Oppo A71 (2019), Um Low-End Com Câmera Frontal Com Inteligência Artificial

Como señalábamos no início das funções de inteligência artificial do terminal, teu principal atrativo, estão limitadas a câmera frontal pra tomar selfies. O Oppo A71 (2018) neste instante está acessível em alguns países da Ásia por, mais ou menos, dependendo da conversão, por volta de 150 euros.

Não há notícias, ainda, de teu lançamento pela Europa, no entanto não há que serem descartadas e muito menos em Portugal, onde os rumores -e até mesmo algumas dicas comprovadas, contudo ainda não oficiais, sinalizam pro desembarque da marca em nosso país.

Despite the difficulties with public perception of AI in the late 70, new ideias were explored in logic programming, commonsense reasoning and many other areas. In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, “toys”.

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AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s. Although some of these limits would be conquistada in later decades, others still stymie the field to this day. Limited computer power: There was not enough memory or processing speed to accomplish anything truly useful. For example, Ross Quillian’a successful work on natural language was demonstrated with a vocabulary of only twenty words, because that was all that would fit in memory. Hans Moravec argued in 1976 computers that were still millions of times too weak to exhibit intelligence.

Eu suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require horsepower. Below a certain threshold, it’s impossible, but, as power increases, eventually it could become easy. With regard to computer vision, Moravec estimated that simply the edge matching and motion detection capabilities of human retina in real time would requer a general-purpose computer capable of 109 operations/second (mil MIPS). As of 2011, practical computer vision applications require 10,000 to 1,000,000 MIPS. 8 million), was only capable of around to oitenta 130 MIPS, and a typical desktop computer at the time achieved less than 1 MIPS.

Intractability and the explosão combinatorial. In 1972, Richard Karp (building on Stephen cozinhar 1971 theorem) showed there are many problems that can probably only be solved in exponential time (in the size of the inputs). Finding optimal solutions to these problems requires unimaginable amounts of computer time except when the problems are trivial. This almost certainly meant that many of the “toy” solutions used by AI would probably never scale up into useful systems. Commonsense knowledge and reasoning.

Many important artificial intelligence applications like vision or natural language require simply enormous amounts of information about the world: the program needs to have some idea of what it might be looking at or what it is talking about. This requires that the program know most of the same things about the world that a child does. Researchers soon discovered that this was a truly vast amount of information.

no one in 1970 could build a database so large and no one knew how a program might learn so much information. Moravec’s paradox: Proving theorems and solving geometry problems is comparatively easy for computers, but a supposedly claro task like recognizing a face or crossing a room without colidindo into anything is extremely alguém.

The frame and qualification problems. AI researchers (like John McCarthy) who used logic discovered that they could not represent ordinary deductions that involved planning or default reasoning without making changes to the structure of logic itself. They developed new logics (like non-monotonic logics and modal logics) to try to solve the problems. The agencies which funded AI research (such as the British government, DARPA and NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI.

The pattern began as early as 1966 when the ALPAC report appeared criticizing machine translation efforts. After spending vinte million dollars, the NRC ended all support. In 1973, the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its “grandiose objectives” and led to the dismantling of AI research in that country. DARPA was 23 países disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars. By 1974, funding for AI projects was hard to find.