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Category: robotics/AI – Page 1,202

Deep-learning models have proven to be highly valuable tools for making predictions and solving real-world tasks that involve the analysis of data. Despite their advantages, before they are deployed in real software and devices such as cell phones, these models require extensive training in physical data centers, which can be both time and energy consuming.

Researchers at Texas A&M University, Rain Neuromorphics and Sandia National Laboratories have recently devised a new system for deep learning models more efficiently and on a larger scale. This system, introduced in a paper published in Nature Electronics, relies on the use of new training algorithms and memristor crossbar , that can carry out multiple operations at once.

“Most people associate AI with health monitoring in smart watches, face recognition in smart phones, etc., but most of AI, in terms of energy spent, entails the training of AI models to perform these tasks,” Suhas Kumar, the senior author of the study, told TechXplore.

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Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) have completed a joint research study using federated learning – a distributed machine learning (ML) artificial intelligence (AI) approach – to help international healthcare and research institutions identify malignant brain tumours.

The largest medical federated learning study to date with an unprecedented global dataset examined from 71 institutions across six continents, the project demonstrated the ability to improve brain tumour detection by 33%.

“Federated learning has tremendous potential across numerous domains, particularly within healthcare, as shown by our research with Penn Medicine,” says Jason Martin, principal engineer at Intel Labs. “Its ability to protect sensitive information and data opens the door for future studies and collaboration, especially in cases where datasets would otherwise be inaccessible.

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Musicians, we have some bad news. AI-powered music generators are here — and it looks like they’re gunning for a strong position in the content-creation industry.

“From streamers to filmmakers to app builders,” claims music generating app Mubert AI, which can transform limited text inputs into a believable-sounding composition, “we’ve made it easier than ever for content creators of all kinds to license custom, high-quality, royalty-free music.”

Of course, computer-generated music has been around for quite some time, making use of various forms of artificial intelligence to come up with results that can sound equally manmade and alien.

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Artificial Intelligence vol. 4 — The Rise of the Machines.

01. Intro — Roy meets Tyrell.
02. Vangelis — Los Angeles, November 2019 [01:08]
03. Mahindra Waves — DNA [03:41]
04. Between Interval — Sea of Darkness [09:00]
05. Carl Sagan’s last Interview — The Warning [11:50]
06. Sam Hulick (Mass Effect OST) — Normandy [12:52]
07. Kammarheit — Provenience [14:10]
08. Vataff Project — Owl [18:03]
09. Field Rotation — Tiefflug [24:50]
10. Juno Reactor — Nitrogen Part 1 [31:28]
11. Mono Junk — Enter [38:30]
12. Gus Gus vs. T-world — Esja [43:10]
13. Aphex Twin — On [51:10]
14. Sephira — Memory Access [56:40]
15. HECQ — 8 [01:00:20]
16. Distant System — Pupillary response [01:01:20]
17. Blastromen — Follow The Command [01:03:20]
18. Blastromen — Battlenet [01:09:50]
19. Asura — Regenesis [01:16:53]
20. Field Rotation — Regenzeit [01:21:50]
21. Vangelis — Blade Runner (End Titles) [01:26:20]

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Light-weight and flying robots the size of small insects could have highly valuable real-world applications, for instance supporting search & rescue missions, inspections of hazardous sites, and even space exploration.

Despite their potential, the realization of these robots has so far proved difficult, particularly due to encountered when trying to stabilize their flight and artificially replicate the innate hovering capabilities of insects.

Researchers at University of Washington have recently developed a flight control and wind sensing system that could help to tackle this challenging robotics problem, finally enabling the stable flight of robots even as small as a gnat. This system, introduced in Science Robotics, is based on the use of accelerometers, a sensor that can measure the acceleration of any moving device, object or body.

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“It’s very impressive, the performance they’re able to achieve on some pretty challenging problems,” said Dr. Armando Solar-Lezama at MIT, who was not involved in the research.

The problems AlphaCode tackled are far from everyday applications—think of it more as a sophisticated math tournament in school. It’s also unlikely the AI will take over programming completely, as its code is riddled with errors. But it could take over mundane tasks or offer out-of-the-box solutions that evade human programmers.

Perhaps more importantly, AlphaCode paves the road for a novel way to design AI coders: forget past experience and just listen to the data.

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