Collaborating Authors

[R] Sparse GPU Kernels for Deep Learning


Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts. In this work, we study sparse matrices from deep learning applications and identify favorable properties that can be exploited to accelerate computation. Based on these insights, we develop high-performance GPU kernels for two sparse matrix operations widely applicable in neural networks: sparse matrix-dense matrix multiplication and sampled dense-dense matrix multiplication. Using our kernels, we demonstrate sparse Transformer and MobileNet models that achieve 1.2-2.1x

AI Could Help Find Cheaper and Smarter Ways to Raise Fish


Artificial intelligence researchers in Norway are testing AI models designed to cut costs and improve efficiency in raising salmon, one of the country’s major exports.

Ford CTO hypes 5G in autonomous vehicle future


"So we really believe that 5G and cellular V2X [vehicle to everything] will be key technology enablers for future smart vehicles, including advanced drive assist technologies. It will allow us to do Level 3 driving with more confidence in more places, including in urban environments." said Ford's CTO Ken Washington at a recent investor event, according to a Seeking Alpha transcript of his remarks. Level 3 autonomous driving is basically midway between regular human driving (Level 0) and full-blown computer driving (Level 5). Level 3 involves a car driving itself, but only under certain conditions and with an attentive human. Washington said that Ford has been investing in locations around Detroit in order to install and test 5G technologies as well as V2X offerings.

DFI's Miniaturized IPCs Empower Edge AI Applications


In the era of Artificial Intelligence of Things (AIoT), Industrial PC (IPCs) is expected more than just a computer for general data processing. Faced with the increasing workload at the edge, end devices are required to be smart, automated, and interconnected, which reflects on the demands of AI computing and M2M (Machine-to-Machine) communication in small-sized PCs. The demand for AI computing emerged on the account of the decentralization trends in recent years to reduce cloud computing workloads and costs, and to reinforce AI performance at the edge, high-end embedded solutions is a must. But to downsize them and meanwhile support the conditions required by edge environments, like tight spaces and abrupt temperature changes, it's definitely a challenge for IPC manufactures. Computing decentralization also infused diversity and heterogeneity into AIoT framework that further stresses the importance of integration capability.

Build a Diverse Team to Solve the AI Riddle


The term artificial intelligence describes algorithms that run on powerful computers to solve complex tasks, and computer scientists are indeed the most skilled at writing such algorithms. Yet systems designed by narrowly focused technical experts -- such as computer scientists, engineers, and mathematicians -- can produce disappointing results, as each expert sees every problem through the lens of his or her respective discipline. Mathematicians, for instance, attempt to solve every problem with statistics. While it's natural to assume that computer scientists play the lead role in AI development, not every problem lends itself to such obvious solutions. Systems that actually get the job done are in fact built by better-rounded teams.

A Comprehensive Guide to Natural Language Processing


Artificial intelligence (AI) is omnipresent and is changing the way we look at the world. However, the advent of AI and data analytics tools has led to the boom of data. And to process this mountain of raw data, we need Natural Language Processing. In technical parlance, NLP is a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, helps in creating and reading textual data, visual data, and more. Basically, it helps computers understand, interpret, and manipulate human language.

Humane AI requires a regulatory regime - Information Age


Artificial intelligence (AI) is set to upend nearly every industry. It's a technology that will deliver astronomical gains in productivity, dramatic cost reductions, and tremendous advances in research and development. With AI set to increase global GDP by more than $15.7 trillion by 2030, it can be easy to assume that the technology can be nothing but an unfettered good. That would be a dangerous mistake. AI, like any technology, can have detrimental personal, societal, and economic effects: some common concerns include the fact it provides tools that can be exploited by criminals to compromise the cyber security of individuals and organisations, or that the predictive abilities of AI raise a swathe of privacy concerns.

Google announces its new $99 smart speaker, Nest Audio


It only took four years, but Google has finally released a successor to the Google Home smart speaker. At its hardware event on Wednesday, the company announced the all-new Nest Audio for $99. The smart speaker comes equipped with a 19mm tweeter and 75mm mid-woofer, which is supposed to give you a fuller and more natural sound. In comparison to the original Google Home, Google claims it delivers 50 percent more bass and 75 percent more volume. Google's Media EQ feature, first introduced in the Google Home Max, is also included in the new Nest Audio.

Enthusiasm around autonomous vehicles has grown during the COVID-19 pandemic - TechRepublic


A report on the public perception of self-driving vehicles in the United States found that 62% of people surveyed believe autonomous vehicles are the way of the future, and that enthusiasm for those vehicles has risen since the onset of the COVID-19 pandemic. The survey of more than 1,000 Americans and its accompanying Consumer Mobility Report comes from Motional, a driverless technology company created by Hyundai and Aptive. Motional was created to work on commercial uses of SAE level four vehicles, which are fully autonomous and able to perform all tasks from the beginning to the end of a trip. Along with finding enthusiasm for driverless vehicles rising, Motional also found that there's a knowledge gap around self-driving vehicles that plays directly into an enthusiasm gap. Respondents who rated themselves extremely knowledgeable about autonomous vehicles were far more likely to believe that those on the road to day are safe and reliable (76%), versus those that said they are less knowledgeable, of whom only 10% said current self-driving vehicles are safe.

Frase – AI for Content


The future of search is about answers, not links. Frase transforms how you deliver answers with your content. Create content that is aligned with user intent, ranks 1st on Google, and is usable by voice devices. The first AI Chatbot that uses your website content to automatically answer visitor questions.