ARTIFICIAL INTELLIGENCE IS COLOSSALLY  hyped these days, but the dirty little secret is that it still has a long, long way to go.

Sure A.I systems have mastered an array of games, from chess and  Go to ''Jeopardy'' and poker, but the technology continues to struggle in the real world.

Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has yet designed a machine that can read reliably at the level of of sixth grader, let alone a college student.

Computers that can educate themselves - a mark of true intelligence - remains a very huge, huge dream.

Even the trendy technique of ''deep learning,'' which uses artificial neural networks to discern complex statistical correlations in huge amounts of data, often comes up short. Some of the best image-recognition systems, for example, can successfully distinguish dog breeds, yet remain capable of major blunders, like mistaking a simple pattern of yellow and black stripes for a school bus.

Such systems can neither comprehend what is going on in complex visual scenes { "who is chasing whom and why? "} nor follow simple instructions [" Read this story and summarize what it means"]

Although the field of Artificial Intelligence is exploding with microdiscoveries, progress toward the robustness and flexibility of human cognition remains elusive.

Not long ago, for example, while sitting with me in a cafe, my 3-year-old daughter spontaneously  realized that she could climb out of her chair in a new way : backward, by sliding through the gap between the back and the seat of the chair.

My daughter had never seen anyone else disembark in quiet this way; she invented it on her own  -and without the benefit of trial and error, or the need for terabytes of labeled data.

Presumably, my daughter relied on an implicit theory of how her body moves, along with an implicit theory of physics - how one complex object travels through the aperture of another.

I challenge any robot to do the same.

A.I. systems tend to be passive vessels, dredging through data in search of statistical correlations; human are active engines for discovering how things work.

To get computers to think like human, we need a new A.I. paradigm, one that places ''top down'' and ''bottom up'' knowledge on equal footing.

Bottom up knowledge is the kind raw information we get directly our senses, like patterns of light falling on our retina. Top-down knowledge comprises cognitive models of the world and how it works.

Deep learning is a very good at bottom up knowledge, like discerning patterns of pixel corresponds to golden retrievers as opposed to Labradors. But is no use when comes to top down knowledge.

If my daughter sees her reflection in a bowl of water, she knows the image is illusory; she knows she is not actually in the bowl.

The Honor and Serving of the Latest Global Operational Research on ''Artificial Intelligence'', innovations and inventions continues. The World students Society thanks author and researcher Professor Gary Marcus, of Psychology and Neural sciences and New York University.

With respectful dedication to the Scientists, Artificial Intelligence Research Scientists, Inventors, Students, Professors and Teachers of the world.

See Ya all ''register'' on !WOW! : wssciw.blogspot.com  and Twitter -!E-WOW! - the Ecosystem 2011 :

''' Students New Paradigms '''

Good Night and God Bless

SAM Daily Times - the Voice of the Voiceless


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