Tag Archives: engineers
Robotics has come a long way in the past few years. Robots can now fetch items from specific spots in massive warehouses, swim through the ocean to study marine life, and lift 200 times their own weight. They can even perform synchronized dance routines.
But the really big question is—can robots put together an Ikea chair?
A team of engineers from Nanyang Technological University in Singapore decided to find out, detailing their work in a paper published last week in the journal Science Robotics. The team took industrial robot arms and equipped them with parallel grippers, force-detecting sensors, and 3D cameras, and wrote software enabling the souped-up bots to tackle chair assembly. The robots’ starting point was a set of chair parts randomly scattered within reach.
As impressive as the above-mentioned robotic capabilities are, it’s worth noting that they’re mostly limited to a single skill. Putting together furniture, on the other hand, requires using and precisely coordinating multiple skills, including force control, visual localization, hand-eye coordination, and the patience to read each step of the manual without rushing through it and messing everything up.
Indeed, Ikea furniture, while meant to be simple and user-friendly, has left even the best of us scratching our heads and holding a spare oddly-shaped piece of wood as we stare at the desk or bed frame we just put together—or, for the less even-tempered among us, throwing said piece of wood across the room.
It’s a good thing robots don’t have tempers, because it took a few tries for the bots to get the chair assembly right.
Practice makes perfect, though (or in this case, rewriting code makes perfect), and these bots didn’t give up so easily. They had to hone three different skills: identifying which part was which among the scattered, differently-shaped pieces of wood, coordinating their movements to put those pieces in the right place, and knowing how much force to use in various steps of the process (i.e., more force is needed to connect two pieces than to pick up one piece).
A few tries later, the bots were able to assemble the chair from start to finish in about nine minutes.
On the whole, nicely done. But before we applaud the robots’ success too loudly, it’s important to note that they didn’t autonomously assemble the chair. Rather, each step of the process was planned and coded by engineers, down to the millimeter.
However, the team believes this closely-guided chair assembly was just a first step, and they see a not-so-distant future where combining artificial intelligence with advanced robotic capabilities could produce smart bots that would learn to assemble furniture and do other complex tasks on their own.
Future applications mentioned in the paper include electronics and aircraft manufacturing, logistics, and other high-mix, low-volume sectors.
Image Credit: Francisco Suárez-Ruiz and Quang-Cuong Pham/Nanyang Technological University Continue reading
A Brain-Boosting Prosthesis Moves From Rats to Humans
Robbie Gonzalez | WIRED
“Today, their proof-of-concept prosthetic lives outside a patient’s head and connects to the brain via wires. But in the future, Hampson hopes, surgeons could implant a similar apparatus entirely within a person’s skull, like a neural pacemaker. It could augment all manner of brain functions—not just in victims of dementia and brain injury, but healthy individuals, as well.”
Here’s How the US Needs to Prepare for the Age of Artificial Intelligence
Will Knight | MIT Technology Review
“The Trump administration has abandoned this vision and has no intention of devising its own AI plan, say those working there. They say there is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes… That looks like a huge mistake. If it essentially ignores such a technological transformation, the US might never make the most of an opportunity to reboot its economy and kick-start both wage growth and job creation. Failure to plan could also cause the birthplace of AI to lose ground to international rivals.”
Underwater GPS Inspired by Shrimp Eyes
Jeremy Hsu | IEEE Spectrum
“A few years ago, U.S. and Australian researchers developed a special camera inspired by the eyes of mantis shrimp that can see the polarization patterns of light waves, which resemble those in a rope being waved up and down. That means the bio-inspired camera can detect how light polarization patterns change once the light enters the water and gets deflected or scattered.”
POLITICS & TECHNOLOGY
‘The Business of War’: Google Employees Protest Work for the Pentagon
Scott Shane and Daisuke Wakabayashi | The New York Times
“Thousands of Google employees, including dozens of senior engineers, have signed a letter protesting the company’s involvement in a Pentagon program that uses artificial intelligence to interpret video imagery and could be used to improve the targeting of drone strikes.
The letter, which is circulating inside Google and has garnered more than 3,100 signatures, reflects a culture clash between Silicon Valley and the federal government that is likely to intensify as cutting-edge artificial intelligence is increasingly employed for military purposes. ‘We believe that Google should not be in the business of war,’ says the letter, addressed to Sundar Pichai, the company’s chief executive. It asks that Google pull out of Project Maven, a Pentagon pilot program, and announce a policy that it will not ‘ever build warfare technology.’ (Read the text of the letter.)”
MIT’s New Headset Reads the ‘Words in Your Head’
Brian Heater | TechCrunch
“A team at MIT has been working on just such a device, though the hardware design, admittedly, doesn’t go too far toward removing that whole self-consciousness bit from the equation. AlterEgo is a headmounted—or, more properly, jaw-mounted—device that’s capable of reading neuromuscular signals through built-in electrodes. The hardware, as MIT puts it, is capable of reading ‘words in your head.’”
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Founded by ex-iRobot engineers, this startup is building a remote presence robot powered by sophisticated sensors and autonomous navigation Continue reading
Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
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These Japanese humanoids can replicate flexible human-like movement during physical workouts like push-ups, crunches, stretches and other whole-body exercises, to help researchers better understand how humans move during athletic sports, aid in the development of artificial limbs and whole bodies, … Continue reading