Artificial Intelligence

“The daily grinding of evolution, as accelerated by technology, churns out more and more complex organisms, with higher rates of energy use, and with …

### Designing Artificial Intelligence for Games (Part 1 …

The gaming industry has seen great strides in game complexity recently. Game developers are challenged to create increasingly compelling games. This series explores …

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### Category:Artificial intelligence – Wikimedia Commons

Category:Artificial intelligence. From Wikimedia Commons, the free media repository. Jump to: navigation, search. Category Artificial intelligence on …

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### The State of Artificial Intelligence in 15 Visuals

Pretty much every cinematic portrayal of artificial intelligence has been less than encouraging. These include smart home components that learn your preferences, any recommendations based on things you already like or have purchased, and of course, digital assistants like Siri, Alexa, and Cortana. All of those gesture control capabilities on your favourite devices that you think is just so cool? That’s considered AI, as are things like automatic speech recognition, content aware functionality, and any machine or device that can learn what its owner likes. As for which categories are seeing the most venture capital, machine learning is far and away the winner, with over $2 billion in funding to date. This isn’t a total shock; in April 2016, in a letter to shareholders, CEO Sundar Pichai lauded machine learning as the real future of AI and computing. According to these numbers, it would appear that the market for machine learning would be about to explode, but it also frees up space for other categories to start landing the big money from venture capitalists. Not surprisingly, the machine learning category has the largest number of companies, in addition to having the largest amount of funding. Why is this? Aside from Pichai’s proclamation, machine learning is perhaps the holy grail of personal computing: a machine that learns how to do things better on its own and without continual input. Again, despite the catastrophic consequences of machine learning that’s depicted in movies like War Games, it is proving to be quite useful, as users of Nest Learning Thermostats and many other learning devices can attest. In the field of Artificial Intelligence, the USA leads the pack. The country has almost 500 companies involved in some sort of AI development – substantially more than any other country. Funding for AI is also predominantly American, a fact that isn’t surprising given the overwhelming number of American AI companies. We may think of tech companies as industrial behemoths, cities unto themselves and employing thousands of people. Just how small are Artificial Intelligence companies? Remarkably so: 90% of the companies in the AI field have fewer than 50 employees each.

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### Godel vs. Artificial Intelligence

What is, perhaps, the most convincing of any of the arguments against AI is based upon Kurt GÃ¶del’s Incompleteness Theorem which says that a “Sufficiently powerful” formal system cannot consistently produce certain theorems which are isomorphic to true statements of number theory. The purpose of this paper is to show that GÃ¶del’s Theorem has little if any impact upon the prospect of producing artificial intelligence comparable to man’s. In 1931 a German mathematician named Kurt GÃ¶del published a paper which included a theorem which was to become known as his Incompleteness Theorem. GÃ¶del’s Theorem uses several terms, the implications of which must be examined in determining its applicability to AI. These terms are: formal system, consistency, completeness, and theorem. There is one axiom for SFS; it is the string “X”. There are two rules of inference in SFS:. Given a SFS string, any single instance of the substring “X” may be replaced by the substring “XX”. Given a SFS string, any single instance of the substring “XX” may be replaced by the substring “X”. Thus, given the axiom “X” only the first rule may be applied: replacing the X in the string with XX gives the result “XX”. Either of the two rules may be applied to this new theorem; rule 1 produces “XXX” and rule 2 leaves “X”, which is already known to be a theorem. Starting with the axiom, repeated application of rule 1 will result in new, unique theorems being formed while rule 2 can only produce old theorems. There are an infinite number of theorems in SFS. By repeated application of one of the two rules of inference, any theorem can be derived from any other theorem. There has been no mention of any meaning for the theorems of SFS. This is because SFS theorems have no meaning; they are merely strings of X’s, such as “XXXX” or “XXXXXXXXXXXXXXXX”. If we count the number of X’s in a theorem we could say that the theorem “XXXXX” represents the number five. If X is a consistent formal system then it cannot produce G as a theorem, for if it were to do so then X would contradict itself by saying that G is a theorem while G, which has been asserted to be true, says that G is not a theorem. Lucas has erred in his ideas about the limits of a mind and the implications of this toward AI. He infers from GÃ¶del’s Theorem that the statement “G is not a theorem of formal system X” is true and so there is something that a mind can prove which cannot be proved by formal system X. He then goes on to conclude that – for this aforementioned reason – minds and machines cannot be the same. When Lucas says, “‘G is not a theorem of formal system X’ is true,” it is apparent that his statement is no more than a theorem of HLS. In the same light, GÃ¶del’s Incompleteness Theorem is a theorem of HLS, so it is called true. There is a statement G which, when suitably represented, cannot be produced as a theorem of any mechanical formal system, nor by a mind. Hence there is no valid reason to use GÃ¶del’s Theorem to discredit the possibility of creating artificial intelligence.

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