A)AI could get 100 times more energy-efficient with IBM’s new artificial synapses
Copying the features of a neural network in silicon might make machine learning more usable on small devices like smartphones.
The catch is that neural nets, which are modeled loosely on the structure of the human brain, are typically constructed in software rather than hardware, and the software runs on conventional computer chips. That slows things down.
IBM has now shown that building key features of a neural net directly in silicon can make it 100 times more efficient. Chips built this way might turbocharge machine learning in coming years.
The IBM chip, like a neural net written in software, mimics the synapses that connect individual neurons in a brain. The strength of these synaptic connections needs to be tuned in order for the network to learn. In a living brain, this happens in the form of connections growing or withering over time. That is easy to reproduce in software but has proved infuriatingly difficult to achieve with hardware, until now.
The IBM researchers demonstrate the microelectronic synapses in a research paper published in the journal Nature. Their approach takes inspiration from neuroscience by using two types of synapses: short-term ones for computation and long-term ones for memory. This method “addresses a few key issues,” most notably low accuracy, that have bedeviled previous efforts to build artificial neural networks in silicon, says Michael Schneider, a researcher at that National Institute of Standards and Technology who is researching neurologically inspired computer hardware.
The researchers tested a neural network built from the components of two simple image-recognition tasks: handwriting and color image classification. They found the system to be as accurate as a software-based deep neural network even though it consumed only 1 percent as much energy.
The discovery isn’t only important for AI. If it scales to commercial production, it could vindicate a big bet IBM has been making. Although the company doesn’t sell computer chips these days, it has been investing in efforts to reinvent computer hardware, hoping that fundamentally new types of microelectronic components might help provide impetus for the next big advances. This new technique could be a first step, making machine learning more efficient and easier to deploy on small devices like smartphones.
“A factor of 100 in energy efficiency and in training speed for fully connected layers certainly seems worth further effort,” says Schneider. Not everyone is convinced, however. Kwabena Boahen, who researches computer architectures at Stanford, says the work reminds him of the hype surrounding “memristors,” a tunable type of transistor somewhat analogous to a synapse, which has been underdevelopment for over a decade.
The design of IBM’s chips is also still relatively clunky, consisting of five transistors and three other components where there would be a single transistor on a normal chip. Some aspects of the system, moreover, have so far been tested only in simulation, a common technique for validating microchip designs. IBM will still need to build and test a complete chip. Nevertheless, the work may be a significant, biologically inspired step toward a computer with AI logic burned into its core.
B)Intelligent Machines
This company tames killer robots
Artificial intelligence can turn the most dangerous industrial robots into helpful coworkers, and that could transform manufacturing.
seven-foot-tall robot arm moves in a blur, carrying a piece of metal about the size of a bowling ball from one workbench to another at superhuman speed. But when a human worker reaches for the piece, the robot goes into slow motion and then eventually stops. As soon as the person steps away, the robot speeds up again.
The machine and the young man are assembling a car suspension together. To anyone familiar with industrial robots, this seems insane.
Industrial robots are capable of killing a person. They are normally either caged off inside factories or monitored by a sensor system that shuts everything down if anyone comes within a few feet. Normally, you’d have to be plain nuts to try grabbing something from one.
But this isn’t any normal robot. Developed by Veo Robotics, a startup based in Waltham, Massachusetts, it uses technology that can turn even the most hulking and brutish industrial robot into a safe workmate.
Veo puts several 3-D sensors around an industrial robot. Its software first builds a representation of a scene. It then identifies objects, including people moving around; estimates where things are headed; and controls the robot accordingly. The smallest error could have catastrophic consequences.
Veo’s CEO, Patrick Sobalvarro, says he got the idea while visiting a BMW plant in Spartanburg, South Carolina. Many tasks, such as installing a dashboard, were being done manually because they involve connecting tubes and cables. But the dashboards are heavy and awkward for a person to maneuver around.
“We put a robot in there for the stuff that doesn’t require dexterity,” says Sobalvarro of the suspension demo. “It cuts the manufacturing time in half.”
Hand me the wrench
A new breed of collaborative industrial robot could transform manufacturing by blurring the line between human and machine capabilities.
Robots are powerful and precise, but there are lots of things they cannot easily do, like fine manipulation or work involving flexible objects. Likewise, humans are adept at manipulation, and good at improvising and adapting, but we aren’t very good at moving heavy items hour after hour.
“What manufacturers have now is this situation where if you can’t fully automate it, you don’t get to have anything,” says Clara Vu, VP of engineering at Veo. “What they’d like is to have a robot take a big heavy thing and put it in a precise location, and then a person can come along and go clip, clip, clip.”
Veo’s approach does not rely on cutting-edge machine-learning techniques, because those approaches tend to be less predictable and more difficult to validate (see “The dark secret at the heart of AI”). The technology can be added to a robot work cell, and the machine can then be programmed normally. It will simply go about its work while making sure not to hurt any humans around. Future versions of the system make include efforts to have robots collaborate with people even more closely.
Veo recently demonstrated its systems to a number of other robotics companies. Geoff Lewis of Soft Robotics, another Boston-based startup that’s developing new types of robot grippers, was one of those who attended.
Lewis was impressed with Veo’s technology, and he believes it could lead to a rapid change in attitudes toward safety. “Manufacturing has historically been a stubborn and slow industry to adopt innovative new technologies,” he says. “Yet time and time again you see early adopters gain a competitive edge, and then everyone piles on.”
Playing nicely
Robots have long been a feature of industries such as car making. But they are rapidly spreading to other manufacturing sectors and into warehousing. The Robotic Industries Association, an organization backed by robot makers in the US, reports that robot shipments grew 22 percent in the US in the first quarter of 2018. The International Federation of Robotics, another industry body, estimates that the number of industrial robots in operation worldwide will double from 2014 figures by 2020.
Advances in sensors, computing, and software are changing the way robots can be designed and used. New kinds of warehouse, office, store, and delivery robots are being tested and commercialized thanks to this progress.
A generation of manufacturing robots has appeared in recent years that can work alongside people, but only because they are not powerful enough to cause any harm. Yet this limits not just what they can lift but how precisely they can operate over a significant distance.
Recent leaps in machine learning, meanwhile, seem likely to give robots yet more capabilities. Many researchers are working on ways for robots to learn, through practice and experimentation, how to grasp even awkward and unfamiliar objects (see “This is the most dexterous robot ever created”). And while Veo’s approach does not rely on machine learning, its technology could complement efforts to make robots smarter and more adaptable through that approach.
Willy Shih, a professor at Harvard Business School who studies manufacturing technology, recently agreed to join Veo’s board. He says the company’s potential was too good to pass up: “It’s a good example of applying cheap and abundant computing power in a way that makes machines much more usable.”
source MIT https://www.technologyreview.com
C)The Promise of Artificial Intelligence
Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the Defense Advanced Research Projects Agency to spur development of autonomous vehicles. In 2011, IBM’s Watson system beat out two long time human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances.
Moreover, AI is already having a major positive impact in many different sectors of the global economy and society. For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress.
To showcase the overwhelmingly positive impact of AI, this report describes the major uses of AI and highlights 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI.
The examples in this report cover 14 sectors of the economy and society, yet they only scratch the surface of the many ways that AI is driving innovation, generating substantial social and economic value, and transforming everyday life around the globe. As with any new technology, there will inevitably be detractors who fear change and how it might impact them. While policymakers should respond to legitimate concerns, they should not allow alarmists to delay progress. Instead, they should remain steadfastly focused on accelerating the development and adoption of AI to usher in its many benefits.
read the report
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