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Researchers from North Carolina State University have discovered that teaching physics to neural networks enables those networks to better adapt to chaos within their environment. The work has implications for improved artificial intelligence (AI) applications ranging from medical diagnostics to automated drone piloting.

Neural networks are an advanced type of AI loosely based on the way that our brains work. Our natural neurons exchange electrical impulses according to the strengths of their connections. Artificial neural networks mimic this behavior by adjusting numerical weights and biases during training sessions to minimize the difference between their actual and desired outputs. For example, a neural network can be trained to identify photos of dogs by sifting through a large number of photos, making a guess about whether the photo is of a dog, seeing how far off it is and then adjusting its weights and biases until they are closer to reality.

The drawback to this neural network training is something called “chaos blindness”—an inability to predict or respond to chaos in a system. Conventional AI is chaos blind. But researchers from NC State’s Nonlinear Artificial Intelligence Laboratory (NAIL) have found that incorporating a Hamiltonian function into neural networks better enables them to “see” chaos within a system and adapt accordingly.

A case of pink eye is now reason to be tested for COVID-19, according to University of Alberta researchers.

Coughing, fever and difficulty breathing are common symptoms of the illness, but a recent case study involving an Edmonton woman and published in the Canadian Journal of Ophthalmology has determined that conjunctivitis and keratoconjunctivitis can also be primary symptoms.

In March, a 29-year-old woman arrived at the Royal Alexandra Hospital’s Eye Institute of Alberta with a severe case of conjunctivitis and minimal respiratory symptoms. After the patient had undergone several days of treatment with little improvement—and after it had been determined that the woman had recently returned home from Asia—a resident ordered a COVID-19 test. The test came back positive.

Honeywell stock doesn’t trade on quantum fundamentals yet. Shares are down about 16% year to date, worse than the comparable drops of the S&P 500 and Dow Jones Industrial Average. Honeywell is a large aerospace supplier, and the commercial aviation business has been hammered by Covid-19. Boeing (BA) stock, for instance, is off more than 40% year to date.

Honeywell stock is flat in early Friday trading. The S&P is up about 0.8%.

The quantum-computing industry hasn’t yet arrived, despite today’s announcement. But quantum computers are already better than regular computers in certain instances. Google parent Alphabet (GOOGL) demonstrated the ability of its rudimentary quantum computer to beat traditional systems.

https://transhumanist-party.org/

Circa 2015

What takes coders months, CSAIL’s “Helium” can do in an hour.

It will replacing one of the military’s aging missiles.

A team of researchers claim to have achieved quantum teleportation using individual electrons.

Quantum teleportation, or quantum entanglement, allows particles to affect each other even if they aren’t physically connected — a phenomenon predicted by famed physicist Albert Einstein.

Rather than a teleportation chamber out of a sci-fi movie, quantum teleportation transports information rather than matter.

How can we train self-driving vehicles to have a deeper awareness of the world around them? Can computers learn from past experiences to recognize future patterns that can help them safely navigate new and unpredictable situations?

These are some of the questions researchers from the AgeLab at the MIT Center for Transportation and Logistics and the Toyota Collaborative Safety Research Center (CSRC) are trying to answer by sharing an innovative new open dataset called DriveSeg.

Through the release of DriveSeg, MIT and Toyota are working to advance research in autonomous driving systems that, much like human perception, perceive the driving environment as a continuous flow of visual information.

Computers are renowned for flexibility, running everything from game consoles to stock exchanges. But at the level of computation, most computers rely on arrays of identical processors called cores. Now, a team at Princeton University has built a hardware platform that allows different kinds of computer cores to fit together, allowing designers to customize systems in new ways.

The goal is to create new systems that parcel out tasks among specialized cores, increasing efficiency and speed.

On top of multi–core collaboration, even more gains are achievable when cores needn’t all rely on the same basic programming code that tells a core how to handle its processing jobs. Designers call this basic code an Instruction Set Architecture (ISA). Well-established ISAs include Intel x86, commonly found in laptops, ARM in smartphones, and POWER in IBM mainframes. Besides mixing together cores specialized for different ISAs, researchers are also interested in developing hybrid ISAs to underpin new processor designs, exploiting the potential of new, cutting-edge, open-source ISAs like RISC-V ISA.