In a world where the term "big data" is being thrown around like the next coming, many business leaders still struggle to understand how more information is going to help them make better decisions that drive their businesses forward. But the real challenge goes well beyond merely accessing more data. The key is accessing data in the right way, at the right time, and in the right format to generate beneficial insights. This process is no small feat. It requires both technology and human analysis in order to identify these critical insights for business leaders.

The ability to predict the stock market is, as any Wall Street quantitative trader (or quant) will tell you, a license to print money. So it should be of no small interest to anyone who likes money that a new system that works in a radically different way than previous automated trading schemes appears to be able to beat Wall Street's best quantitative mutual funds at their own game. It's called the Arizona Financial Text system, or AZFinText, and it works by ingesting large quantities of financial news stories (in initial tests, from Yahoo Finance) along with minute-by-minute stock price data, and then using the former to figure out how to predict the latter. Then it buys, or shorts, every stock it believes will move more than 1% of its current price in the next 20 minutes - and it never holds a stock for longer. The system was developed by Robert P. Schumaker of Iona College in New Rochelle and and Hsinchun Chen of the University of Arizona, and was first described in a paper published early this year.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Science fiction writers describe them as intelligent robots. Industry 4.0 advocates call them autonomous cyber-physical systems. The former often depict them in the worst possible light, as part of their nightmarish visions and dystopian horror stories. From Arthur C. Clarke's HAL to James Cameron's T-800, these AI-powered mechanical monsters have a tendency to use their supernatural strength and intelligence to outwit, outrun and outgun humans. On the other hand, the tech sector tends to view them as the catalyst to exponential productivity growth–the key to automation utopia.

The principles are laid out to ensure that the rapidly developing artificial intelligence systems of the world remain in service of humanity. MANILA, Philippines – Recently, an artificial intelligence (AI) computer program called Libratus defeated 4 poker professionals – a "landmark step" for AI, said its creator, because poker had been particularly challenging for AI up until that point. AI's victory in the poker contest – following wins in chess and the even more complex boardgame "go" – is a reminder that AI is getting smarter all the time. How then does humanity make sure that it remains an ally and doesn't go rogue like Skynet did in the Terminator series? That was the question discussed by global experts at the Beneficial AI conference held in early January in California.

Computer vision and image recognition are integral parts of artificial intelligence (AI), which has quickly gone from niche to mainstream in the past few years. And nowhere was this more evident than at CES 2017 earlier this month. From a few days of wandering the floor, here are some of the coolest new uses of computer vision. The biggest displays of computer vision are coming from the automotive industry, because computer vision, after all, is one of the central enabling technologies of semi- and fully-autonomous cars. NVIDIA, which already helped supercharge the deep learning revolution with its deep learning GPU tools, is powering many of the autonomous car innovations with the NVIDIA Drive PX 2, a self-driving car reference platform that Tesla, Volvo, Audi, BMW, and Mercedes-Benz are already using for semi- and fully-autonomous functions.

Prof. Tom Griffiths is the director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at UC Berkeley. He studies human cognition and is involved with the Center for Human Compatible Artificial Intelligence. I asked him for insight into the intersection of cognitive science and AI. He offers his thoughts on the historical interaction of the fields and what aspects of human cognition might be relevant to developing AI in the future.

If you think Americans have a love affair with cars now, just wait until people begin to treat them not merely as a means of transportation but as smartphones on wheels -- or robot servants. Automobiles are more wired than ever. But experts say the late-model cars on the road today are just a precursor to truly connected cars. In the near future this next generation of cars -- loaded with sensors and screens, feeding back to third parties huge amounts of data on vehicle performance and even driver behavior -- will communicate with other devices, be loaded with apps and even make their own decisions. While for now the smart car is lagging behind the smart home, automobile manufacturers, technology firms, ride-hailing companies and financial institutions are on course to integrate automobiles into the burgeoning Internet of Things. In the process they intend to revolutionize transportation and the fabric of people's daily lives. "The car is going to be an extension of who you are, just like the phone is," said Suresh Ramamurthi, the chairman and chief technology officer of CBW Bank, a small bank in Weir, Kan., that has attracted a national client base of fintech startups. In the more distant future, the car may become something greater still: an autonomous agent that can carry out tasks and authorize payments without requiring its owner's input.

Oxford philosopher and author Nick Bostrom (left) and DeepMind CEO Demis Hassabis (right). Machines will quickly become significantly smarter than humans when they achieve human level intelligence, according to a high-profile panel of artificial intelligence (AI) leaders. A YouTube video released by the Future of Humanity Institute this week shows Elon Musk, the billionaire cofounder of Tesla, SpaceX and PayPal, talking on a panel earlier this month alongside the likes of DeepMind CEO Demis Hassabis, who sold his company to Google for £400 million in 2014, and Oxford philosopher Nick Bostrom. "Once we get to human level-AI, how long before we get to where things start taking off?" asked MIT professor and panel moderator Max Tegmark, citing an "intelligence explosion." Tegmark added: "Some people say days or hours. Others envision it will happen but it might take thousands of years or decades."

Artificial Intelligence (AI) revenues will reach $47 billion in 2020, from $8.0 billion last year, According to International Data Corporation (IDC). Artificial Intelligence is complex technologies and algorithms that enable machines, software, and systems to make human-like decisions. End user organizations and software developers are in the process of deploying artificial intelligence into almost every type of enterprise process or application. Since AI represents an important opportunity, today we will highlight 10 contemporary technology trends in artificial intelligence. Nowadays, hardware is getting a lot of attention in the AI industry.