Lifeboat Foundation

To get a better look at the world around them, animals constantly are in motion. Primates and people use complex eye movements to focus their vision (as humans do when reading, for instance); birds, insects, and rodents do the same by moving their heads, and can even estimate distances that way. Yet how these movements play out in the elaborate circuitry of neurons that the brain uses to “see” is largely unknown. And it could be a potential problem area as scientists create artificial neural networks that mimic how vision works in self-driving cars.

To better understand the relationship between movement and vision, a team of Harvard researchers looked at what happens in one of the brain’s primary regions for analyzing imagery when animals are free to roam naturally. The results of the study, published Tuesday in the journal Neuron, suggest that image-processing circuits in the primary visual cortex not only are more active when animals move, but that they receive signals from a movement-controlling region of the brain that is independent from the region that processes what the animal is looking at. In fact, the researchers describe two sets of movement-related patterns in the visual cortex that are based on head motion and whether an animal is in the light or the dark.

The movement-related findings were unexpected, since vision tends to be thought of as a feed-forward computation system in which visual information enters through the retina and travels on neural circuits that operate on a one-way path, processing the information piece by piece. What the researchers saw here is more evidence that the visual system has many more feedback components where information can travel in opposite directions than had been thought.

AI is emerging as a driving technology behind the internet of things (IoT). Learn about the new AIoT, and how it will impact the future.

On the higher end, they work to ensure that development is open in order to work on multiple cloud infrastructures, providing companies the ability to know that portability exists.

That openness is also why deep learning is not yet part of a solution. There is still not the transparency needed into the DL layers in order to have the trust necessary for privacy concerns. Rather, these systems aim to help manage information privacy for machine learning applications.

Artificial intelligence applications are not open, and can put privacy at risk. The addition of good tools to address privacy for data being used by AI systems is an important early step in adding trust into the AI equation.

Facebook is using cutting-edge artificial intelligence to extract valuable data from images. The company showed Digital Trends some of its ongoing work.

Robert Behnken and Chris Cassidy concluded their spacewalk at 12:41 p.m. EDT, after five hours and 29 minutes. The two NASA astronauts completed a number of tasks designed to upgrade International Space Station systems.

They began by installing a protective storage unit that includes two Robotic External Leak Locator (RELL) units the Canadian Space Agency’s Dextre robot can use to detect leaks of ammonia, which is used to operate the station’s cooling system.

Behnken and Cassidy then removed two lifting fixtures at the base of station solar arrays on the near port truss, or backbone, of the station. The “H-fixtures” were used for ground processing of the solar arrays prior to their launch.

AI air-fights, Xbox Series S and more!

GPT-3, the largest artificial intelligence language model, is trained on an estimated 45 terabytes of text data run through 175 billion parameters. It can do more than just autocomplete, like generate code and write stories, just like a human — but it can make errors like a human too.

In hopes of helping clinicians pinpoint different stages of the disease as it advances.

In the end, we look back at our careers and reflect on what we’ve achieved. It may have been the hundreds of human interactions we’ve had; the thousands of emails read and replied to; the millions of minutes of physical labor—all to keep the global economy ticking along.

According to Gallup’s World Poll, only 15 percent of people worldwide are actually engaged with their jobs. The current state of “work” is not working for most people. In fact, it seems we as a species are trapped by a global work crisis, which condemns people to cast away their time just to get by in their day-to-day lives.

Technologies like artificial intelligence and automation may help relieve the work burdens of millions of people—but to benefit from their impact, we need to start changing our social structures and the way we think about work now.