Lifeboat Foundation

Welcome to the future.

In the first experiment of its kind, scientists have been able to translate brain signals directly into intelligible speech. It may sound like wild science fiction at first, but this feat could actually help some people with speech issues.

And yes, we could also get some futuristic computer interfaces out of this.

Continue reading “Scientists Have Found a Way to Convert Human Brain Signals Directly Into Speech” »

Last year, Microsoft Corp.’s Azure security team detected suspicious activity in the cloud computing usage of a large retailer: One of the company’s administrators, who usually logs on from New York, was trying to gain entry from Romania. And no, the admin wasn’t on vacation. A hacker had broken in.

Microsoft quickly alerted its customer, and the attack was foiled before the intruder got too far.

Chalk one up to a new generation of artificially intelligent software that adapts to hackers’ constantly evolving tactics. Microsoft, Alphabet Inc.’s Google, Amazon.com Inc. and various startups are moving away from solely using older “rules-based” technology designed to respond to specific kinds of intrusion and deploying machine-learning algorithms that crunch massive amounts of data on logins, behavior and previous attacks to ferret out and stop hackers.

Continue reading “Microsoft, Google Use Artificial Intelligence to Fight Hackers” »

Generative adversarial networks are not just good for causing mischief. They can also show us how AI algorithms “think.”

#OpenAccess #FullArticle The results of a new clinical trial have shown the safety and efficacy of the interoperable Artificial Pancreas System smartphone app (iAPS), which can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms. The clinical trial and the app, which runs on an unlocked smartphone, are described in an article published in Diabetes Technology & Therapeutics (DTT), a peer-reviewed journal from Mary Ann Liebert, Inc., publishers.

Diabetes Technology & TherapeuticsVol. 21, No. 1Original ArticlesFree AccessSunil Deshpande,…

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google’s search, Facebook’s news feed, and Netflix’s recommendation engine—and are transforming industries like health care and education.

Our study of 25 years of artificial-intelligence research suggests the era of deep learning is coming to an end.

A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMind’s AlphaZero conquer chess and Go could tackle an even more complex task—training a robotic knee to help amputees walk smoothly.

Computer algorithms help prosthetics wearers walk within minutes rather than requiring hours of training.

An artificial intelligence solution (AI) can accurately identify precancerous changes that could require medical attention in images from a woman’s cervix. Researchers from the National Institutes of Health and Global Good developed the computer algorithm, which is called automated visual evaluation.

Researchers created the algorithm by using more than 60,000 cervical images from a National Cancer Institute (NCI) archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.

More than 9,400 women participated in that population study, with follow up that lasted up to 18 years. Because of the prospective nature of the study, the researchers said that they gained nearly complete information on which cervical changes became pre-cancers and which did not.

Continue reading “Can AI Really Be a Game Changer in Cervical Cancer Screenings?” »

One of the most powerful techniques in machine learning, generative adversarial networks, has been tested on a quantum computer for the first time.

Buried deep within the DNA of Asian individuals is a genetic clue pointing to the existence of an unknown human ancestor. Remarkably, it wasn’t a human who reached this startling conjecture, but rather an artificially intelligent algorithm. Welcome to archaeology in the 21st century.

New research published last week in Nature Communications suggests a yet-to-be discovered hominid interbred with modern humans tens of thousands of years ago. This mystery species eventually went extinct, but an AI developed by researchers from the Institute of Evolutionary Biology (IBE) and several other European institutions found traces of its existence in the DNA of present-day people with Asian ancestry. A press release issued by the Centre for Genomic Regulation said it’s the first time deep learning has been used to explain human history, “paving the way for this technology to be applied in other questions in biology, genomics and evolution.”