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Artificial Intelligence Turns 50


Tormod

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"When I'm asked whether computers will ever really mimic humans, I say, yes and no," says Dartmouth philosophy professor James Moor, director of AI@50, a conference this summer at Dartmouth commemorating the golden anniversary of the field of artificial intelligence.

 

lefthttp://hypography.com/gallery/files/5/450px-HONDA_ASIMO_thumb.jpg[/img]"Yes, neural net computers are being built that operate somewhat analogously to the brain; and no, humans are biological creatures with emotions, feelings, and creativity that are unlikely to be fully captured by machines, at least for the foreseeable future."

 

The field of AI has its roots at the 1956 Dartmouth Summer Research Project on Artificial Intelligence. In those early days, says Moor, researchers wanted to make machines more cognizant and to lay out a framework to better understand human intelligence. Today, according to Moor, these remain goals for AI, but AI has become more focused on specific aspects of intelligence, such as learning, reasoning, vision, and action.

 

Moor's expertise lies in the philosophy of computing, which explores such questions as, what kinds of minds can machines have, what does it mean to be creative or aware, and what kinds of decisions should machines make? These questions draw out interesting features of human nature, and highlight what it is that makes humans similar to and different from machines.

 

He considers the field on two levels. One is the applied computing level that involves the development of "expert systems," for such areas as spectrographic analysis, stock market patterns, and medical diagnostics; and the development of robotic systems for driving cars as well as software for searching the Internet. The other is the philosophical level that tries to answer a question such as, what is the nature of intelligence? He thinks that bridging these two levels is challenging, but very useful in developing a fuller understanding of minds. Science forces philosophy to be more empirical and philosophy forces science to be more reflective.

 

"The initial hope was that AI could do much more than has actually panned out in 50 years," he says. "Language use and translation by machine, for example, was once expected to be quite easy. Turns out that language use requires extensive knowledge of how the world works. This complex knowledge usually assumed in ordinary conversation, which is easy for us, but it's difficult to teach a computer voice inflection, social and cultural idiosyncrasies, as well as a multitude of social contexts."

 

At the AI@50 conference, which will be held at Dartmouth on July 13-15, Moor believes there will be considerable debate about the future direction that AI should take. He says that the plan for the conference is not only to honor the past and assess present accomplishments, but also to help seed ideas for future artificial intelligence research.

 

"AI has come a long way in 50 years, and it has a bright future. Although computers may never replace us, smart machines will be prevalent in our environment, and may someday be even implanted in us. The future of artificial intelligence deserves careful and sustained scrutiny."

 

Listen to philosophy professor Jim Moor's podcast where he discusses the history of AI and some of the philosophical questions surrounding it.

 

Source: Darthmouth College

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Dartmouth is underappreciated as an early pioneer in computer science, as one of the principal initiators of the Multics operating system (progenitor of Unix) and the Basic programming language (by John Kemeny and Tom Kurtz)...

 

Computadora,

Buffy

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AI has faced huge issues in its years of ascendance; after all when advanced computing began the main problem was making anything work at all!

 

However there have been points in its history where AI has taken a huge step backwards. I say backwards because often we arrive at a point, realise our path is not ideal and take a turn for the better often involving going back to where we started and thinking again.

 

One important such point was where AI began to think Qualitatively rather than quantitatively. This step is essential in moving towards programs that 'think' as we do, because humans do not process data quantitatively at all. This paradigm shift led to a whole new branch of AI springing up almost over night known as 'Qualitative Reasoning'.

 

I, amoungst others, wait to see when we will be blessed with our next great leap backwards.

 

GB

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