Google tapped into the superior intelligence of its DeepMind neural network to find ways to vastly reduce the energy it uses in its data centers, which make up 40% of the worldwide Internet. "This will also help other companies who run on Google's cloud to improve their own energy efficiency," Google said in a blog about the achievement. "While Google is only one of many data center operators in the world, many are not powered by renewable energy as we are." Google has set a goal to eventually power its data centers using 100% renewable energy. Today, the company claims, renewable energy is used for 35% of its power needs.

For more than a decade, technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial Internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Google has created artificial intelligence that's able to save the amount of electricity it uses to power its data centres. Using machine learning developed by the firm's AI research company, DeepMind, it was possible to reduce the energy used for cooling the centres by a staggering 40 per cent. By applying machine learning to its own centres, which power Google Search, Gmail, YouTube and all of Google's services, it was able to improve their efficiency. The algorithms and methods used could also be transferred to air conditioning systems in large manufacturing plants or, on an even larger scale, to reduce wastage in the energy grid. "What we've been trying to do is build a better predictive model that essentially uses less energy to power the cooling system by more accurately predicting when the incoming compute load is likely to land," Mustafa Suleyman, the co-founder of DeepMind told WIRED.

The government will introduce new rules on running Japanese language schools to eliminate poorly managed ones and keep the educational quality at an adequate level, sources said Wednesday. The Justice Ministry will revise the relevant ordinance soon, more clearly stating disqualifying conditions and making its screening more stringent, the sources said. There were 549 approved Japanese language schools in fiscal 2015, which ended in March. Due to Japan's declining population, the government aims to promote the establishment of Japanese language schools to attract more highly skilled foreign workers, but inappropriate operations at some schools have surfaced recently. A man running a Japanese language school in Fukuoka Prefecture was convicted in May of finding part-time jobs for students who worked more hours than allowed by law so they could earn money for school fees.

It's a rare business today that doesn't take at least some steps to optimize its website for search-engine rankings, but how best to do that remains an open question. A new study published Wednesday suggests that inbound links -- those leading from other websites into your own -- are even more critical than you may think. Search engine optimization, or SEO, essentially refers to all the things you can do to give your website the best possible placement in unpaid search results. If you sell widgets, you want your page to place as close as possible to the top of Google's results when someone searches on the term "widgets." The trick is making that happen.

Big data and machine learning have generated tremendous interest in empirical finance research. In this presentation,Yin examines a unique news analytics database provided by Ravenpack. He applies a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns. He finds news sentiment data adds significant incremental predictive power to his machine learning based global stock selection models. Presentation held at the RavenPack 4th Annual Research Symposium, New York, June 16th 2016.

Now let's run the NbClust algorithm to estimate the ideal number of clusters (Figure 4). Figure 4. Number of clusters chosen by 26 indices, post standardization.

The evolution of technology, from natural language processing to machine learning, is now helping the software world find more places to interact with biology. A company launched today in Palo Alto, CA, that has plans to build a "life science search engine" that may be able to speed up the process of drug discovery and life sciences research. Called Bioz, the startup closed 3 million in seed funding from 5AM Ventures to begin offering its software-as-a-service system. The software, which uses machine learning, sifts through millions of pages of scientific papers to select products, help plan experiments, and perform other research-related functions, with the goal of improving the process of developing treatments for disease. Bioz, founded by CEO Daniel Levitt and Stanford research scientist Karin Lachmi, says it will help workers select products--from reagents to instruments--they would use in research projects.

A Jupyter notebook accompanies this blog post. Why Multi-Task Learning When you think about the way people learn to do new things, they often use their experience and knowledge of the world to speed up the learning process. When I learn a new language, especially a related one, I use my knowledge of languages I already speak to make shortcuts. The process works the other way too - learning a new language can help you understand and speak your own better. Our brains learn to do multiple different tasks at the same time - we have the same brain architecture whether we are translating English to German or English to French.

At Satisfi, we are on the hunt for ways to improve customer engagement in retail spaces and change the way brands and consumers interact. By tapping into the cognitive computing smarts of IBM Watson, coupled with our intelligent engagement platform, our goal is to uncover new ways retailers can reach customers and deliver the personalized experiences they crave.Today, Satisfi has teamed up with IBM and Macy's to unveil the pilot of Macy's On Call, a first-of-its kind, in-store shopping assistant powered by artificial intelligence. Using our platform and Watson's Natural Language Classifier and Language Translation APIs, we've built a tool to help shoppers easily access the information they need as they shop and navigate the store. Consumers can ask questions in natural language and seek out information in-store, all from the palms of their hands.Macy's On Call is being piloted at 10 Macy's across the country. In response, the tool will deliver a relevant response and the location of that product in the store.