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Interesting article on the limited future of human paid employment for AI, some thoughts.


By Paul R. Daugherty and H. James Wilson

Superman versus Batman. Captain America versus Iron Man. Zuckerberg versus Musk?

The reported clash between the two technology titans is proof that not everyone sees the benefits and dangers of artificial intelligence in the same light. Yet from Facebook’s algorithms to Tesla’s self-driving cars, it’s clear that AI isn’t science fiction any longer—and that we’re already at the cusp of a new era, with AI poised to deliver transformational change in business and society.

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Learning algorithms which improve how they learn, computers which define their own objectives and then do it, robots which learn from us like children do, its all not far off now.


Panelists:

Professor juergen schmidhuber director & professor, the swiss AI lab IDSIA – USI & SUPSI

Get Started at https://directory.cognitionx.com/

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A glimpse at the coming AI researchers. (AI’s that do research).


A new type of artificial-intelligence-driven chemistry could revolutionise the way molecules are discovered, scientists claim.

In a new paper published today in the journal Nature, chemists from the University of Glasgow discuss how they have trained an artificially-intelligent organic chemical synthesis robot to automatically explore a very large number of .

Their ‘self-driving’ system, underpinned by machine learning algorithms, can find new reactions and molecules, allowing a digital-chemical data-driven approach to locating new molecules of interest, rather than being confined to a known database and the normal rules of organic synthesis.

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While scientists have been learning more and more about our solar system and the way things work, many of our Sun’s mechanics still remain a mystery. In advance of the launch of the Parker Solar Probe, which will make contact with the Sun’s outer atmosphere, however, scientists are foreshadowing what the spacecraft might see with new discoveries. In a paper published this week in The Astrophysical Journal, scientists detected structures within the Sun’s corona, thanks to advanced image processing techniques and algorithms.

The question that this group of scientists, led by Craig DeForest from the Southwest Research Institute’s branch in Boulder, Colorado, was trying to answer was in regard to the source of solar wind. “In deep space, the solar wind is turbulent and gusty,” said DeForest in a release. “But how did it get that way? Did it leave the Sun smooth, and become turbulent as it crossed the solar system, or are the gusts telling us about the Sun itself?”

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Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.

“Using an optical chip to perform neural computations more efficiently than is possible with digital computers could allow more complex problems to be solved,” said research team leader Shanhui Fan of Stanford University. “This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can’t imagine now.”

An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.

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In 1610, Galileo redesigned the telescope and discovered Jupiter’s four largest moons. Nearly 400 years later, NASA’s Hubble Space Telescope used its powerful optics to look deep into space—enabling scientists to pin down the age of the universe.

Suffice it to say that getting a better look at things produces major scientific advances.

In a paper published on July 18 in The Astrophysical Journal, a team of scientists led by Craig DeForest—solar physicist at Southwest Research Institute’s branch in Boulder, Colorado—demonstrate that this historical trend still holds. Using advanced algorithms and data-cleaning techniques, the team discovered never-before-detected, fine-grained structures in the outer —the Sun’s million-degree atmosphere—by analyzing taken by NASA’s STEREO spacecraft. The new results also provide foreshadowing of what might be seen by NASA’s Parker Solar Probe, which after its launch in the summer 2018 will orbit directly through that region.

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Our Fast Lightweight Autonomy program recently completed Phase 2 flight tests, demonstrating advanced algorithms designed to turn small air and ground systems into team members that can autonomously perform tasks dangerous for humans — such as pre-mission reconnaissance in a hostile urban setting or searching damaged structures for survivors following an earthquake.

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WANT a job with a successful multinational? You will face lots of competition. Two years ago Goldman Sachs received a quarter of a million applications from students and graduates. Those are not just daunting odds for jobhunters; they are a practical problem for companies. If a team of five Goldman human-resources staff, working 12 hours every day, including weekends, spent five minutes on each application, they would take nearly a year to complete the task of sifting through the pile.

Little wonder that most large firms use a computer program, or algorithm, when it comes to screening candidates seeking junior jobs. And that means applicants would benefit from knowing exactly what the algorithms are looking for.

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An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically accelerates artificial intelligence (AI) training algorithms. This gives full AI capability to inexpensive computers, and would make it possible in one to two years for supercomputers to utilize Artificial Neural Networks that quadratically exceed the possibilities of today’s artificial neural networks. The scientists presented their method on June 19 in the journal Nature Communications.

Artificial Neural Networks (or ANN) are at the very heart of the AI revolution that is shaping every aspect of society and technology. But the ANNs that we have been able to handle so far are nowhere near solving very complex problems. The very latest supercomputers would struggle with a 16 million-neuron network (just about the size of a frog brain), while it would take over a dozen days for a powerful desktop computer to train a mere 100,000-neuron network.

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