Lifeboat Foundation

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Japan sold a fully-functional robot suit that doesn’t just resemble a combat mech from a video game, it was able to actually fight like one too. With video games like “Armored Core,” mech fans already have a good idea of what giant fighting robots would be like in real life. One such fan is Kogoro Kurata –- an artist who wasn’t satisfied with leaving that notion to the imagination. Kurata explained that driving these giant robots was his dream, saying it was something the “Japanese had to do” (via Reuters). Unlike some of the coolest modern robots available today, Kurata envisioned a full-sized mech he can actually enter and operate: Something he actually accomplished in 2012.

Continue reading “The Japanese Robot Suit That Looks Straight Out Of Armored Core” »

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 training 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 hardware, 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.

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.

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.

IStock/ Volodymyr Horbovyy.

Not only did the research demonstrate that equipping E. coli bacteria with artificial components is possible, but the scientists could also precisely navigate the bots remotely using magnets.

You’ve probably heard of it by now: ChatGPT, a new AI powered chatbot has taken the Internet by storm. And it’s good. Scary good.

We asked it to make us a shopping list for a dinner party, write some recipes, and estimate the cost of the ingredients.

It did it.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

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]

Making sure our machines understand the intent behind our instructions is an important problem that requires understanding intelligence itself.