Machine thinking on its own is called

• Afrikaans • العربية • অসমীয়া • Azərbaycanca • বাংলা • Беларуская • Български • Català • Čeština • Cymraeg • Dansk • Deutsch • Eesti • Español • Esperanto • Euskara • فارسی • Français • Gaeilge • Galego • 한국어 • Հայերեն • हिन्दी • Hrvatski • Bahasa Indonesia • Íslenska • Italiano • עברית • ქართული • Қазақша • Latina • Latviešu • Lietuvių • Magyar • മലയാളം • Bahasa Melayu • Монгол • Nederlands • 日本語 • Nordfriisk • Norsk bokmål • Polski • Português • Română • Русский • Scots • Shqip • Simple English • Slovenčina • Slovenščina • Српски / srpski • Srpskohrvatski / српскохрватски • Suomi • Svenska • ไทย • Türkçe • Українська • Tiếng Việt • 吴语 • 粵語 • 中文 • v • t • e The Turing test, originally called the imitation game by The test was introduced by Turing in his 1950 paper " '" Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." imitation game?" Since Turing introduced his test, it has been both highly influential and widely criticised, and has become an important concept in the History [ ] Philosophical background [ ] The question of whether it is possible for machines to think has a long history, which is firmly entrenched in the distinction between [H]ow many different automata or moving machines could be made by the industry of man... For we can easily understand a machine's being constituted so that it can utter words, and even emit some responses to ...

Subscribe to Crazy/Genius : Can artificial intelligence be smarter than a person? Answering that question often hinges on the definition of artificial intelligence. But it might make more sense, instead, to focus on defining what we mean by “smart.” In the 1950s, the psychologist J. P. Guilford divided creative thought into two categories: convergent thinking and divergent thinking. Convergent thinking, which Guilford defined as the ability to answer questions correctly, is predominantly a display of memory and logic. Divergent thinking, the ability to generate many potential answers from a single problem or question, shows a flair for curiosity, an ability to think “outside the box.” It’s the difference between remembering the capital of Austria and figuring how to start a thriving business in Vienna without knowing a lick of German. When most people think of AI’s relative strengths over humans, they think of its convergent intelligence. With superior memory capacity and processing power, computers outperform people at rules-based games, complex calculations, and data storage: chess, advanced math, and Jeopardy. What computers lack, some might say, is any form of imagination, or rule-breaking curiosity—that is, divergence. But what if that common view is wrong? What if AI’s real comparative advantage over humans is precisely its divergent intelligence—its creative potential? That’s the subject of the latest episode of the podcast Crazy/Genius, produced by Kasia Mychajlowy...

Everywhere and invisible You are likely reading this on a computer. You are also likely taking that fact for granted. That’s even though the device in front of you would have astounded computer scientists just a few decades ago, and seemed like sheer magic much before that. It contains billions of tiny computing elements, running millions of lines of software instructions, collectively written by countless people across the globe. The result: You click or tap or type or speak, and the result seamlessly appears on the screen. In 1833, Charles Babbage envisioned a programmable machine with a “store” for holding numbers, a “mill’ for operating on them (shown), an instruction reader and a printer. © The Board of Trustees of the Science Museum ( Computers once filled rooms. Now they’re everywhere and invisible, embedded in watches, car engines, cameras, televisions and toys. They manage electrical grids, analyze scientific data and predict the weather. The modern world would be impossible without them, and our dependence on them for health, prosperity and entertainment will only increase. Scientists hope to make computers faster yet, to make programs more intelligent and to deploy technology in an ethical manner. But before looking at where we go from here, let’s review where we’ve come from. In 1833, the English mathematician Charles Babbage conceived a programmable machine that presaged today’s computing architecture, featuring a “store” for holding numbers, a “mill” for oper...

The Industrial Revolution was a period of scientific and technological development in the 18th century that transformed largely rural, agrarian societies—especially in Europe and North America—into industrialized, urban ones. Goods that had once been painstakingly crafted by hand started to be produced in mass quantities by machines in factories, thanks to the introduction of new machines and techniques in textiles, iron making and other industries. When Was the Industrial Revolution? Though a few innovations were developed as early as the 1700s, the Industrial Revolution began in earnest by the 1830s and 1840s in Britain, and soon spread to the rest of the world, including the United States. Modern historians often refer to this period as the First Industrial Revolution, to set it apart from a Spinning Jenny Thanks in part to its damp climate, ideal for raising sheep, Britain had a long history of producing textiles like wool, linen and cotton. But prior to the Industrial Revolution, the British textile business was a true “cottage industry,” with the work performed in small workshops or even homes by individual spinners, weavers and dyers. Starting in the mid-18th century, innovations like the spinning jenny (a wooden frame with multiple spindles), the flying shuttle, the water frame and the power loom made weaving cloth and spinning yarn and thread much easier. Producing cloth became faster and required less time and far less human labor. More efficient, mechanized prod...

• v • t • e The philosophy of artificial intelligence is a branch of the philosophy of artificial intelligence. Some scholars argue that the AI community's dismissal of philosophy is detrimental. The philosophy of artificial intelligence attempts to answer such questions as follows: • Can a machine act intelligently? Can it solve any problem that a person would solve by thinking? • Are human intelligence and machine intelligence the same? Is the • Can a machine have a feel how things are? Questions like these reflect the divergent interests of Important • • The • • • Can a machine display general intelligence? [ ] Is it possible to create a machine that can solve all the problems humans solve using their intelligence? This question defines the scope of what machines could do in the future and guides the direction of AI research. It only concerns the behavior of machines and ignores the issues of interest to really thinking, as a person thinks, rather than just producing outcomes that appear to result from thinking? The basic position of most AI researchers is summed up in this statement, which appeared in the proposal for the • "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." Arguments against the basic premise must show that building a working AI system is impossible because there is some practical limit to the abilities of computers or that there is some special quality of the human m...

The argument and thought-experiment now generally known as the Chinese Room Argument was first published in a 1980 article by American philosopher John Searle (1932– ). It has become one of the best-known arguments in recent philosophy. Searle imagines himself alone in a room following a computer program for responding to Chinese characters slipped under the door. Searle understands nothing of Chinese, and yet, by following the program for manipulating symbols and numerals just as a computer does, he sends appropriate strings of Chinese characters back out under the door, and this leads those outside to mistakenly suppose there is a Chinese speaker in the room. The narrow conclusion of the argument is that programming a digital computer may make it appear to understand language but could not produce real understanding. Hence the “Turing Test” is inadequate. Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symbol strings, but have no understanding of meaning or semantics. The broader conclusion of the argument is that the theory that human minds are computer-like computational or information processing systems is refuted. Instead minds must result from biological processes; computers can at best simulate these biological processes. Thus the argument has large implications for semantics, philosophy of language and mind, theories of consciousness, computer science and cognitive science generally. As a resul...