The hardware largely remains the same, but performance gets a boost. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Without much fanfare, Apple has unveiled three new flagship products today via a press release--no special event, no pre-recorded show. That might be because the new iPad Pro, MacBook Pro, and Vision Pro don't change the mold--they're identical to their predecessors--but internally, they're debuting Apple's highly anticipated M5 chip.

We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.

While we don't know much about the Radeon Vega Mobile GPU yet, it's not exactly a surprising announcement. Gamers have been waiting eagerly to see when AMD's new graphics hardware would make it into high-powered laptops. In October, the company revealed that Vega was coming to its new Ryzen mobile processors. It was only a matter of time until it had a more powerful dedicated offering. AMD is also positioning it as something you'd find in ultrathin notebooks, and not just chunky gaming machines.

Gene-expression microarrays, commonly called gene chips, make it possible to simultaneously measure the rate at which a cell or tissue is expressing--translating into a protein--each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells; identify novel targets for drug design; and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. This article describes microarray technology, the data it produces, and the types of machine learning tasks that naturally arise with these data.

CHIP's highest-level goals were programmed C and runs on board. The RAP system is designed to deal with achieving goals in a dynamic environment. Each RAP task description encodes a set of methods for carrying out the task in different situations, a success check to tell when the task has accomplished its purpose, and notations that describe when things are not going as expected. At run time, a RAP task examines its methods and selects one that is appropriate in the current situation. By doing method selection at run time, RAPs are more likely to select the best method, even if the world is changing or contains details that cannot be predicted in advance.

Designing specialized processing chips for artificial intelligence is becoming common for serious tech companies, and Tesla, it seems, is no exception. According to reports from both The Register and CNBC, CEO Elon Musk was talking up the company's custom AI chips at machine learning conference NIPS last night, telling attendees that Tesla is "developing specialized AI hardware that we think will be the best in the world." "I wanted to make it clear that Tesla is serious about AI, both on the software and hardware fronts," said Musk, according to The Register. "We are developing custom AI hardware chips". We'd heard rumors about Tesla building its own AI chips before, with a CNBC report in September claiming that the company had more than 50 people working on the project.

This morning at the WSJ's D.Live event, Intel formally unveiled its Nervana Neural Network Processor (NNP) family of chips designed for machine learning use cases. Intel has previously alluded to these chips using the pre-launch name Lake Crest. The technology underlying the chips is heavily tied to Nervana Systems, a deep learning hardware startup Intel purchased last August for $350 million. Intel's NNP chips nix standard cache hierarchy and use software to manage on-chip memory to achieve faster training times for deep learning models. Intel has been scrambling in recent months to avoid being completely leveled by Nvidia.

Huawei announced its Mate 10 series in Munich Monday, showcasing three models of the device, which features the new Kirin 970 chip. The SoC is the first on the market to introduce a dedicated NPU or Neural Network Processing Unit to enable artificial intelligence capabilities natively on the device. The Huawei Mate 10 will be available to markets including Spain, UAE, Saudi Arabia, Malaysia, Singapore and Australia in late October for €699, while the Mate 10 Pro will be available to markets including Germany, France, Italy, UAE, Saudi Arabia, Malaysia, Singapore, and Thailand in November for €799. The Porsche Design Mate 10 will also be available to select markets in mid-November for €1395. With the Mate 10 series, Huawei is promoting the transition from the smartphone to the "intelligent machine."

NVIDIA GPUs have been on the forefront of accelerated neural network processing and are the de facto standard for accelerated neural network research and development (R&D) plus deep learning training. At the NVIDIA GPU Technology Conference (GTC) in Beijing China earlier this week, the company maneuvered to also become the de facto standard for accelerated neural network inference deployment. At GTC Beijing, NVIDA lined up the major Chinese cloud companies for AI computing: Alibaba Cloud, Baidu Cloud, and Tencent Cloud. At GTC-Beijing, it announced inference designs with Alibaba Cloud, Tencent, Baidu Cloud, JD.com, and iFlytek.