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Category: robotics/AI – Page 1097

Research in the field continues to focus on seizure prevention, prediction and treatment. Dr. Van Gompel predicts that the use of artificial intelligence and machine learning will help neurologists and neurosurgeons continue to move toward better treatment options and outcomes.

“I think we will continue to move more and more toward removing less and less brain,” says Dr. Van Gompel. “And in fact, I do believe in decades, we’ll understand stimulation enough that maybe we’ll never cut out brain again. Maybe we’ll be able to treat that misbehaving brain with electricity or something else. Maybe sometimes it’s drug delivery, directly into the area, that will rehabilitate that area to make it functional cortex again. That’s at least our hope.”

On the Mayo Clinic Q&A podcast, Dr. Van Gompel discusses the latest treatment options for epilepsy and what’s on the horizon in research.

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Continuous-time neural networks are one subset of machine learning systems capable of taking on representation learning for spatiotemporal decision-making tasks. Continuous differential equations are frequently used to depict these models (DEs). Numerical DE solvers, however, limit their expressive potential when used on computers. The scaling and understanding of many natural physical processes, like the dynamics of neural systems, have been severely hampered by this restriction.

Inspired by the brains of microscopic creatures, MIT researchers have developed “liquid” neural networks, a fluid, robust ML model that can learn and adapt to changing situations. These methods can be used in safety-critical tasks such as driving and flying.

However, as the number of neurons and synapses in the model grows, the underlying mathematics becomes more difficult to solve, and the processing cost of the model rises.

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Chemists have created nanorobots propelled by magnets that remove pollutants from water. The invention could be scaled up to provide a sustainable and affordable way of cleaning up contaminated water in treatment plants.

Martin Pumera at the University of Chemistry and Technology, Prague, in the Czech Republic and his colleagues developed the nanorobots by using a temperature-sensitive polymer material and iron oxide. The polymer acts like tiny hands that can pick up and dispose of pollutants in the water, while the iron oxide makes the nanorobots magnetic. The researchers also added oxygen and hydrogen atoms to the iron oxide that can attach onto target pollutants.

The robots are about 200 nanometres wide and are powered by magnetic fields, which allow the team to control their movements.

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Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

A supercomputer, providing massive amounts of computing power to tackle complex challenges, is typically out of reach for the average enterprise data scientist. However, what if you could use cloud resources instead? That’s the rationale that Microsoft Azure and Nvidia are taking with this week’s announcement designed to coincide with the SC22 supercomputing conference.

Nvidia and Microsoft announced that they are building a “massive cloud AI computer.” The supercomputer in question, however, is not an individually-named system, like the Frontier system at the Oak Ridge National Laboratory or the Perlmutter system, which is the world’s fastest Artificial Intelligence (AI) supercomputer. Rather, the new AI supercomputer is a set of capabilities and services within Azure, powered by Nvidia technologies, for high performance computing (HPC) uses.

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Self-driving car company Argo AI failure when Ford and VW pulled the plug after spending over $3 billion. It is big evidence that Lidar-dependent self-driving has a long way to go. All of the self-driving car companies except Tesla and Comma were using Lidar. Ford said removing the driver is over 5 years away. Most robotaxi players are dependent upon removing the driver for their business model to work enough to get to serious scale. 5+ years to get to the true starting point and 5+ years to scale translates to an 8-year lead for Tesla if Tesla solves robotaxi in 2 years. Uber had a 2.5 year lead over Lyft and that meant three times the market share for Uber.

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Would you like to see the classic magic trick of a rabbit being pulled out of a hat? I hope so since you are about to witness something ostensibly magical, though it has to do with Artificial Intelligence (AI) rather than rabbits and hats.

Here’s the deal.

A lot of debate takes place about whether we ought to recognize AI with some form of legal personhood. Surprisingly, some believe that we can already shoehorn AI into legal personhood by a bit of corporate legal wrangling. See what this is all about.

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