When a poker-playing AI program developed at Carnegie Mellon university challenged a group of machine learning-savvy engineers and investors in China, the results were the same as when the software went up against professional card players: it beat its human competition like a drum. And that points to AI’s greatest strength, as well as its greatest weakness.
A supercomputing application that can figure out if state legislative districts have been unfairly drawn, has the potential to change electoral politics in the United States. According to its inventors at the University of Illinois at Urbana-Champaign, the application could be used by courts to determine if partisan gerrymandering has been used to unfairly manipulate these maps.
Cori, the fifth fastest supercomputer in the world, has been used to model a 45-qubit circuit, which, by all accounts, is the largest simulation of a quantum computer ever achieved. The virtual circuit is just a handful of qubits short of a quantum computing system that would be more powerful than any conventional computer currently devised.
Hyperion Research, the company that recently emerged from the spin-out of the IDC HPC analyst team, has come out with its 2016 server revenue numbers for high performance computing. Their data shows that global sales of these servers totaled $11.2 billion last year, up 4.4 percent from 2015, and beating the previous record of $11.1 billion in 2012.
This week Russia posted its list of the 50 most powerful supercomputing systems installed in the country. From the looks of things, not much is happening in the HPC space there. System turnover across the Top50 during the last six months was minimal and aggregate performance barely budged.
Although Google’s Tensor Processing Unit (TPU) has been powering the company’s vast empire of deep learning products since 2015, very little was known about the custom-built processor. This week the web giant published a description of the chip and explained why it’s an order of magnitude faster and more energy-efficient than the CPUs and GPUs it replaces.
A recent survey compiled by MIT Technology Review and Google Cloud suggests that machine learning (ML) is being adopted by businesses at a rapid pace. According to data collected, 60 percent of respondents indicated they have already implemented ML strategies, with almost a third attesting they were at the “mature stage” of those efforts.
Ministers from seven European countries have signed on to a plan to develop an exascale capability based on technology developed within the EU member states. The goal is to bring up two pre-exascale supercomputers by 2020 and two full exascale systems no later than 2023.