Cyborgs_Transhumanism
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Trends about the next generation
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Creating #sentient machines with #deepLearning’ AI technology

Creating #sentient machines with #deepLearning’ AI technology | Cyborgs_Transhumanism | Scoop.it
Building machines that process information the same way a brain does has been a dream for over 50 years. Artificial intelligence, fuzzy logic, and neural networks have all experienced some degrees of success, but machines still cannot recognize pictures or understand language as well as humans can.
luiy's insight:

Kurzweil believes a conscious machine capable of understanding complex natural language will be developed within 16 years. "I've had a consistent date of 2029 for that vision," he said "and that doesn't just mean logical intelligence. It means emotional intelligence, being funny, getting the joke, being sexy, loving, understanding emotions. This is what separates computers and humans today."

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Computer science: The learning machines | #deepLearning

Computer science: The learning machines | #deepLearning | Cyborgs_Transhumanism | Scoop.it

Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence.


Via Szabolcs Kósa
luiy's insight:

Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.

 

Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.

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R Schumacher & Associates LLC's curator insight, January 15, 2014 1:43 PM

The monikers such as "deep learning" may be new, but Artificial Intelligence has always been the Holy Grail of computer science.  The applications are many, and the path is becoming less of an uphill climb.