We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference.

Intel's 14th-gen mobile processor, code-named Meteor Lake, represents a dramatic shift from the chips Intel has shipped for years -- and Intel has provided us with a ton of information. If you don't have time to wade through it all, however, we've summed up the most important points here. Here's what you need to know about Meteor Lake. For a much more detailed examination of what this new generation of processors offers, please see our deep-dive into Meteor Lake. Intel will brand Meteor Lake as the Core Ultra, and it will launch on Dec. 14, Intel chief executive Pat Gelsinger said at its Intel Innovation conference.

This new family incorporates an upgraded hard processor system (HPS), Enhanced DSP with AI Tensor Block, MIPI I/O support, and a hardened time-sensitive network controller (TSN). Intel Agilex D-Series devices also keep features from previous families like the 2nd-generation Intel Hyperflex FPGA Architecture and high-speed SerDes transceivers. The new Intel Agilex device family inherits many of the most important architectural features of earlier Intel Agilex devices including the second-generation Intel Hyperflex FPGA Architecture, which places Hyper-Registers throughout the FPGA. These devices combine the Intel Agilex FPGA fabric with a broad set of intellectual property (IP) and connectivity options including high-speed transceivers that support data rates to 28.1 Gbps and the PCIe 4.0 interface protocol. The new Intel Agilex device family also supports DDR4, LPDDR4, DDR5, and LPDDR5 SDRAM; general purpose I/O with output voltages ranging from 1.05 V to 3.3 V; in addition a hard processor system (HPS) based on a multi-core Arm Cortex CPU with two Arm Cortex-A76 processor cores that can run as fast as 1.8 GHz and two Arm Cortex-A55 processor cores that can run as fast as 1.5 GHz.

A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members. Fiber -- which was developed to power large-scale parallel scientific computation projects like POET -- is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines. As the researchers point out, increasing computation underlies many recent advances in machine learning, with more and more algorithms relying on distributed training for processing an enormous amount of data.

In August, Tencent announced it had developed an AI system capable of defeating teams of pros in a five-on-five match in Honor of Kings (or Arena of Valor, depending on the region). This was a noteworthy achievement -- Honor of Kings occupies the video game subgenre known as multiplayer online battle arena games (MOBAs), which are incomplete information games in the sense that players are unaware of the actions other players choose. The endgame, then, isn't merely AI that achieves Honor of Kings superhero performance, but insights that might be used to develop systems capable of solving some of society's toughest challenges. A paper published this week peels back the layers of Tencent's technique, which the coauthors describe as "highly scalable." They claim its novel strategies enable it to explore the game map "efficiently," with an actor-critic architecture that self-improves over time.

Intel's Nervana NNP-I chips are designed to be crammed into data centers for AI tasks like translating text or analyzing photos. It may not be obvious, but you're almost certainly using AI every day. Artificial intelligence-boosting hardware in your phone enables voice recognition and spots your friends in photos. In the cloud, it delivers search results and weeds out spam email. Next up for dedicated AI hardware will be your laptop, Intel expects.

Robots with truly humanlike dexterity are far from becoming reality, but progress accelerated by AI has brought us closer to achieving this vision than ever before. In a research paper published in September, a team of scientists at Google detailed their tests with a robotic hand that enabled it to rotate Baoding balls with minimal training data. And at a computer vision conference in June, MIT researchers presented their work on an AI model capable of predicting the tactility of physical things from snippets of visual data alone. Now, OpenAI -- the San Francisco-based AI research firm cofounded by Elon Musk and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman -- says it's on the cusp of solving something of a grand challenge in robotics and AI systems: solving a Rubik's cube. Unlike breakthroughs achieved by teams at the University of California, Irvine and elsewhere, which leveraged machines tailor-built to manipulate Rubik's cubes with speed, the approach devised by OpenAI researchers uses a five-fingered humanoid hand guided by an AI model with 13,000 years of cumulative experience -- on the same order of magnitude as the 40,000 years used by OpenAI's Dota-playing bot.

LONDON – Following the launch of its AI inference chip last year, Habana Labs (Tel-Aviv, Israel) has unveiled an AI training chip built on the same architecture that can outpace the incumbent technology by a substantial margin, and features on-chip RoCE (remote direct memory access over Converged Ethernet) communications for scalability. While the company's inference chip, Goya, set records for ResNet-50 inference back in September 2018, the new training chip, Gaudi, offers similar high performance. Gaudi can process 1650 images per second at a batch size of 64 when training a ResNet-50 network, which Habana claims is a new world record for this benchmark. This throughput is delivered at 140W power consumption, also a substantial advantage versus competing solutions, according to the company. Impressive, but is Habana's architecture designed specifically to beat the ResNet-50 benchmark, or will it offer similar throughput advantages for other types of neural networks?

I was honored to be invited by DevTO to give a talk at their May meetup. The organizers were keen to have someone speak about high-performance machine learning, and I was happy to oblige. The general thesis of the talk is that, for the purposes of machine learning, setting up large compute clusters is wholly unnecessary. Furthermore, it should generally be considered harmful as those efforts are extremely time consuming and detract from solving the actual machine learning problem at hand. To illustrate the point, I showed an online learning approach to binary classification problems using logistic regression with adaptive learning rates.

Last summer, I spent a week at a conference dedicated to graphics-processing units--GPUs. It was presented by GPU big name Nvidia, a brand that is largely associated with gaming hardware. At the conference, however, gaming was a sideshow. For that matter, graphics themselves (excluding VR) were a sideshow, despite being in the actual name. In general, this was a machine learning conference, and, to most of the attendees, of course it was. With chipmaker AMD's announcement this week at CES that the bleeding-edge of its GPU product line will be targeted at machine learning, at least initially, I thought it would be a good opportunity to take a step back and offer a bit of background on why GPUs and machine learning are so intimately connected in the first place.