Google, Amazon, and Facebook can magically recognize images and voices, thanks to superfast servers equipped with GPUs in their mega data centers. But not all companies can afford that level of resources for deep learning, so they turn to cloud services, where servers in remote data centers do the heavy lifting. Microsoft has made such cloud services trendy with Azure and is one of the few companies offering remote servers with GPUs, which excel in machine-learning tasks. But Azure uses older Nvidia GPUs, and it now has competition from Nimbix, which offers a cloud service with faster GPUs based on the Nvidia's latest Pascal architecture. After renting time on the cloud service, customers get a virtual machine with access to bare-metal server hardware.

The Oak Ridge National Laboratory (ORNL), home of the Titan supercomputer and a research team dedicated to Vice President Joe Biden's Cancer Moonshot program, is experimenting with how deep learning can be used to improve cancer research. Specifically, ORNL researchers applied new deep learning techniques to automate how information is extracted from cancer pathology reports documented across a nationwide network of cancer registry programs. These registry programs collect demographic and clinical information related to diagnosis, treatment, and history of cancer incidences in the US, and are used by physicians as a consultation tool for broad cancer surveillance. Using a dataset composed of 1,976 pathology reports, ORNL researchers trained a deep-learning algorithm to multitask, which in this context means it was made to simultaneously carry out two different but closely related information-extraction tasks. In the first task the algorithm aimed to identify the primary location of the cancer, and in the second it identified which side of the body the cancer was located.

Google cut power usage in its data centers by several percentage points earlier this year by trusting artificially intelligent software derived from 1980s-era Atari video games. And in the years to come, the Internet giant not only could save much more electricity, but also solve far larger problems by taking on a much more complex video game. Research scientists at Google's DeepMind unit announced Friday they are developing a computer program that reads data about Blizzard Entertainment's "StarCraft II" games and learns how to play on its own. The software would have to figure out how to split its attention between micromanagement and long-term strategic decisions. It's that maneuvering that could deliver big breakthroughs.

Elon Musk's "Alien Dreadnought" is bringing a German crew on board. Tesla, the electric car company based in Palo Alto, announced Tuesday that it will buy Grohmann Engineering, based in Prum, Germany, to be renamed Tesla Grohmann Automation. Grohmann, founded by Chief Executive Klaus Grohman, is a highly regarded supplier of factory automation systems in industries ranging from automobiles to microelectronics. It has outposts around the world, including offices in Charlotte, N.C. and Chandler, Ariz. The German company employs about 700 people.

Ecommerce company Etsy today disclosed in a filing that it spent $32.5 million to acquire Blackbird Technologies, a startup that had developed artificial intelligence (AI) software that could be used for various search applications in the context of shopping. "The Company completed this acquisition to improve the quality and relevance of search on Etsy.com," says the SEC filing. Total consideration for the acquisition was approximately $15.0 million, consisting of $8.1 million in cash and 513,304 shares of the Company's common stock with a fair value of $6.9 million on the acquisition date. Additionally, the Company issued 184,230 shares of common stock or restricted stock units ("RSUs") on the acquisition date with a fair value of $2.5 million which are tied to continuous service with the Company as an employee and are being accounted for as post-acquisition stock-based compensation expense over a three-year vesting period. The Company will pay up to an additional $8.8 million in cash and issue up to an additional 460,575 shares of RSUs post-close with a fair value of $6.2 million, both of which are also tied to continuous service with the Company as an employee and are being accounted for as post-acquisition stock-based and other compensation expense over a three-year vesting period.

Pundits are quick to hype AI and machine learning as the future of everything. But, anyone who has been caught screaming at Siri for its lack of understanding of the most basic of queries knows that we have a long, ponderous way to go before "we have arrived." That's why I find Gil Press' summary of the recent O'Reilly AI Conference so helpful and important. Some of the observations are banal ("AI is not going to exterminate us, AI is going to empower us"), but others capture the essence of what makes AI so promising...and beguiling. AI is hard...get over it The first observation ("AI is difficult") seems obvious, yet for all the wrong reasons. The first thing that makes AI and machine learning difficult comes down to trust.

IAB (Interactive Advertising Bureau) and its Data Center of Excellence today announced the winners of the inaugural IAB Data Rockstar Awards, celebrating top industry leaders and practitioners who have demonstrated achievement in data science or technology. The top finalists were selected by the IAB Data Center of Excellence Board of Directors and were evaluated based on demonstrated excellence, creativity or forward-thinking approaches to solving problems in data science, as well as the impact their contributions have made to their company or industry. Chalasani developed a highly efficient, distributed, extreme-scale, single-pass online logistic regression learning system in Scala/Spark, using variants of Stochastic Gradient Descent, capable of handling hundreds of millions of sparse features and billions of training observations. His system incorporates a number of state-of-the-art techniques that do not exist together in any other machine learning system, including adaptive feature-scaling, adaptive gradients, feature-interactions and feature-hashing. Chalasani work is central to MediaMath's vision for every addressable interaction between a marketer and a consumer to be driven by Machine Learning optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes.

Crunching vast tracts of data is a growing task for defense, intelligence and security agencies. They need analysis, fast, of what's going on in the air and on the ground to assess battlefields, secure environments, and decide when and how to deploy people or humanitarian aid. Artificial intelligence may be the key to digesting the barrage of data from multiple sources. To unlock insights from this data, agencies are increasingly turning to GPU-powered deep learning, with algorithms that can identify relevant content and patterns in raw data at machine speed. The GPU is the engine of modern AI, NVIDIA solution architect Jon Barker Barker told a broad audience from the defense, intelligence and homeland security communities at the recent annual GEOINT Symposium.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

London-based BenevolentAI believes it has built the kind of artificial intelligence tech that will allow it to identify and develop drugs faster and better than any group of mere scientific mortals can hope for. And now J&J is handing over some experimental molecules it needs to prove it's right. The upstart joins a long line scrambling to apply vast amounts of computational power towards drug development. Their goal is to usher in the long-awaited "pharma 2.0" and finally bend the expensive curve of late-stage trial failure. It's unclear how BenevolentAI's algorithms are any better at evaluating the potential of any small-molecule than other computationally-taxing approaches developed by other groups -- and it's all driven by the data.