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Category: information science – Page 292

A robotic leg, born without prior knowledge, learns to walk

For a newborn giraffe or wildebeest, being born can be a perilous introduction to the world—predators lie in wait for an opportunity to make a meal of the herd’s weakest member. This is why many species have evolved ways for their juveniles to find their footing within minutes of birth.

It’s an astonishing evolutionary feat that has long inspired biologists and roboticists—and now a team of USC researchers at the USC Viterbi School of Engineering believe they have become the first to create an AI-controlled robotic limb driven by animal-like tendons that can even be tripped up and then recover within the time of the next footfall, a task for which the was never explicitly programmed to do.

Francisco J. Valero-Cuevas, a professor of Biomedical Engineering a professor of Biokinesiology & Physical Therapy at USC in a project with USC Viterbi School of Engineering doctoral student Ali Marjaninejad and two other doctoral students—Dario Urbina-Melendez and Brian Cohn, have developed a bio-inspired algorithm that can learn a new walking task by itself after only 5 minutes of unstructured play, and then adapt to other tasks without any additional programming.

Waking Up with Sam Harris

James Hughes : “Great convo with Yuval Harari, touching on algorithmic governance, the perils of being a big thinker when democracy is under attack, the need for transnational governance, the threats of automation to the developing world, the practical details of UBI, and a lot more.”

In this episode of the Waking Up podcast, Sam Harris speaks with Yuval Noah Harari about his new book 21 Lessons for the 21st Century. They discuss the importance of meditation for his intellectual life, the primacy of stories, the need to revise our fundamental assumptions about human civilization, the threats to liberal democracy, a world without work, universal basic income, the virtues of nationalism, the implications of AI and automation, and other topics.

Yuval Noah Harari has a PhD in History from the University of Oxford and lectures at the Hebrew University of Jerusalem, specializing in world history. His books have been translated into 50+ languages, with 12+ million copies sold worldwide. Sapiens: A Brief History of Humankind looked deep into our past, Homo Deus: A Brief History of Tomorrow considered far-future scenarios, and 21 Lessons for the 21st Century focuses on the biggest questions of the present moment.

Twitter: @harari_yuval

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The World’s Most Valuable AI Companies, and What They’re Working On

Artificial intelligence and its subset of disciplines—such as machine learning, natural language processing, and computer vision —are seemingly becoming integrated into our daily lives whether we like it or not. What was once sci-fi is now ubiquitous research and development in company and university labs around the world.

Similarly, the startups working on many of these AI technologies have seen their proverbial stock rise. More than 30 of these companies are now valued at over a billion dollars, according to data research firm CB Insights, which itself employs algorithms to provide insights into the tech business world.

Private companies with a billion-dollar valuation were so uncommon not that long ago that they were dubbed unicorns. Now there are 325 of these once-rare creatures, with a combined valuation north of a trillion dollars, as CB Insights maintains a running count of this exclusive Unicorn Club.

The subset of AI startups accounts for about 10 percent of the total membership, growing rapidly in just 4 years from 0 to 32. Last year, an unprecedented 17 AI startups broke the billion-dollar barrier, with 2018 also a record year for venture capital into private US AI companies at $9.3 billion, CB Insights reported.

AI startup investments graph cbinsights Peter Rejcek AI

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Google’s Doodle celebrates Russian mathematician Olga Ladyzhenskaya’s 97th birth anniversary

Google’s Doodle on Thursday marked the 97th birth anniversary of Russian mathematician Olga Ladyzhenskaya. She was known for her work on partial differential equations and in the field of fluid dynamics, which led to several developments in the study of fluid dynamics and paved the way for advances in weather forecasting, oceanography, aerodynamics, and cardiovascular science.

The Math That Takes Newton Into the Quantum World

In my 50s, too old to become a real expert, I have finally fallen in love with algebraic geometry. As the name suggests, this is the study of geometry using algebra. Around 1637, René Descartes laid the groundwork for this subject by taking a plane, mentally drawing a grid on it, as we now do with graph paper, and calling the coordinates x and y. We can write down an equation like x + y = 1, and there will be a curve consisting of points whose coordinates obey this equation. In this example, we get a circle!

It was a revolutionary idea at the time, because it let us systematically convert questions about geometry into questions about equations, which we can solve if we’re good enough at algebra. Some mathematicians spend their whole lives on this majestic subject. But I never really liked it much until recently—now that I’ve connected it to my interest in quantum physics.

If we can figure out how to reduce topology to algebra, it might help us formulate a theory of quantum gravity.

Intel Unveils the Intel Neural Compute Stick 2 at Intel AI Devcon Beijing for Building Smarter AI Edge Devices

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What’s New: Intel is hosting its first artificial intelligence (AI) developer conference in Beijing on Nov. 14 and 15. The company kicked off the event with the introduction of the Intel® Neural Compute Stick 2 (Intel NCS 2) designed to build smarter AI algorithms and for prototyping computer vision at the network edge. Based on the Intel® Movidius™ Myriad™ X vision processing unit (VPU) and supported by the Intel® Distribution of OpenVINO™ toolkit, the Intel NCS 2 affordably speeds the development of deep neural networks inference applications while delivering a performance boost over the previous generation neural compute stick. The Intel NCS 2 enables deep neural network testing, tuning and prototyping, so developers can go from prototyping into production leveraging a range of Intel vision accelerator form factors in real-world applications.

“The first-generation Intel Neural Compute Stick sparked an entire community of AI developers into action with a form factor and price that didn’t exist before. We’re excited to see what the community creates next with the strong enhancement to compute power enabled with the new Intel Neural Compute Stick 2.” –Naveen Rao, Intel corporate vice president and general manager of the AI Products Group

New milestones in helping prevent eye disease with Verily

Over the last three years, Google and Verily—Alphabet’s life sciences and healthcare arm—have developed a machine learning algorithm to make it easier to screen for disease, as well as expand access to screening for DR and DME. As part of this effort, we’ve conducted a global clinical research program with a focus on India. Today, we’re sharing that the first real world clinical use of the algorithm is underway at the Aravind Eye Hospital in Madurai, India.

Google and Verily share updates to their initiative to diagnose diabetic eye disease leveraging machine learning.

Researchers develop a fleet of 16 miniature cars for cooperative driving experiments

A team of researchers at The University of Cambridge has recently introduced a unique experimental testbed that could be used for experiments in cooperative driving. This testbed, presented in a paper pre-published on arXiv, consists of 16 miniature Ackermann-steering vehicles called Cambridge Minicars.

“Using true-scale facilities for vehicle testbeds is expensive and requires a vast amount of space,” Amanda Prorok. “Our main objective was to build a low-cost, multi-vehicle that is easy to maintain and that is easy to use to prototype new algorithms. In particular, we were interested in testing and tangibly demonstrating the benefits of cooperative driving on multi-lane road topographies.”

Studies investigating cooperative driving are often expensive and time consuming due to a lack of available low-cost platforms that researchers can use to test their systems and algorithms. Prorok and her colleagues thus set out to develop an effective and inexpensive experimental that could ultimately support research into cooperative driving and multi-car navigation.

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