ENVIRONMENT


Making robots see

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"There is a fundamental disconnect between what we roboticists say and what the public perceives," says Ian Reid, deputy director of the Australian Centre for Robotic Vision, in Brisbane. And that leads to the heart of the problem, and what researchers mean when they talk about "robotic vision": using cameras to guide robots to carry out tasks in increasingly uncontrolled environments. Is this another of Ian Reid's "disconnects" between the research world and the public's sci-fi driven expectations? "In rich countries like Japan where there are also demographic challenges, you will see a big increase in social robotics – in aged, robotic companions and robotic pets," Mahony predicts.


Curiosity could help artificially intelligent machines advance

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A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn't immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an "intrinsic curiosity model" to make their learning algorithm work even when there isn't a strong feedback signal. The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, has pioneered, over the past several years, the development of computer programs and robots that exhibit simple forms of inquisitiveness.


Why AI won't replace all human data analysts

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When most people think of artificial intelligence, they think of a coldly rational decision maker, lacking in emotion -- like Data, the fictional android from Star Trek. But as AI and machine learning have progressed, algorithms have become incredibly good at pattern recognition, and have started to act more biologically -- more like instincts based on experience than decisions based on logic. The work of an analyst, however, does not just involve conducting data analysis within closed environments. Like a manager, every human will have a task force of AI, pattern matching and conducting closed environment analysis.


Curiosity could help artificially intelligent machines advance

#artificialintelligence

A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn't immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an "intrinsic curiosity model" to make their learning algorithm work even when there isn't a strong feedback signal. The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, has pioneered, over the past several years, the development of computer programs and robots that exhibit simple forms of inquisitiveness.


The 6 top machine learning trends

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The complexity, as well as the number of active servers to manage, has increased significantly, resulting in a much larger amount of collected data to sort through and track. Despite the increase in instrumentation capabilities and the amount of collected data, enterprises barely use significantly larger data sets to improve availability and performance process effectiveness with root cause analysis and incident prediction. This field studies how to design algorithms that can learn by observing data, discovering new insights in data, developing systems that can automatically adapt and customize themselves, and designing systems where it is too complicated and costly to implement all possible circumstances (such as search engines and self-driving cars). Many organizations are finding that machine learning allows them to better analyze large amounts of data, gain valuable insights, reduce incident investigation time, determine which alerts are correlated, and what causes event storms – and even prevent incidents from happening in the first place.


How Artificial Intelligence will Transform IT Operations and DevOps

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One of the biggest challenges IT operations and DevOps teams face nowadays is being able to pinpoint the small yet potentially harmful issues in large streams of Big Data being logged in their environment. The human mind is no longer capable of keeping up with the velocity, volume, and variety of Big Data streaming through daily operations, making AI a powerful and essential tool for optimizing the analyzing and decision-making processes. DevOps engineers, IT Operations managers, CTOs, VP engineering, and CISO face numerous challenges, which can be mitigated effectively by integrating AI in log analysis and related operations. Quickly find the needle in the "IT operations" haystack and eliminate the main problems Using AI driven log analytics systems, it becomes considerably easy to find the needle in the haystack, and efficiently solve issues.


Cinematography on the fly

MIT News

But a team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable. Then, on the fly, it generates control signals for a camera-equipped autonomous drone, which preserve that framing as the actors move. With our solution, if the subject turns 180 degrees, our drones are able to circle around and keep focus on the face. The researchers tested the system at CSAIL's motion-capture studio, using a quadrotor (four-propeller) drone.


15 tech trends in autonomous cars, artificial intelligence, and machine learning for 2017

AITopics Original Links

Tech is about to change dramatically next year. Want to stay up to date? In each section, a designer explains all of the details. Which one is already on your radar? I want to hear your viewpoints, so drop me an email.


Collaborative Language Grounding Toward Situated Human-Robot Dialogue

AI Magazine

One particular challenge is to ground human language to robot internal representation of the physical world. Although copresent in a shared environment, humans and robots have mismatched capabilities in reasoning, perception, and action. A robot not only needs to incorporate collaborative effort from human partners to better connect human language to its own representation, but also needs to make extra collaborative effort to communicate its representation in language that humans can understand. This article gives a brief introduction to this research effort and discusses several collaborative approaches to grounding language to perception and action.


U.S. intelligence agencies envision the world in 2035

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By 2035, developers will have learned to automate many jobs. Investments in artificial intelligence (A.I.) and robotics will surge, displacing workers. And a more connected world will increase -- not reduce -- differences, increasing nationalism and populism, according to a new government intelligence assessment prepared just in time for President-elect Donald Trump's administration. The "Global Trends" report, unveiled Monday, is produced every four years by the National Intelligence Council. It is released just before the inauguration of a new or returning president.