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.

With Watch Room, our goal is to contribute to the budding conversation around the promise and perils of Artificial Intelligence research, in a way that respects the complexities involved. As such, we've done our best to create a story that touches on everything from simulation theory, to brain emulation, to Roko's Basilisk... to that most hallowed of science fiction questions: "What makes us human?" Another goal of ours is to illustrate the possibilities within the realm of virtual reality. Of course, Watch Room's scientific roots drink deeply from rich dramatic soil. On one level, we're just plain old excited to make a film that's a joy to watch: smart and twisting in a way that respects the audience and keeps you guessing right up to the end.

Google said it now gets 3.5 times as much computing power out of the same amount of energy as it did five years ago thanks to custom-built servers, more efficient cooling systems that use outside air, and investment in green energy. The company wants to cap its increase in energy use at four per cent a year between 2014 and 2020 even as data use grows at a faster rate. It also plans to be 100 per cent powered by renewable energy. But it hasn't said when it will reach that goal, or how much of its power currently comes from renewable sources. The data centre algorithm can eventually be used to improve efficiency in other areas, according to Google, including getting more energy from the same amount of input at power plants, and reducing energy and water usage in semiconductor manufacturing.

We are long, long past the point where humans can manage this network expansion. Our network management systems, billing systems and customer management systems cannot do this job the way we want it done. SoftBank said this week that it is buying chipmaker ARM for 31.4 billion. But the presence of a major chipmaker and telecom companies in the same AI-friendly portfolio makes it seem likely that deep attention will be paid to the topic.

The definition of artificial intelligence (AI) is a bit fuzzy, so when AT&T claims that it has been using AI for more than 20 years, it should be kept in mind that the AI of 1996 is a lot different from the AI of 2016. The bigger point is that the newer version is doing a lot for the carrier. AT&T is settling on an AI platform that can be used for different things instead of developing "one-off" solutions every time a task requiring the predictive capabilities and massive number-crunching abilities of AI presents itself. AI can be leveraged to anticipate rather than simply react to events, as less sophisticated AI platforms have done in the past. The driver is software-defined networks (SDNs), according to Computerworld.

The Intergovernmental Panel on Climate Change (IPCC) highlights six main lines of evidence for climate change. First, we have tracked (see chart) the unprecedented recent increase in the amount of atmospheric carbon dioxide and other greenhouse gases since the beginning of the industrial revolution. By burning coal, oil, and natural gas, we accelerate the process, releasing vast amounts of carbon (carbon that took millions of years to accumulate) into the atmosphere every year.