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Makes sense.


Moth Orchid flower (credit: Imgur.com)

A computer scientist and biologist propose to unify the theory of evolution with learning theories to explain the “amazing, apparently intelligent designs that evolution produces.”

The scientists — University of Southampton School of Electronics and Computer Science professor Richard Watson * and Eötvös Loránd University (Budapest) professor of biology Eörs Szathmáry * — say they’ve found that it’s possible for evolution to exhibit some of the same intelligent behaviors as learning systems — including neural networks.

In the early days of the space race of the 1960s, NASA used satellites to map the geography of the moon. A better understanding of its geology, however, came when men actually walked on the moon, culminating with Astronaut and Geologist Harrison Schmitt exploring the moon’s surface during the Apollo 17 mission in 1972.

Image credit: Scientific American
Image credit: Scientific American

In the modern era, Dr. Gregory Hickock is one neuroscientist who believes the field of neuroscience is pursuing comparable advances. While scientists have historically developed a geographic map of the brain’s functional systems, Hickock says computational neuroanatomy is digging deeper into the geology of the brain to help provide an understanding of how the different regions interact computationally to give rise to complex behaviors.

“Computational neuroanatomy is kind of working towards that level of description from the brain map perspective. The typical function maps you see in textbooks are cartoon-like. We’re trying to take those mountain areas and, instead of relating them to labels for functions like language, we’re trying to map them on — and relate them to — stuff that the computational neuroscientists are doing.”

Hickok pointed to a number of advances that have already been made through computational neuroanatomy: mapping visual systems to determine how the visual cortex can code information and perform computations, as well as mapping neurally realistic approximations of circuits that actually mimic motor control, among others. In addition, researchers are building spiking network models, which simulate individual neurons. Scientists use thousands of these neurons in simulations to operate robots in a manner comparable to how the brain might perform the job.

That research is driving more innovation in artificial intelligence, says Gregory. For example, brain-inspired models are being used to develop better AI systems for stores of information or retrieval of information, as well as in automated speech recognition systems. In addition, this sort of work can be used to develop better cochlear implants or other sorts of neural-prostheses, which are just starting to be explored.

“In terms of neural-prostheses that can take advantage of this stuff, if you look at patterns and activity in neurons or regions in cortex, you can decode information from those patterns of activity, (such as) motor plans or acoustic representation,” Hickok said. “So it’s possible now to implant an electrode array in the motor cortex of an individual who is locked in, so to speak, and they can control a robotic arm.”

More specifically, Hickok is interested in applying computational neuroanatomy to speech and language functions. In some cases where patients have lost the ability to produce fluid speech, he states that the cause is the disconnection of still-intact brain areas that are no longer “talking to each other”. Once we understand how these circuits are organized and what they’re doing computationally, Gregory believes we might one day be able to insert electrode arrays and reconnect those brain areas as a form of rehabilitation.

As he looks at the future applications in artificial intelligence, Hickok says he expects continued development in neural-prostheses, such as cochlear implants, artificial retinas, and artificial motor control circuits. The fact that scientists are still trying to simulate how the brain does its computations is one hurdle; the “squishy” nature of brain matter seems to operate differently than the precision developed in digital computers.

Though multiple global brain projects are underway and progress is being made (Wired’s Katie Palmer gives a succinct overview), Gregory emphasizes that we’re still nowhere close to actually re-creating the human mind. “Presumably, this is what evolution has done over millions of years to configure systems that allow us to do lots of different things and that is going to (sic) take a really long time to figure out,” he said. “The number of neurons involved, 80 billion in the current estimate, trillions of connections, lots and lots of moving parts, different strategies for coding different kinds of computations… it’s just ridiculously complex and I don’t see that as something that’s easily going to give up its secrets within the next couple of generations.”

Anyone paying attention to all of the news about autonomous vehicles from Google and other companies may have noticed a common thread in the stories, photos and videos. The roads are always dry and the sun is shining. That’s because many of the sensors used to let a car manage its own trajectory don’t work well unless they can see the road and other surroundings clearly. Ford is now claiming to be the first automaker to test its prototype autonomous vehicles in winter weather conditions.

After becoming the first automaker to use the Mcity test facility in Ann Arbor, Mich. for autonomous vehicle tests last fall, the Dearborn automaker continued its development work into December when the snow started to fly.

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And, no one should say “Never” when it comes to people replaced by robots in the military.

Not good for the Russian military people.


Science fiction movies are quickly becoming a reality on the modern battlefield, as robots gradually supplant people in certain aspects of Russian military operations. The full automation of the armed forces using artificial intelligence is still a long way off, but some key functions once entrusted only to humans have already been passed on to machines.

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Wow! Talk about your “hot” topic; this will definitely have everyone across tech and the web talking: https://lnkd.in/bavpnTf


(Bloomberg) — Googles chairman thinks artificial intelligence will let scientists solve some of the worlds “hard problems,” like population growth, climate change, human development, and education.

Rapid development in the field of AI means the technology can help scientists understand the links between cause and effect by sifting through vast quantities of information, said Eric Schmidt, executive chairman of Alphabet Inc., the holding company that owns Google.

AI will play this role to navigate through this and help us.

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“It’s probably better than a person right now (at driving),” Musk said on the call.

Musk added that in the next two years or so, Tesla cars “will be able to drive virtually all roads at a safety level significantly better than humans.”

“I think within two years you’ll be able to summon your car from across the country,” Musk said.

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