Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

Alan Turing, Albert Einstein, Stephen Hawking, John Nash--these "beautiful" minds never fail to enchant the public, but they also remain somewhat elusive. How do some people progress from being able to perform basic arithmetic to grasping advanced mathematical concepts and thinking at levels of abstraction that baffle the rest of the population? Neuroscience has now begun to pin down whether the brain of a math wiz somehow takes conceptual thinking to another level. Specifically, scientists have long debated whether the basis of high-level mathematical thought is tied to the brain's language-processing centers--that thinking at such a level of abstraction requires linguistic representation and an understanding of syntax--or to independent regions associated with number and spatial reasoning. In a study published this week in Proceedings of the National Academy of Sciences, a pair of researchers at the INSERM–CEA Cognitive Neuroimaging Unit in France reported that the brain areas involved in math are different from those engaged in equally complex nonmathematical thinking.

If 2015 was the'year of data science' then 2016 is when it's going to take over completely. In the past year, we saw many applications of data science in our everyday lives- from Uber, to Amazon, to Siri, to image recognition, to generally smarter marketing campaigns… data science made its presence felt. But to say that data science is now completely'mainstream' would still not be quite accurate. In 2016, data science plans to become more exciting and impactful. While the finance industry has been one of the earliest adopters of data science, this adoption has not been uniform across all the banking services verticals.

The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML, Amazon ML, BigML) for developers getting started in Machine Learning. It focuses on only two learning techniques, which turn out to be the most commonly used in practice: decision trees and ensembles. Each workshop is 2 day long and comprises 8 modules of 3 blocks of 30' each--including time for questions. Blocks are either Theory or Exercise, with at least one Exercise per module. The goal is to make you operational with machine learning at the end of the workshop.

Google has received tons of gushy press for its bubble-shaped self-driving car, though it's still years from the showroom floor. But for years John Deere has been selling tractors that practically drive themselves for use on farms in America's heartland, where there are few pesky pedestrians or federal rules to get in the way. For a glimpse at the future, meet Jason Poole, a 34-year-old crop consultant from Kansas. After a long day of meetings earlier this month and driving five hours across the state to watch his little girl's softball game, he was still able to run his John Deere tractor until 2 a.m. The land is hilly on Poole's family farm, so he drives the first curved row manually to teach the layout to his tractor's guidance system and handles the turns himself.

At the GPU Technology Conference, Nvidia unveiled the Tesla P100, the latest addition to Nvidia's Tesla Accelerated Computing Platform (TACP). The accelerator unit is being marketed as the most advanced hyperscale datacenter accelerator ever built – with a claimed 12x improvement over the previous Maxwell architecture, thanks to the new Pascal architecture. Designed to provide the equivalent performance of hundreds of general purpose CPUs in a much smaller package, and with significantly lower opex costs, Nvidia is targeting the next-gen datacenter use cases, which consist largely of artificial intelligence applications – which require very different compute resources than most current datacenters can provide. Cloud computing and the supercomputing that powers dense data analytics are very important for the progression of the Internet of Things (IoT). With the image-recognition that will power computer visions, smart grid management, smart city operations, and the massive amounts of sensor data that need to be crunched to realize more efficient business practices, systems like Nvidia's provide a very capable alternative to gigantic arrays of general purpose compute resources in datacenters.

Toyota doesn't just want its cars to drive themselves; it wants them to grab the wheel to stop you from crashing. Toyota's researchers are developing what they call a "guardian angel" system that will automatically take control of a vehicle, or subtly adjust a driver's actions, in order to avert danger. In contrast to other companies working on self-driving vehicles, the Japanese carmaker sees combining machine and human driving as a key step toward full autonomy. "In the same way that antilock braking and emergency braking work, there is a virtual driver that is trying to make sure you don't have an accident by temporarily taking control from you," explains Gill Pratt, CEO of the Toyota Research Institute, a company the carmaker created last year with 1 billion in funding to research automated driving, artificial intelligence, and robotics (see "Toyota's Billion-Dollar Bet"). Pratt announced the guardian-angel effort, as well as plans to create a new TRI facility close to the University of Michigan in Ann Arbor, during a speech at a conference in San Jose today.

A discussion of three major approaches to building smart machines - Classic AI, Simple Neural Networks, and Biological Neural Networks - and examples as to how each approach might address the same problem.

The field draws not only from AI, but from all areas of computer science. Data about a patient must be stored securely within a database. Effective computer-human interaction and natural language processing tools are needed to enter the data. If the patient has had imaging tests, techniques from computer vision are important in order to find the salient features and apply predictive techniques. Information from thousands of previously seen patients, requires machine learning-based approaches.

Elon Musk has not been shy about his concerns over artificial intelligence turning evil. So it wasn't a surprise in December when Musk announced the formation of OpenAI, an open-source, non-profit focused on advancing "digital intelligence in the way that is most likely to benefit humanity as a whole." That's all well and good, but not much has been revealed about what exactly OpenAI is working on. OpenAI's co-founder and CTO told Tech Insider that OpenAI is primarily focusing on advancing machine learning, which is the technology that enables computers to learn how to complete tasks through experience. Specifically, the company is focusing on two key types of machine learning that every major tech company is investing in right now.