What 17 prominent roboticists think Google should do with its robots

From IEEE Spectrum:

These days, whenever a group of roboticists gets together to talk shop, the subject almost inevitably turns to Google and its secretive robotics division. What are those guys up to?

The curiosity is understandable. It’s been nearly three years since Google made its huge move into robotics by acquiring an impressive and diverse group of companies, including Meka and Redwood Robotics, Industrial Perception, Bot & Dolly, Holomni, Autofuss, Schaft, Reflexxes, and, most notably, Boston Dynamics. Google’s robotics division, which has some of the world’s brightest robotics engineers and some of the most advanced robotics hardware ever built, has been working quietly at various secluded locations in California, Massachusetts, and Tokyo, and details about their plans have been scarce. Earlier this year, following Google’s reorganization as Alphabet, the robotics unit became part of X, Alphabet’s experimental technology lab, or as the company calls it, its “moonshot factory.”

The search for moonshots will probably continue to be a matter of intense debate internally. For those outside the company, it’s a captivating issue, and everyone seems to have a different opinion on what Alphabet could or should do with its robots. In fact, some roboticists encouraged us to gather those opinions in a public place to stimulate the debate. We liked the idea, so we contacted nearly 50 robotics people with a variety of different backgrounds and asked them the following question:

If you were in charge of Google’s robotics division and you had all those robotics companies at your disposal, what would you do? What kinds of robots would you build and for what markets?

The answers.

Trajectory data mining: an overview by Yu Zheng from Microsoft Research

From Microsoft Research :


The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a roadmap from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

Artificially intelligent hackers

From The Guardian:

Could you invent an autonomous hacking system that could find and fix vulnerabilities in computer systems before criminals could exploit them, and without any human being involved ?

That’s the challenge faced by seven teams competing in Darpa’s Cyber Grand Challenge in August.

Each of the teams has already won $750,000 for qualifying and must now put their hacking systems up against six others in a game of “capture the flag”. The software must be able to attack the other team’s vulnerabilities as well as find and fix weaknesses in their own software – all while protecting its performance and functionality. The winning team will walk away with $2m.

“Fully automated hacking systems are the final frontier. Humans can find vulnerabilities but can’t analyse millions of programs,” explained Giovanni Vigna, a professor of computer science at University of California Santa Barbara, speaking at the RSA security conference in San Francisco.

Atlas, the next generation

From Youtube:

A new version of Atlas, designed to operate outdoors and inside buildings. It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5' 9" tall (about a head shorter than the DRC Atlas) and weighs 180 lbs.

Powerful speech technology from China’s leading Internet company

From MIT Technology Review:

Powerful speech technology from China’s leading Internet company makes it much easier to use a smartphone.

A growing number of China’s 691 million smartphone users now regularly dispense with swipes, taps, and tiny keyboards when looking things up on the country’s most popular search engine, Baidu. China is an ideal place for voice interfaces to take off, because Chinese characters were hardly designed with tiny touch screens in mind. But people everywhere should benefit as Baidu advances speech technology and makes voice interfaces more practical and useful. That could make it easier for anyone to communicate with the machines around us.

Artificial intelligence safety - podcast

Giving robots the ability to feel textures like humans do

From TheTalkingMachines.com:

In episode four of season two, we talk about some of the major issues in AI safety, (and how they’re not really that different from the questions we ask whenever we create a new tool.) One place you can go for other opinions on AI safety is the Future of Life Institute. We take a listener question about time series and we talk with Nick Patterson of the Broad Institute about everything from ancient DNA to Alan Turing. If you're as excited about AlphaGo playing Lee Sedol at Nick is, you can get details on the match on DeepMind's You Tube channel March 5th through the 15th.


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