Tag Archives: mimic

#431165 Intel Jumps Into Brain-Like Computing ...

The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.

At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
Image Credit: Intel Continue reading

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#431110 Soft robotics: self-contained soft ...

Researchers at Columbia Engineering have solved a long-standing issue in the creation of untethered soft robots whose actions and movements can help mimic natural biological systems. A group in the Creative Machines lab led by Hod Lipson, professor of mechanical engineering, has developed a 3D-printable synthetic soft muscle, a one-of-a-kind artificial active tissue with intrinsic expansion ability that does not require an external compressor or high voltage equipment as previous muscles required. The new material has a strain density (expansion per gram) that is 15 times larger than natural muscle, and can lift 1000 times its own weight. Continue reading

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#430955 This Inspiring Teenager Wants to Save ...

It’s not every day you meet a high school student who’s been building functional robots since age 10. Then again, Mihir Garimella is definitely not your average teenager.
When I sat down to interview him recently at Singularity University’s Global Summit, that much was clear.
Mihir’s curiosity for robotics began at age two when his parents brought home a pet dog—well, a robotic dog. A few years passed with this robotic companion by his side, and Mihir became fascinated with how software and hardware could bring inanimate objects to “life.”
When he was 10, Mihir built a robotic violin tuner called Robo-Mozart to help him address a teacher’s complaints about his always-out-of-tune violin. The robot analyzes the sound of the violin, determines which strings are out of tune, and then uses motors to turn the tuning pegs.
Robo-Mozart and other earlier projects helped Mihir realize he could use robotics to solve real problems. Fast-forward to age 14 and Flybot, a tiny, low-cost emergency response drone that won Mihir top honors in his age category at the 2015 Google Science Fair.

The small drone is propelled by four rotors and is designed to mimic how fruit flies can speedily see and react to surrounding threats. It’s a design idea that hit Mihir when he and his family returned home after a long vacation to discover they had left bananas on their kitchen counter. The house was filled with fruit flies.
After many failed attempts to swat the flies, Mihir started wondering how these tiny creatures with small brains and horrible vision were such masterful escape artists. He began digging through research papers on fruit flies and came to an interesting conclusion.
Since fruit flies can’t see a lot of detail, they compensate by processing visual information very fast—ten times faster than people do.
“That’s what enables them to escape so effectively,” says Mihir.
Escaping a threat for a fruit fly could mean quickly avoiding a fatal swat from a human hand. Applied to a search-and-response drone, the scenario shifts—picture a drone instantaneously detecting and avoiding a falling ceiling while searching for survivors inside a collapsing building.

Now, at 17, Mihir is still pushing Flybot forward. He’s developing software to enable the drone to operate autonomously and hopes it will be able to navigate environments such as a burning building, or a structure that’s been hit by an earthquake. The drone is also equipped with intelligent sensors to collect spatial data it will use to maneuver around obstacles and detect things like a trapped person or the location of a gas leak.
For everyone concerned about robots eating jobs, Flybot is a perfect example of how technology can aid existing jobs.
Flybot could substitute for a first responder entering a dangerous situation or help a firefighter make a quicker rescue by showing where victims are trapped. With its small and fast design, the drone could also presumably carry out an initial search-and-rescue sweep in just a few minutes.
Mihir is committed to commercializing the product and keeping it within a $250–$500 price range, which is a fraction of the cost of many current emergency response drones. He hopes the low cost will allow the technology to be used in developing countries.
Next month, Mihir starts his freshman year at Stanford, where he plans to keep up his research and create a company to continue work on the drone.
When I asked Mihir what fuels him, he said, “Curiosity is a great skill for inventors. It lets you find inspiration in a lot of places that you may not look. If I had started by trying to build an escape algorithm for these drones, I wouldn’t know where to start. But looking at fruit flies and getting inspired by them, it gave me a really good place to look for inspiration.”
It’s a bit mind boggling how much Mihir has accomplished by age 17, but I suspect he’s just getting started.
Image Credit: Google Science Fair via YouTube Continue reading

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#428993 Mimicking human facial expression ...

Hanson Robotics’ “Han” is a humanoid robot capable of recognizing someone in front of it, including their voice, and then mimicking that human’s facial expressions while interacting and talking. Wow! Related Posts This Week’s Awesome Stories From …ROBOTICS Meet Iorek, … Continue reading

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