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Category: robotics/AI

When you read about what some startups are doing these days it seems like you’re reading a sci-fi book. Earlier this year we published an article titled “3 Companies Building Nanorobot Companies” and we talked about using software, robots, and synthetic biology to engineer synthetic organisms (essentially nanorobots) that can be used to create efficiencies. According to BCC Research, the global market for microbes and microbial products was projected to approach $154.7 billion in 2015 and almost double to $306 billion by 2020. Healthcare is largest consumer of microbes (61%) followed by energy (24%) and manufacturing (13%). The massive size of the microbe industry is just begging for a bit of disruptive technology to address it and that’s exactly what Zymergen is getting up to.

Zymergen_Logo

Founded in 2013, San Francisco startup Zymergen has taken in a total of $174 million from a whole slew of investors that include Draper Fisher Jurvetson and Softbank. Their most recent funding round of $130 million closed just last week and was led by Softbank, a publicly traded Japanese technology conglomerate. This should come as no surprise considering Softbank has recently announced their intention to become the world’s number one technology investor with up to $100 billion allocated to investing in future technology companies.

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https://youtube.com/watch?v=RA4u_9FLzso

1st question that comes to mind is why? Then, I think about how this can be used against enemy states or would be criminals who are considering kidnapping or assignation attempts on leaders; then I see opportunity.

In Brief:

  • The robot can stay cooled for nearly 12 hours with a single cup of water.
  • The method is three times more effective that simple air cooling.

A novel design for robots allows them to “sweat”, greatly improving thermal and mechanical integrity. The bot from SCHAFT was a top scorer in the DARPA Robotics Challenge Trials in 2013.

The University of Tokyo’s JSK Lab’s Kengoro is a 1.7-meter (5.6 feet) tall, 56-kilogram (123 pounds) musculoskeletal humanoid crammed to the brim with circuit boards and 108 motors. These structural components generate a lot of heat which would constrain the bot’s performance, and there wasn’t much room for any cooling mechanisms. The researchers coped with this by comparing it to our own—sweat.

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DeepMind, an artificial intelligence firm that was acquired by Google in 2014 and is now under the Alphabet umbrella, has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions.

That’s huge, because it means that future AI could respond to queries from humans without being taught every possible correct answer.

TNW Momentum is our New York technology event for anyone interested in helping their company grow.

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A dozen years ago, an auto accident left Nathan Copeland paralyzed, without any feeling in his fingers. Now that feeling is back, thanks to a robotic hand wired up to a brain implant.

“I can feel just about every finger – it’s a really weird sensation,” the 28-year-old Pennsylvanian told doctors a month after his surgery.

Today the brain-computer interface is taking a share of the spotlight at the White House Frontiers Conference in Pittsburgh, with President Barack Obama and other luminaries in attendance.

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My guess is there is some QC help in this picture.

Artificial neural networks — systems patterned after the arrangement and operation of neurons in the human brain — excel at tasks that require pattern recognition, but are woefully limited when it comes to carrying out instructions that require basic logic and reasoning. This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision.

In a step toward overcoming this hurdle, researchers at Google’s DeepMind — the company that developed the Go-playing computer program AlphaGo — announced earlier this week the creation of a neural network that can not only learn, but can also use data stored in its memory to “logically reason” and make inferences to answer questions.

DeepMind’s new system — called a Differentiable Neural Computer (DNC) — combines deep learning, wherein it can learn from examples and make sense of complex input it has never received before, with an external memory, which, as the DeepMind researchers Alexander Graves and Greg Wayne explain in a blog post, allows it to “store knowledge quickly and reason about it flexibly.”

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For decades the efficient coding hypothesis has been a guiding principle in determining how neural systems can most efficiently represent their inputs. However, conclusions about whether neural circuits are performing optimally depend on assumptions about the noise sources encountered by neural signals as they are transmitted. Here, we provide a coherent picture of how optimal encoding strategies depend on noise strength, type, location, and correlations. Our results reveal that nonlinearities that are efficient if noise enters the circuit in one location may be inefficient if noise actually enters in a different location. This offers new explanations for why different sensory circuits, or even a given circuit under different environmental conditions, might have different encoding properties.

Citation: Brinkman BAW, Weber AI, Rieke F, Shea-Brown E (2016) How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits? PLoS Comput Biol 12(10): e1005150. doi:10.1371/journal.pcbi.1005150

Editor: Jeff Beck, Duke University, UNITED STATES

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The DeepMind artificial intelligence (AI) being developed by Google’s parent company, Alphabet, can now intelligently build on what’s already inside its memory, the system’s programmers have announced.

Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank.

What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain.

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