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Top AI Researchers Keep Leaving Facebook

Jumping Ship Mark Zuckerberg's Meta-formerly-Facebook is bleeding its top AI leaders. As Fortune reports, the company has lost at least three significant AI leaders in March alone: Devi Parikh, Meta's now-former senior director of generative AI; Abhishek Das, the now-former head of Meta's Fundamental AI Research (FAIR) team; and Erik Meijer, Meta's now-former director of […]

The 'Meta AI Mafia' brain drain continues with 3 more major departures

This is "probably the best time in tech history to strike out on your own," say Erik Meijer who recently left Meta.

Maharashtra: 32,000 Trees Axed, 9,000 More at Risk to Make Way for Mumbai-Vadodara Highway

AI&HPC for Everything - the 32nd HPC Connection Workshop at SC23

Data de-identification: Best practices in the new age of regulation

The increasing importance of de-identification of PII data in an organization, third parties and applications, particularly in the cloud.

What's New in HPC Research: ROBE, OpenMP Automated Scheduling, SCALSALE, & More

ThirdAI raises $6M to democratize AI to any hardware – TechCrunch

RAMBO can cut indexing times for huge DNA databases

RAMBO can cut indexing times for huge DNA databases Rice University computer scientists are sending RAMBO to rescue genomic researchers who sometimes wait days or weeks for search results from enormous DNA databases. DNA sequencing is so popular, genomic datasets are doubling in size every two years, and the tools to search the data haven t kept pace. Researchers who compare DNA across genomes or study the evolution of organisms like the virus that causes COVID-19 often wait weeks for software to index large, metagenomic databases, which get bigger every month and are now measured in petabytes. RAMBO, which is short for repeated and merged bloom filter, is a new method that can cut indexing times for such databases from weeks to hours and search times from hours to seconds. Rice University computer scientists presented RAMBO last week at the Association for Computing Machinery data science conference SIGMOD 2021.

RAMBO speeds searches on huge DNA databases

Rice, Intel optimize AI training for commodity hardware

Credit: Photo by Jeff Fitlow/Rice University HOUSTON (April 7, 2021) Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors. The cost of training is the actual bottleneck in AI, said Anshumali Shrivastava, an assistant professor of computer science at Rice s Brown School of Engineering. Companies are spending millions of dollars a week just to train and fine-tune their AI workloads. Shrivastava and collaborators from Rice and Intel will present research that addresses that bottleneck April 8 at the machine learning systems conference MLSys.