Good news for folks looking to learn about the latest AI development techniques: Nvidia is now allowing the general public to access the online workshops it provides through its Deep Learning Institute (DLI). The GPU giant today announced today that selected workshops in the DLI catalog will be open to everybody. These workshops previously were available only to companies that wanted specialized training for their in-house developers, or to folks who had attended the company's GPU Technology Conferences. Two of the open courses will take place next month, including "Fundamentals of Accelerated Computing with CUDA Python," which explores developing parallel workloads with CUDA and NumPy and cost $500. There is also "Applications of AI for Predictive Maintenance," which explores technologies like XGBoost, LSTM, Keras, and Tensorflow, and costs $700.

Hardware makers have figured out that enterprises are the best way to make money off of VR and AR, not consumers. NVIDIA, a company that does both things well but has been particularly strong on the business side lately, has just opened up its Holodeck "intelligent" VR platform to select designers and developers. First unveiled in May, it allows for photorealistic graphics, haptics, real-world physics and multi-user collaboration. That helps engineers and designers build and interact with photorealistic people, objects and robots in a fully simulated environment. The idea is to get new hardware prototyped in as much detail as possible before building real-world models.

A deep neural network's ability to teach itself is a strength, because the machine gets better with experience, and a weakness, because it's got no code that an engineer can tweak. That's why the creators of Google Deep Mind's AlphaGo couldn't explain how it played the game of Go. All they could do was watch their brainchild rise from beginner status to defeat one of the best players in the world. If a robocar makes a mistake, engineers must be able to look under the hood, find the flaw and fix it so that the car never makes the same mistake again. One way to do this is through simulations that first show the AI one feature and then show it another, thus discovering which things affect decision making.