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Age of RobotsAge of Robots

Age of Robots January 2018

Age of Robots magazine is dedicated to reporting on the technology of the Second Machine Age and how it is impacting our society and psychology. We aim to keep readers abreast of the many advances taking place in artificial intelligence, robotics, and associated fields.

국가:
Australia
언어:
English
출판사:
This Side of the Cross Pty Ltd
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the editor

W Autonomous vehicle because was of elcome a new anything development to we year our ’re third in in to for the go issue an is Age exciting advancing by and , of then Robots the ride challenges strap at . in If a 2018 2017 rapid in— ! pace, with Uber, Apple, Intel, Google, and Bosch joining all the major vehicle manufacturers in the race. It seems the technology may be closer to realisation on our streets than we thought, though government bureaucracy and local transport laws are likely to put the brakes on (unless you happen to be a resident of Dubai, a city embracing new tech every chance it gets). Elon Musk wants to take us to Mars—in the meantime, (given the availability of launch/ landing…

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in brief

Long Live The Photonic Qubit Scientists of the Quantum Dynamics Division at the Max Planck Institute of Quantum Optics have demonstrated the long-lived storage of a photonic qubit on a single atom trapped in an optical resonator.“The new technique allows us to preserve the quantum nature of the stored bit for more than 100 milliseconds,” says Matthias Körber, a doctoral candidate at the experiment. “Although an envisioned global quantum network which allows for secure and reliable transport of quantum information still demands a lot of research, the long-lived storage of quantum bits is one of the key technologies, and we believe that the current improvements will bring us a significant step closer to its realization.”Source: MPQGraphic: Christoph Hohmann Brain-on-a-chip Lawrence Livermore National Laboratory (LLNL)…

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ai building ai better than we can

Google’s new real-time computer vision system bests anything created by humans. Google Brain researchers Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc Le have been experimenting with an AI system that can create AI systems better than we can. They also have reports of remarkably accurate real-time object identification, illustrated in the image below.In May 2017, the team introduced their AutoML project, a machine-learning approach that automates the design of machine-learning models using reinforcement learning. While they found that AutoML could design small neural networks that performed on a par with neural networks designed by human experts, these results were constrained to small academic datasets. The team wanted to know how the method would perform on larger, more challenging datasets such as ImageNet, which contains many thousands…

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machine learning detects marketing and sale of opioids on twitter

Yadira Galindo is a communications and media relations manager at UC San Diego Health, focusing on sharing compelling stories about the latest medical treatments. U a machine sing cross-disciplinary advanced learning, team of University of California San Diego researchers developed technology that mined Twitter to identify entities illegally selling prescription opioids online.Between June and November 2015, some 619,937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone and hydrocodone were collected. The findings, published online in the American Journal of Public Health in October, detected 1,778 posts that were marketing the sale of controlled substances, 90 percent included hyperlinks to online sites for purchase.“An unhealthy use of prescription and nonprescription opioid drugs continues to rise in the United States. Public policy and law enforcement efforts are trying to…

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speedy collision detector could make robots better human assistants

E San University lectrical Diego engineers have of developed California at the a faster collision-detection algorithm that uses machine learning to help robots avoid moving objects and weave through complex, rapidly changing environments in real time. The algorithm, dubbed “Fastron”, runs up to 8 times faster than existing collision-detection algorithms.Fastron simulation: the autonomous arm (blue arm) reaches the target configuration (wireframe arm) while avoiding the motions of a human-controlled arm (red arm). Image courtesy of ARClab at UC San Diego.A team of engineers, led by Michael Yip (in the image below), a professor of electrical and computer engineering and member of the Contextual Robotics Institute at UC San Diego, will present the new algorithm at the first annual Conference on Robot Learning, Nov. 13 to 15 at Google headquarters…

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machine learning

A Cambridge University team from , Los the of Alamos National Laboratory, and Boston University identified a hidden signal leading up to earthquakes and used this ‘fingerprint’ to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal. Generally speaking, pinpointing where an earthquake will occur is fairly straightforward: if an earthquake has struck a particular place before, the chances are it will strike there again. The questions that have challenged scientists for decades are how to pinpoint when an earthquake will occur, and how severe it will be. Over the past 15 years, advances in…

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