The MIT Parkinson’s-tracking effort aims to help clinicians overcome challenges in treating the estimated 10 million people afflicted by the disease globally. Typically, Parkinson’s patients’ motor skills and cognitive functions are evaluated during clinical visits, but these can be skewed by outside factors like tiredness. Add to that fact that commuting to an office is too overwhelming a prospect for many patients, and their situation grows starker.

As an alternative, the MIT team proposes an at-home device that gathers data using radio signals reflecting off of a patient’s body as they move around their home. About the size of a Wi-Fi router, the device, which runs all day, uses an algorithm to pick out the signals even when there are other people moving around the room.

In a study published in the journal Science Translational Medicinethe MIT researchers showed that their device was able to effectively track Parkinson’s progression and severity across dozens of participants during a pilot study. For instance, they showed that gait speed declined almost twice as fast for people with Parkinson’s compared to those without, and that daily fluctuations in a patient’s walking speed corresponded with how well they were responding to their medication.

Moving from healthcare to the plight of whales, the Whale Safe project — whose stated mission is to “utilize best-in-class technology with best-practice conservation strategies to create a solution to reduce risk to whales” — in late September deployed buoys equipped with onboard computers that can record whale sounds using an underwater microphone. An AI system detects the sounds of particular species and relays the results to a researcher, so that the location of the animal — or animals — can be calculated by corroborating the data with water conditions and local records of whale sightings. The whales’ locations are then communicated to nearby ships so they can reroute as necessary.

Image Credits: Benioff Ocean Science Laboratory

Whale Safe currently has buoys deployed in the Santa Barbara Channel near the ports of Los Angeles and Long Beach. In the future, the project aims to install buoys in other American coastal areas including Seattle, Vancouver and San Diego.

Conserving forests is another area where technology is being brought into play. Surveys of forest land from above using lidar are helpful in estimating growth and other metrics, but the data they produce aren’t always easy to read. Point clouds from lidar are just undifferentiated height and distance maps — the forest is one big surface, not a bunch of individual trees. Those tend to have to be tracked by humans on the ground.

Diagram showing how collaborative decision making in which a few cars opt for a longer route actually makes it faster for most. Image Credits: Carnegie Mellon University

The key difference, they argue, is that autonomous vehicles drive “altruistically,” which is to say they deliberately accommodate other drivers — by, say, always allowing other drivers to merge ahead of them. This type of behavior can be taken advantage of, but at a policy level it should be rewarded, they argue, and AVs should be given access to things like toll roads and HOV and bus lanes, since they won’t use them “selfishly.”

Illustration for AlphaTensor. Image Credits: DeepMind

To leverage AlphaTensor, DeepMind converted the problem of finding matrix multiplication algorithms into a single-player game where the “board” is a three-dimensional array of numbers called a tensor. According to DeepMind, AlphaTensor learned to excel at it, improving an algorithm first discovered 50 years ago and discovering new algorithms with “state-of-the-art” complexity. One algorithm the system discovered, optimized for hardware such as Nvidia’s V100 GPU, was 10% to 20% faster than commonly used algorithms on the same hardware.

Perceptron: AI saving whales, steadying gaits and banishing traffic by Kyle Wiggers originally published on TechCrunch