Neither the machine or nor the professional human debaters were given prior knowledge of the subject of the on-stage debate, which took place in San Francisco on Monday.
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Category: robotics/AI – Page 2,141
Nature article presents an AI developed by Google’s Medical Brain team which outperforms hospitals’ own warning system in predicting the death risk among hospit…al patients.
Google’s Medical Brain team is now training its AI to predict the death risk among hospital patients — and its early results show it has slightly higher accuracy than a hospital’s own warning system.
Bloomberg describes the healthcare potential of the Medical Brain’s findings, including its ability to use previously unusable information in order to reach its predictions. The AI, once fed this data, made predictions about the likelihood of death, discharge, and readmission.
In a paper published in Nature in May, from Google’s team, it says of its predictive algorithm:
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Neural networks running on GPUs have achieved some amazing advances in artificial intelligence, but the two are accidental bedfellows. IBM researchers hope a new chip design tailored specifically to run neural nets could provide a faster and more efficient alternative.
It wasn’t until the turn of this decade that researchers realized GPUs (graphics processing units) designed for video games could be used as hardware accelerators to run much bigger neural networks than previously possible.
That was thanks to these chips’ ability to carry out lots of computations in parallel rather than having to work through them sequentially like a traditional CPU. That’s particularly useful for simultaneously calculating the weights of the hundreds of neurons that make up today’s deep learning networks.
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A space launch every 3 hours may soon be possible using rockets carried on a fully autonomous unmanned airplane, a new startup company suggests.
Alabama-based startup Aevum aims to per mission, using an air-launch system called Ravn.
“Ravn is designed to launch every 180 minutes,” Jay Skylus, Aevum’s CEO and chief launch architect, told Space.com. “Other launch vehicles fly only a handful of times a year with an average of 18 months of lead time.” [Rocket Launches: The Latest Liftoffs, Photos & Videos].
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Interplanetary Challenge
Posted in robotics/AI, space travel
IBM Research AI team demonstrated deep neural network (DNN) training with large arrays of analog memory devices at the same accuracy as a Graphical Processing Unit (GPU)-based system. This is a major step on the path to the kind of hardware accelerators necessary for the next AI breakthroughs. Why? Because delivering the Future of AI will require vastly expanding the scale of AI calculations.
Above – Crossbar arrays of non-volatile memories can accelerate the training of fully connected neural networks by performing computation at the location of the data.
This new approach allows deep neural networks to run hundreds of times faster than with GPUs, using hundreds of times less energy.
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It’s not unrealistic to think that 80% of what doctors do will be replaced by algorithms and artificial intelligence. The idea, evangelized by venture capitalist Vinod Khosla two years ago, is that machines can more accurately diagnosis us — and that will reduce deadly medical errors and free doctors up to do other things.
The bottom line: We’re getting closer to this reality. Algorithms, for example, can already diagnose diseases from imaging scans better than human radiologists. Computers possibly could take over the entire radiology specialty.
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RIPPA, a fully autonomous robot, can cover five acres a day on a solar charge — finding and exterminating pests and weeds on every single plant over the equivalent of four football fields. Are robots like RIPPA the future of farming?
RIPPA stands for “Robot for Intelligent Perception and Precision Application”.
Catalyst joins engineers from the Australian Centre for Field Robotics as they explore the world of agriculture to develop robots and smarter ways of farming.
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