Boston Dynamics has made a lot of headlines in recent times not only for the way they test their robots but also for the bloopers they have made during live demonstrations. Despite the fact that Boston Dynamics has made some of the most advanced robots the world has seen, the company has been mocked for being the maker of viral videos, not robots.
From Videos To Real World
Boston Dynamic’s Robot Dog Spot has been watched with great interest by tech enthusiasts. From its resemblance to a robot dog from Black Mirror to its acrobatic agility, the robot gained fame swiftly.
Cops have long had dogs, and robots, to help them do their jobs. And now, they have a robot dog.
Massachusetts State Police is the first law enforcement agency in the country to use Boston Dynamics’ dog-like robot, called Spot. While the use of robotic technology is not new for state police, the temporary acquisition of Spot — a customizable robot some have called “terrifying” — is raising questions from civil rights advocates about how much oversight there should be over police robotics programs.
The state’s bomb squad had Spot on loan from the Waltham-based Boston Dynamics for three months starting in August until November, according to records obtained by the American Civil Liberties Union of Massachusetts and reviewed by WBUR.
Uncovering trolls and malicious or spammy accounts on social media is increasingly difficult as the miscreants find more and more ways to camouflage themselves as seemingly legitimate. Writing in the International Journal of Intelligent Engineering Informatics, researchers in India have developed an algorithm based on ant-colony optimization that can effectively detect accounts that represent a threat to normal users.
Asha Kumari and Balkishan Department of Computer Science and Applications at Maharshi Dayanand University, in Rohtak, India, explain that the connections between twitter users are analogous to the pheromone chemical communication between ants and this can be modeled in an algorithm based on how ant colonies behave to reveal the strongest connections in the twitter network and so uncover the accounts that one might deem as threatening to legitimate users.
The team’s tests on their system were successful in terms of precision, recall, f-measure, true-positive rate, and false-positive rate based on 26 features examined by the system played against almost 41,500 user accounts attracted to honeypots. Moreover, they report that the approach is superior to existing techniques. The team adds that they hope to be able to improve the system still further by adding so-called machine learning into the algorithm so that it can be trained to better identify threatening accounts based on data from known threats and legitimate accounts.
Over the last few years, rapid progress in AI has enabled our smartphones, social networks, and search engines to understand our voice, recognize our faces, and identify objects in our photos with very good accuracy. These dramatic improvements are due in large part to the emergence of a new class of machine learning methods known as Deep Learning.
Animals and humans can learn to see, perceive, act, and communicate with an efficiency that no Machine Learning method can approach. The brains of humans and animals are “deep”, in the sense that each action is the result of a long chain of synaptic communications (many layers of processing). We are currently researching efficient learning algorithms for such “deep architectures”. We are currently concentrating on unsupervised learning algorithms that can be used to produce deep hierarchies of features for visual recognition. We surmise that understanding deep learning will not only enable us to build more intelligent machines but will also help us understand human intelligence and the mechanisms of human learning. http://www.cs.nyu.edu/~yann/research/deep/
Park Las Vegas, sponsored by Bleutech Park Properties, Inc. is breaking ground in the Las Vegas Valley in December 2019 as the first city in the world to boast a digital revolution in motion, redefining the infrastructure industry sector. This $7.5 billion, six year project, will be constructed of net-zero buildings within their own insular mini-city, featuring automated multi-functional designs, renewable energies from solar/wind/water/kinetic, autonomous vehicles, artificial intelligence (AI), augmented reality, Internet of Things (IoT), robotics, supertrees, and self-healing concrete structures.
Bleutech Park’s mixed-use environment featuring workforce housing, offices, retail space, ultra-luxury residential, hotel and entertainment will introduce a new high-tech biome to the desert valley.
When you hear the word “cyborg,” scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgs —humans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the body —are already among us.
But innovators are pushing the human-machine boundary even further. While prosthetic limbs are tied in with a person’s nervous system, future blends of biology and technology may be seen in computers that are wired into our brains.
Our ability to technologically enhance our physical capabilities—the “hardware” of our human systems, you could say—will likely reshape our social world. Will these changes bring new forms of dominance and exploitation? Will unaltered humans be subjected to a permanent underclass or left behind altogether? And what will it mean to be human—or will some of us be more than human?
AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react to situations you never anticipated.
Which programming language should you learn to plumb the depths of AI? You’ll want a language with many good machine learning and deep learning libraries, of course. It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages. That’s a long list of requirements, but there are still plenty of good options.
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The mentioned blog post on the gradients and its notebook are available here: Post: https://www.wandb.com/articles/exploring-gradients Notebook: https://colab.research.google.com/drive/1bsoWY8g0DkxAzVEXRigrdqRZlq44QwmQ
📝 The paper “Solving Rubik’s Cubewith a Robot Hand” is available here: https://openai.com/blog/solving-rubiks-cube/
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