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(An alternate version of this article was originally published in the Boston Globe)

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words.

Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it’s vital we understand this new language, and what it’s increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it.

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“There is no AI without robotics… This realization is often called the ‘embodiment problem’ and most researchers in AI now agree that intelligence and embodiment are tightly coupled issues. Every different body has a different form of intelligence, and you see that pretty clearly in the animal kingdom.”


Google DeepMind’s artificial intelligence AlphaGo is a big advance but it will not get us to strong AI.

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Now, we’re hitting Terminator mode with this.


If you’re worried that artificial intelligence will take over the world now that computers are powerful enough to outsmart humans at incredibly complex games, then you’re not going to like the idea that someday computers will be able to simply build their own chips without any help from humans. That’s not the case just yet, but researchers did come up with a way to grow metal wires at a molecular level.

At the same time, this is a remarkable innovation that paves the way for a future where computers are able to create high-end chip solutions just as a plant would grow leaves, rather than having humans develop computer chips using complicated nanoengineering techniques.

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Specifically, artificially intelligent computers…


As sophisticated algorithms can complete tasks we once thought impossible, computers are seeming to become a real threat to humanity. Whether they decide to pulp us into human meat paste, or simply make our work completely unnecessary, argues technology reporter Alex Hern, we should be afraid of computers.

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David Hanson’s best work yet.

They are getting really close to being passable for human. Now, it’s just a question of when they will stick a robot like ATLAS inside of something like this so it can walk around, talk, and look like a person. That will happen around 2020’ish..


Meet Sophia. Hanson Robotics human-like robot that may embody the androids of our future.

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March 16th, 2016 Editors Nanomedicine

light-activated-bio-botsBiologically powered robots may one day be used to perform surgical procedures, deliver drugs, and maybe to even make humanoid overlords for us mortals. A big step toward that was taken by researchers at University of Illinois at Urbana-Champaign who used light-activated muscle cells as the power source to make tiny bio-bots.

biobot
The optogenetic technique published in Proceedings of the National Academy of Sciences relies on genetically engineered mouse muscle cells that were made to contract in response to blue light. Rings of these cells were placed around a 3D printed flexible rods of different lengths between two and seven millimeters. When light was illuminated over the mechanism, the biobots contracted and walked in a certain direction. Various lengths and configurations were tried to achieve the best walking results. Moreover, the researchers were able to change the direction of the walking bio-bot.

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Even if we don’t create a true AI for a thousand years, these algorithms, pared with our exponentially increasing computing power, could have much of the same effect on our civilization as the more traditional, AI-centric type Singularity. Very, very soon.


A schematic diagram of machine learning for materials discovery (credit: Chiho Kim, Ramprasad Lab, UConn)

Replacing inefficient experimentation, UConn researchers have used machine learning to systematically scan millions of theoretical compounds for qualities that would make better materials for solar cells, fibers, and computer chips.

Led by UConn materials scientist Ramamurthy ‘Rampi’ Ramprasad, the researchers set out to determine which polymer atomic configurations make a given polymer a good electrical conductor or insulator, for example.

Let’s step back and consider the broader digital technology landscape for one moment. We have built our past, current, and new technology off of a digital foundation with machine language of standard not very complex algorithms that processes 0s & 1s which has been around since the 50’s. So, not too shock by this article; in fact we may not see a major leap in Humanoid Robots possibly until Quantum hits the mainstream. Quantum holds a lot of promise; however, it’s still too early to know for sure.


Artificial intelligence may be coming to your IT department sooner than you think, but not the way you might imagine.

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