In the Summer of 2015 a team of hackers attempted to take control of an unmanned military helicopter known as Little Bird. The helicopter, which is similar to the piloted version long-favored for US special operations missions, was stationed at a Boeing facility in Arizona. The hackers had a head start: At the time they began the operation, they already had access to one part of the drone's computer system. From there, all they needed to do was hack into Little Bird's onboard flight-control computer, and the drone was theirs. When the project started, a "Red Team" of hackers could have taken over the helicopter almost as easily as it could break into your home Wi-Fi. But in the intervening months, engineers from the Defense Advanced Research Projects Agency had implemented a new kind of security mechanism--a software system that couldn't be commandeered.

Investigators eager to uncover the genetic basis of autism could now have hundreds of promising new leads thanks to a study by Princeton University and Simons Foundation researchers. In the first effort of its kind, the research team developed a machine-learning program that scoured the whole human genome to predict which genes may contribute to autism spectrum disorder (ASD). The results of the program's analyses -- a rogue's gallery of 2,500 candidate genes -- vastly expand on the 65 autism-risk genes currently known. Researchers have recently estimated that 400 to 1,000 genes underpin the complex neurodevelopmental disorder. This newest research provides a manageable, "highly enriched" pool from which to pin down the full suite of ASD-related genes, the researchers said.

Machine learning applications for NBA coaches and players might seem like an odd choice for me to write about. Let us get something out of the way: I don't know much about basketball. However, one of the perks of working in data science and machine learning is that I can still say somewhat insightful things about sports, as long as I have data. So with that expertise I'll weigh in to offer some machine learning applications for basketball. During a conversation with my good colleague Ray Wright, who does know quite a bit about basketball and had been looking at historical data from NBA games, we suddenly realized something about player shooting.

In case you haven't heard yet, Data Science is all the craze. Courses, posts, and schools are springing up everywhere. However, every time I take a look at one of those offerings, I see that a lot of emphasis is put on specific learning algorithms. Of course, understanding how logistic regression or deep learning works is cool, but once you start working with data, you find out that there are other things equally important, or maybe even more. I can't really blame these courses.

Why: 72% of individual investors -- especially next-generation/Millennial investors -- believe that companies benefit when they focus on sustainability. About TruValue Labs: TruValue Labs is the first technology company to apply artificial intelligence (AI) and machine learning to the 80% or more of financial information about public companies obscured in unstructured data. TruValue Labs' flagship product, Insight360, makes Environmental, Social, & Governance (ESG) characteristics easy to understand and communicate. Offered as a SaaS subscription, Insight360 monitors thousands of public companies in tens of thousands of sources and analyzes ESG data to provide actionable investment insights in real-time.

BENGALURU HYDERABAD: Apple's recent acquisition of Indian machine-learning startup Tuplejump offers further evidence of a revival in the interest of global technology giants in the country's startup space, especially in areas such as artificial intelligence, cloud infrastructure and automation. Enthusiasm for tech startups in India had waned in the last two years with fewer exits, a funding crunch and an inability to scale up. That seems to be changing with Intel, Apple and Nutanix shopping around for companies and the people who work there. Intel bought Soft Machines, a Silicon Valley chip designer with offices in Hyderabad, for 300 million in September. The company was cofounded by former Intel veteran Mahesh Lingareddy.

Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages.

Next-generation cognitive computing is redefining how we live and work as more businesses are using all the data available to them to improve performance and customer service, and drive innovation and revenue. Today's business challenges have never been more complex, and the critical insights that can help address these challenges are often buried in an avalanche of data. Previously, these insights were beyond the capabilities of conventional computing solutions – programmable systems based on mathematical principles that harken back to the 1940s. But IBM Watson has changed the game. IBM Watson is built upon a new foundation called cognitive computing – a system that learns and reasons from interactions with humans, files, online interactions and its environment.

Google's DeepMind AI division is famous for defeating the Go world champion and for performing medical research with the UK's NHS. But the team has developed a very impressive new technology, that allows the AI, or deep neural network, to mimic human speech. Talking to our robots, and them answering us back, has been a sort of dream for the field of artificial intelligence. In recent years the technology has gotten better, as most of you know from Siri, Cortana or Google Now voice interactions. But even our powerful digital assistants still rely on pre-recorded human voices, or they quickly turn cold and robotic. However, the work done by the DeepMind engineers may help change that forever.

The legal sector is more advanced in use its use, understanding and application of AI technologies, a New CenturyLink study suggests. Before 2015, the legal sector was one of the least technologically ept professions. But since then it has undergone an automation transformation. In history and practice, law has largely relied on lawyers and legal aids sifting through copious documents seeking evidence or precedents. A precedent is defined as'an earlier event or action that is regarded as an example or guide to be considered in subsequent similar circumstances', according to Google.