AI & RoboticsNews

MIT presents AI frameworks that compress models and encourage agents to explore

In a pair of papers accepted to the International Conference on Learning Representations (ICLR) 2020, MIT researchers investigated new ways to motivate software agents to explore their environment and pruning algorithms to make AI apps run faster. Taken together, the twin approaches could foster the development of autonomous industrial, commercial, and home machines that require less computation…
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AI & RoboticsNews

Amazon’s AI uses a microphone array to localize multiple speakers in a room

In a technical paper scheduled to be presented next month at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), a group of Amazon researchers propose an AI-driven approach to multiple-source localization, or the problem of estimating a sound’s location using microphone audio. They say that in experiments involving real and simulated data (the former from the…
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AI & RoboticsNews

The surge of sensationalist COVID-19 AI research

There seems to be a tendency to hastily use imperfect and questionable data to train an AI solution for COVID-19, a dangerous trend that not only does not help any patient or physician but also damages the reputation of the AI community. Dealing with a pandemic — as…
AI & RoboticsNews

AI Weekly: AI models illustrate the importance of continued social distancing

As the COVID-19 pandemic rages on unabated in countries around the world, there’s a shared desire among those forced to shelter in place to see the extent to which social distancing is slowing the disease’s spread. It’s understandable — collateral damage from government-imposed business closures threatens to devastate entire industries. As of this week, 26 million Americans have filed for…
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AI & RoboticsNews

Google claims its AI can design computer chips in under 6 hours

In a preprint paper coauthored by Google AI lead Jeff Dean, scientists at Google Research and the Google chip implementation and infrastructure team describe a learning-based approach to chip design that can learn from past experience and improve over time, becoming better at generating architectures for unseen components. They claim it completes designs in under six hours on average, which is…
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