AI News, D-Wave Founder's New Startup Combines AI, Robots, and Monkeys in Exo-Suits

D-Wave Founder's New Startup Combines AI, Robots, and Monkeys in Exo-Suits

As if quantum computing wasn’t mind-bending enough, one of D-Wave Systems’ founders is now pursuing another futuristic idea: using artificial intelligence and high-tech exoskeleton suits to allow humans—and, at least according to one description of the technology, monkeys—to control and train an army of intelligent robots.

Geordie Rose is a cofounder and chief technology officer of D-Wave, the Canadian company selling machines that it claims exploit quantum mechanical effects to solve certain problems hundreds of millions times faster than traditional computers.

“An operator may include a non-human animal such as a monkey,” says the patent, “and the operator interface may be…re-sized to account for the differences between a human operator and a monkey operator.” (This isn’t the first device to enable monkeys to direct robots, but previous research has focused on brain-machine interfaces, not robot control and autonomy.) The application goes into some detail about the operator interface, a wearable robotic suit [image, right]that includes head and neck motion sensors, devices to capture arm movements, and haptic gloves.

“Although a wealth of information included in human brains for performing various human executable tasks is available, robotic-related devices used to execute these tasks have historically not utilized this information or not made good use of it,” says Kindred’s patent document.

“Device control instructions and environment sensor information generated over…multiple runs may…be used to derive autonomous control information which may be used to facilitate autonomous behavior in an autonomous device,” says the patent application.

The document suggests that Kindred will manage this using “deep hierarchical learning algorithms” such as a conditional deep belief network (CDBN) or a conditional restricted Boltzmann machine(CRBM), a type of powerful recurrent neural network.

Quantum computing firm D-Wave says that the operation of its system is “analogous to a…restricted Boltzmann machine,” and that its research team is “working to exploit the parallels between these architectures to substantially accelerate learning in deep, hierarchical neural networks.” In 2010, Geordie Rose coauthored a paper that claimed a quantum computer could perform some types of machine learning applications more efficiently than software on a traditional computer.

a set of verbal communications…to be played through a speaker on the [robot]).” While it is unclear how far along Kindred is in actually building the telerobotics system described in its patent application, the document does include renderings of 3D models of the exo-suit, details of some components, and photos of glove assemblies and robotic tracks.

The next big leap in AI could come from warehouse robots

Ask Geordie Rose and Suzanne Gildert, co-founders of the startup Kindred, about their company’s philosophy, and they’ll describe a bold vision of the future: machines with human-level intelligence.

Gildert, a physicist who conceived Kindred in 2013 while working with Rose at quantum computing company D-Wave, thinks giving AI a physical body is the only way to make real progress toward a true thinking machine.

Because so many warehouse workers today spend a significant amount of time sorting products and scanning barcodes, Kindred developed a robotic arm that can do some elements automatically.

Meanwhile, humans step in when needed to manually operate the robot to perform tasks that are difficult for machines, like gripping a single product from a cluster of different items.

Because the company gathers data every time a human uses the Orb, engineers are able to improve its software over time using techniques such as reinforcement learning, which improves software through repetition.

Rather, they believe the greatest strides will come from programs running inside a physical robot can gain knowledge about the world and itself from the ground up, like a human infant does from birth.

Other more conventional forms of “weak” or “narrow” AI include the underlying software behind Netflix and Amazon recommendations, Snapchat camera effects that rely on facial recognition, and Google’s fast and accurate language translations.

This is the fundamental challenge of AGI: how to create an intelligent system, the kind we know only from science fiction, that can truly learn on its own without needing to be fed thousands of examples and trained over the course of weeks or months.

“One of the things we’re most excited about is not just that it can play Go better, but we hope that this’ll actually lead to technologies that are more generally applicable to other challenging domains,” DeepMind co-founder and CEO Demis Hassabis said at the event last week.

“Our founding belief was that in order to make real progress toward the original objectives of AI, you needed to start by grounding your ideas in the physical world,” Rose says.

“And that means robots, and robots with sensors that can look around, touch, hear the world that surrounds them.” This body-first approach to AI is based on a theory called embodied cognition, which suggests that the interplay between our brain, body, and the physical world is what produces elements of consciousness and the ability to reason.

(A fun exercise here is thinking about how many common metaphors have physical underpinnings, like thinking of affection as warmth or something inconceivable as being “over your head.”) Without understanding how the brain developed to control the body and guide functions like locomotion and visual processing, the theory goes, we may never be able to reproduce it artificially.

By giving its robot sensory abilities and motor functions and then using AI training techniques, the BRETT team devised a way for it to acquire knowledge and physical skills much faster than with standard programming — and with the flexibility to keep learning.

Much like how babies are constantly adjusting their behavior when attempting something new, BRETT also approaches unique problems, fails at first, and then adjusts over repeated attempts and under new constraints.

Applying AI to real-world tasks like picking up objects and stacking blocks involves tackling a whole suite of new problems, Tobin says, like managing unfamiliar textures and replicating minute motor movements.

Wojciech Zaremba, who leads OpenAI’s robotics work, says that a holy grail of sorts would be a general-purpose robot powered by AI that can learn a new task — scrambling eggs, for instance — by watching someone do it just once.

This is why OpenAI is working on teaching robots new skills that are first demonstrated by a human in a simulated VR environment, much like a video game, where it’s much easier and less costly to produce and collect data.

“And then if you want the robot to replicate this behavior, you just download the file.” When I first operated the Orb, on an April afternoon in Kindred’s San Francisco warehouse space, a group of six or so engineers were scattered about testing the robotic arms with various pink-colored bins of products — vitamin bottles, soft plastic cylinders of Lysol cleaning wipes, rolls of paper towels.

Amazon is working on something similar, and the company now holds an annual “picking” challenge to spur development in industrial robotics that are capable of handling and sorting physical items.

Max Bennett, Kindred’s robotics product manager, says that the process is designed so that human warehouse workers can operate multiple Orbs simultaneously, gripping objects and letting the software take the reins before cycling to the next setup.

D-Wave founder to unveil secretive Artificial Intelligence robot exoskeleton project

Kindred Systems filed a patent in August relating to the creation of a robotic exoskeleton attached to an array of sensors, with the intention of teaching robots working remotely to mimic the exoskeleton wearer’s actions until the robots learn to reproduce the action.

The Kindred robot is presented as a 1.2-meter-tall humanoid, potentially covered in synthetic skin, rolling around on wheeled treads, having at least two arms with hands or grippers, and at least one camera on its head streaming high-definition video to its operator, with other sensors detecting a range of environmental information, including infrared and ultraviolet visuals, a radiation detector, GPS, and strain or touch sensors.

Last week, at D-Wave’s inaugural users group conference in Santa Fe, New Mexico, the company announced its next-generation system, using a 2000-qubit processor, effectively doubling the number of qubits capacity of the previous generation D-Wave 2X system, which the company sold to Google and NASA in 2013 for $15 million.

Last November, Gilderts was spotted at a ladies-only “Battle of the Bots” event during which she expressed a belief that robots are evolving Artificial Intelligence, and revealed that she had been working on a Machine Learning system for robots to learn behaviours through data mining and to recognize patterns for decision-making.

The Download

Following a four-day trial, lawyers representing Uber and Waymosay they have suddenly come to a settlement in their suit overthe theft of autonomous-car technology.

Uber denies wrongdoing: In a statement,UberCEO Dara Khosrowshahi said he doesn’t believe any trade secrets ever made their way from Waymo to Uber, adding, “Nor do we believe that Uber has used any of Waymo’s proprietary information in its self-driving technology.”

Google got annoyed when Uber started building driverless cars, then downright mad when it poached staff and, allegedly, secrets—and now it has a small part of the ride-hailer in its back pocket.

Canadian smart-robot maker scores $28-million in Tencent-led venture deal

Kindred Systems Inc., a smart-robot manufacturing startup co-founded by Canadian inventor and theoretical physicist Geordie Rose, has secured $28-million (U.S.) in a venture financing deal led by Chinese internet giant Tencent Holdings Ltd., which is quickly emerging as one of the most active foreign investors in the North American startup space.

Kindred, which is attempting to build C-3PO-like robots that can think for themselves and perform tasks previously done by humans – including sorting and packing orders in e-commerce distribution centres – also said Tuesday that it has begun pilot programs with major global retailers, including Gap Inc., in their existing fulfilment centres using the startup's first commercial offering, a product it calls Kindred Sort.

The company has been training its robots to physically interact with the world in part by having human operators – sometimes with a mouse, and sometimes wearing a robot suit – remotely control the machines to perform tasks such as sorting.

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