflattering moments. The GOP staff are then cherry picking their preferences and sharing them on social media. While not everyone’s favourite use of the technology, it does demonstrate the abilities in audience and social media monitoring and how real-time targeting can be put into practice.
Alan Turing's 'Morphogenesis' Theory Confirmed 60 …
Searle's CR argument was thus directed against the claim that acomputer is a mind, that a suitably programmed digital computerunderstands language, or that its program does. Searle's thoughtexperiment appeals to our strong intuition that someone who didexactly what the computer does would not thereby come to understandChinese. As noted above, many critics have held that Searle is quiteright on this point—no matter how you program a computer, thecomputer will not literally be a mind and the computer will notunderstand natural language. This however cannot show that somethingelse understands—it cannot show that AI cannot produceunderstanding of natural language, for this is a different claim. Itis not the claim that the computer understands language, or that theprogram or even the system does. It is the claim that AI createsunderstanding, with the thing doing the understandingunspecified. This understanding mind might not be identical with thecomputer, the program, nor the system consisting of computer andprogram. Hauser (2002) accuses Searle of Cartesian bias in hisinference from “it seems to me quite obvious that I understandnothing” to the conclusion that I really understandnothing. Normally, if one understands English or Chinese, one knowsthat one does—but not necessarily. Searle lacks the normalintrospective awareness of understanding—but this, whileabnormal, is not conclusive.
Alan Turing hated the United States
The style of made dress belied his nature as somewhat more free from restrictions whereas the woman, bound by corsets and strict dress-codes found herself held back in clothing as in society.
Young Alan Turing was told by his science teacher he …
Turing was optimistic that computers themselves would soon be able toexhibit apparently intelligent behavior, answering questions posed inEnglish and carrying on conversations. Turing (1950) proposed what isnow known as the Turing Test: if a computer could pass for human inon-line chat, it should be counted as intelligent. By the late 1970s,as computers became faster and less expensive, some in the burgeoningAI community claimed that their programs could understand Englishsentences, using a database of background information. The work of oneof these, Yale researcher Roger Schank (Schank & Abelson 1977)came to the attention of John Searle (Searle's U.C. Berkeley colleagueHubert Dreyfus was an earlier critic of the claims made by AIresearchers). Schank developed a technique called “conceptualrepresentation” that used “scripts” to representconceptual relations (a form of Conceptual Role Semantics). Searle'sargument was originally presented as a response to the claim that AIprograms such as Schank's literally understand the sentences that theyrespond to.