Nvidia has topped an independent AI Inference benchmarking test called MLPerf. The test measures AI performance in data-centres and at the edge (on your phone, in robots, etc.) in several categories, all of which Nvidia managed to top. Much like children, AI needs to be taught to do things before it's allowed into the wild. This is done by first training an AI model in a supercomputer, where it is brought up on a diet of data. The idea is that the AI munches through this data and figures out how to deduce information from it, in other words, the AI learns to infer.

The Hot Chips 31 Symposium started this week. AI (artificial intelligence) was the hot topic among participants Intel (INTC), NVIDIA (NVDA), and Advanced Micro Devices (AMD). The stocks of these three chip companies rose 1.18%, 2%, and 3.2%, respectively, on August 21 as they discussed their AI strategies. It is essential for long-term investors to understand the three companies' AI opportunity. All three have a different approach to tapping AI, so their AI TAM (total addressable value) is also different.

Amazon is one of the largest buyers of chips from Intel and Nvidia, whose semiconductors help power Amazon's booming cloud computing unit, Amazon Web Services. But Amazon has started to design its own chips. Amazon's so-called "Inferentia" chip announced on Wednesday will help with what researchers call inference, which is the process of taking an artificial intelligence algorithm and putting it to use, for example by scanning incoming audio and translating that into text-based requests. The Amazon chip is not a direct threat to Intel and Nvidia's business because it will not be selling the chips. Amazon will sell services to its cloud customers that run atop the chips starting next year.

In this video, Information Technology Subcommittee Chairman Will Hurd begins a three-part hearing on Artificial Intelligence. The IT Subcommittee will identify and define criteria to guide government understanding and adoption of AI. Over the next three months, the IT Subcommittee will hear from industry professionals as well as government stakeholders with the goal of working together to keep the United States the world leader in artificial intelligence technology," said Chairman Hurd. "We thank IT Subcommittee Chairman Will Hurd for holding this important series of hearings. Artificial intelligence technology is rapidly developing, and it is critical that we ensure our workforce and government are poised to take advantage of the significant benefits that it will bring.

The race between two key chip makers to put self-driving cars on the streets is getting heated. And it's no less entertaining, involving the likes of BB8, a self-driving Nvidia car named after a droid in Star Wars, and the company's automotive supercomputer called Xavier, named after an X-men superhero. These two playfully named products are a big part of Nvidia's ambitious plans to put a fleet of self-driving cars on the streets by 2020. Nvidia is collaborating with Audi to develop these autonomous cars, which will be based on the Drive-PX computer. Nvidia's announcement comes just a day after Intel and BMW said they were putting 40 self-driving cars on the street by the end of this year.

There's a race to put more computing power in self-driving cars, and it's shaping up to be eerily similar to an earlier battle between Intel and AMD to crank up PC horsepower. Intel at CES announced the powerful Go computer with up to 28 Xeon chips so self-driving cars can cruise the streets safely. Beyond Xeon, Go will also be available with either next-generation Atom chips or 5G connectivity. The first 40 self-driving BMW cars based on the Go will hit the streets in tests this year. Autonomous cars need a lot of computational power under the hood to avoid accidents and make smart driving decisions.

In less than 12 hours, three different people offered to pay me if I'd spend an hour talking to a stranger on the phone. All three said they'd enjoyed reading an article I'd written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn't say who it was. The requests came from what are called expert networks--research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google's AI processor would affect the chip market.

In less than 12 hours, three different people offered to pay me if I'd spend an hour talking to a stranger on the phone. All three said they'd enjoyed reading an article I'd written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn't say who it was. The requests came from what are called expert networks--research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google's AI processor would affect the chip market.