Lifeboat Foundation

Mayo Clinic researchers have proposed a new model for mapping the symptoms of Alzheimer’s disease to brain anatomy. This model was developed by applying machine learning to patient brain imaging data. It uses the entire function of the brain rather than specific brain regions or networks to explain the relationship between brain anatomy and mental processing. The findings are reported in Nature Communications.

“This new model can advance our understanding of how the brain works and breaks down during aging and Alzheimer’s disease, providing new ways to monitor, prevent and treat disorders of the mind,” says David T. Jones, M.D., a Mayo Clinic neurologist and lead author of the study.

Alzheimer’s disease typically has been described as a protein-processing problem. The toxic proteins amyloid and tau deposit in areas of the brain, causing neuron failure that results in clinical symptoms such as memory loss, difficulty communicating and confusion.

Only surprised there are games left for AI to beat people at.

Victory marks milestone for AI as bridge requires more human skills than other strategy games.

Nvidia’s AI model is pretty impressive: a tool that quickly turns 2D snapshots into a 3D-rendered scene. The tool is called Nvidia Instant NeRF, referring to “neural radiance fields”.

Nvidia’s AI model is pretty impressive: a tool that quickly turns a collection of 2D snapshots into a 3D-rendered scene. The tool is called Instant NeRF, referring to “neural radiance fields”.

Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. The Nvidia research team has developed an approach that accomplishes this task almost instantly making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering.

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Science_Hightech — operanewsapp.

Quantum computing startups are all the rage, but it’s unclear if they’ll be able to produce anything of use in the near future.

As a buzzword, quantum computing probably ranks only below AI in terms of hype. Large tech companies such as Alphabet, Amazon, and Microsoft now have substantial research and development efforts in quantum computing. A host of startups have sprung up as well, some boasting staggering valuations. IonQ, for example, was valued at $2 billion when it went public in October through a special-purpose acquisition company. Much of this commercial activity has happened with baffling speed over the past three years.

I am as pro-quantum-computing as one can be: I’ve published more than 100 technical papers on the subject, and many of my PhD students and postdoctoral fellows are now well-known quantum computing practitioners all over the world. But I’m disturbed by some of the quantum computing hype I see these days, particularly when it comes to claims about how it will be commercialized.

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DARPA, the innovation arm of the U.S. military, wants artificial intelligence to make battlefield medical decisions, raising red flags from some experts and ethicists.

On top of the environmental concerns, Japan has an added motivation for this push towards automation —its aging population and concurrent low birth rates mean its workforce is rapidly shrinking, and the implications for the country’s economy aren’t good.

Thus it behooves the Japanese to automate as many job functions as they can (and the rest of the world likely won’t be far behind, though they won’t have quite the same impetus). According to the Nippon Foundation, more than half of Japanese ship crew members are over the age of 50.

In partnership with Misui OSK Lines Ltd., the foundation recently completed two tests of autonomous ships. The first was a 313-foot container ship called the Mikage, which sailed 161 nautical miles from Tsuruga Port, north of Kyoto, to Sakai Port near Osaka. Upon reaching its destination port the ship was even able to steer itself into its designated bay, with drones dropping its mooring line.

What most people define as common sense is actually common learning, and much of that is biased.

The biggest short term problem in AI: as mentioned in the video clip, an over-emphasis on data set size, irrelevant of accuracy, representation or accountability.

The biggest long term problem in AI: Instead of trying to replace us we should be seeking to complement us. Merge is not necessary nor advisable.

Continue reading “The biggest problem in AI? Machines have no common sense” »