A simulation system invented at MIT to train driverless cars creates a photorealistic world with infinite steering possibilities, helping the cars learn to navigate a host of worse-case scenarios before cruising down real streets. Control systems, or "controllers," for autonomous vehicles largely rely on real-world datasets of driving trajectories from human drivers. From these data, they learn how to emulate safe steering controls in a variety of situations. But real-world data from hazardous "edge cases," such as nearly crashing or being forced off the road or into other lanes, are -- fortunately -- rare. Some computer programs, called "simulation engines," aim to imitate these situations by rendering detailed virtual roads to help train the controllers to recover.

This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Like any tool, technology can be used for both good and bad. And sometimes, that bad is inadvertent; tech in the form of airplanes helped expedite the spread of the coronavirus around the world. But fortunately, technology will also aid in stopping this pandemic crisis. A few weeks ago, we wrote about how the San Francisco-based company BlueDot utilized artificial intelligence (AI) to warn the general public about the dangers of COVID-19 well ahead of health officials. In case you missed it, you can read it here. Examples like BlueDot give us hope that emerging tools like AI can help humanity tackle problems like the coronavirus in unprecedented ways.

You may not be surprised that 81.5% of the U.S. population search for health information online. Actually, around 70% of them treat the internet as the first source to get healthcare information. However, a large number of fake health news is poisoning the online environment. For example, a popular health news piece, "Ginger is 10,000x more effective at killing cancer than chemo," which generated around 1 million engagements on Facebook, turned out to be a misleading claim. These statements are threatening public health.

A man in Cyprus had to walk his dog during the country's COVID-19 shutdown and decided to see if a drone could handle the job. Vakis Demetriou shared his experiment on his Instagram, showing his dog Oliver walking up a residential street with a small drone following close behind holding the leash. '5th day quarantine,' he wrote. 'Stay home safe but don't forget your dog's happiness.' While the video is charming, it's unlikely a drone would be able to walk most dogs, even those as small and lightweight as Oliver.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Centers for Disease Control and Prevention (CDC) announced a new bot over the weekend that it says can analyze COVID-19 symptoms and recommend a course of action in an attempt to aid an overwhelmed healthcare system. The bot, known as Clara, utilizes Microsoft's Azure Healthcare Bot framework, which the government agency hopes will help allay the fears of people who believe they may have the virus. "Microsoft's Healthcare Bot service is one solution that uses artificial intelligence (AI) to help the CDC and other frontline organizations respond to these inquiries, freeing up doctors, nurses, administrators and other healthcare professionals to provide critical care to those who need it," Microsoft wrote in a statement in conjunction with the CDC announcement.

Face and Image Recognition is not only about security and surveillance or controlling the quality of industrial production processes. The technology is proving increasingly impactful to the fashion and beauty industries, generating multiple exciting opportunities for manufacturers and consumers alike. Face and Image recognition being an AI frontrunner in terms of security, agriculture, and industrial QA, the technology's business uses beyond these three realms are still much less known. As a result, many businesses in industries other than security and surveillance, agriculture, and industrial production have barely given any thought to employing Image Recognition as a means of attaining better capabilities to raise their sights and achieve higher levels of quality and profitability. Meanwhile, the Image Recognition- inspired and - enabled opportunities, which have been cropping up of late elsewhere, can barely be ignored and should be taken note of by a much, much wider audience.