Google just paid for part of its acquisition of DeepMind in a surprising way. The internet giant is using technology from the DeepMind artificial-intelligence subsidiary for big savings on the power consumed by its data centers, according to DeepMind co-founder Demis Hassabis. In recent months, the Alphabet unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, "which is a huge saving in terms of cost but, also, great for the environment," he said.

If Gabe Otte '11 hadn't had a Cornell advisor who steered him down a more challenging path and hadn't had some chance conversations with Nobel Prize-winning chemist Roald Hoffman, he might be squirreled away in a lab somewhere. Instead, he's the CEO of Freenome, a start-up just awarded 5.5 million in venture capital for its product, a data-driven blood test that can detect various types of cancers in their earliest stages and recommend the best treatments. Otte came to Cornell planning to study computer science, but a freshman-year advisor encouraged him to choose another major. "I had been coding and programming since I was nine years old," Otte said, so he elected to study chemistry and computational biology, using his knack for computer science to do his homework. "I fell in love with chemistry when I took organic chemistry," he said, adding that he developed his own computer program to do computations related to the synthesis of molecules.

Pete is a professional data scientist. He has used machine-learning algorithms to create predictive engines in variety of fields including marketing, mechanical prognostics and health management, and algorithmic stock trading. Pete enjoys Codementoring and the great interaction and learning that comes with it. Pete's degree is in Physics from the University of Texas, Austin.

In a world where data is abundant, leveraging machines to learn valuable patterns from structured data can be extremely powerful. In this course, we will explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization. You'll come away with a strong understanding of the core concepts in machine learning and the ability to efficiently train and benchmark accurate predictive models. Students gain hands-on practice with powerful packages like scikit-learn, building complex ETL pipelines to handle data in a variety of formats and techniques, developing models with tools like feature unions and pipelines that allow them to reuse existing models and reduce duplicate work, and practicing tricks like parallelization to speed up prototyping and development. Mini Project: Working with a real data sets students will take restaurant reviews and, based on various characteristics, build predictive models to predict the restaurant's score.

Precision medicine [1] has become a most promising methodology for clinical medicine, which relies heavily on rich biomedical knowledge and information of individual patients such as genetic content, living habits, environmental factors, etc. [2]. US National Academy of Sciences claims in a 2011 research report that a biomedical knowledge network based on biological data and knowledge is necessary for precision medicine [3]. How to compute relatedness between concepts and discover valuable information and implicit knowledge effectively and efficiently from such hybrid knowledge (both structural and non-structural) networks is a key of paramount importance to the realization of precision medicine, and a huge challenge facing the biomedical research community. It is agreeable that the knowledge network should include all the knowledge sources, information systems and repositories in biomedicine available today and in the future, spanning the whole spectrum of structural and non-structural information and knowledge. One type of important knowledge sources is ontology.

Two years ago, Google spent over half a billion dollars for the tiny artificial intelligence startup DeepMind. Since then, the unit has walloped Atari video games and beaten an impossible board game. But those AI demonstrations have yet to spell actual revenue. Until now -- although the efforts are helping Google save money on its most expensive part. DeepMind chief Demis Hassabis told Bloomberg that his unit recently began applying its advanced AI to Google's data centers, finding ways to reduce the company's sizable energy bill.

Warning: The robots that power ever-popular review platform Yelp are becoming sentient. Okay, not really, but as Fast Company reports, the company is using artificial intelligence to better serve its millions of users. For an entire generation of diners, taking photos of their food has become second-nature -- and thanks to them, Yelp has a gigantic database of photos. While previously the company has relied on users to caption their own photos with "search-friendly metadata," it's now armed with software intelligence that can identify information about a restaurant based on photos alone. As FastCo explains, this is thanks to deep learning, "a form of machine learning that involves training neural networks to solve problems using large sets of data."

Recently I was consulting with a publishing company that is exploring various ways to digitize and contextualize its content. Knowing that some of the company's competitors had signed deals with IBM's Watson, I asked several executives why they had not done a Watson deal themselves. "We think that the market for AI software is rapidly commoditizing, and we believe we can assemble the needed capabilities ourselves at much lower cost," was this company's party line. Some particularly knowledgeable managers mentioned that they expected the company would instead make use of open-source cognitive software made available from various providers. These potential providers are not small vendors -- they include, for example, Google, Facebook, Microsoft, Amazon, and Yahoo.

There are some very good questions being asked about artificial intelligence, and some very good answers on both sides from some very intelligent people. But which do you find more convincing? The Drum presents the case for and against as part of a recently published issue of the magazine, guest edited using AI. PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian.

Samsonovich made the comments while attending the 2016 Annual International Conference on Biologically Inspired Cognitive Architectures (BICA) in New York City, which takes place from July 16-19. The conference was sponsored by MEPhl and attracted more than 200 participants. "We are on the verge of a major breakthrough that was discussed since the fifties of the previous century," Samsonovich said on Tuesday. The breakthrough, according to Samsonovich, is the creation of free thinking machines capable of feeling and understanding human emotions, understanding narratives and thinking in those narratives, as well as being capable to actively learn on their own. "Those are the three key capabilities in my view that will determine the breakthrough," Samsonovich said, adding that progress is expected to be made in "several years."