Society is on the brink of the second industrial revolution according to Wired co-founder Kevin Kelly who says the formula for tomorrow's startups is simple; add artificial intelligence (AI). Kelly was speaking as part of a keynote at the Cannes Lions on the "inevitable" 12 technological forces that will shape the world. One of the key changes that Kelly sees on the horizon is AI, which he says is "launching the second industrial revolution". The first revolution, he says, came about because of the invention of "artificial power" which could be added to manual processes to make them easier. Using an example of a water pump moving from a manual process to an electric pump, Kelly says the formula for the industrial revolution was "take X, add electricity, and you have something new that can do something we couldn't imagine before".

The artificial intelligence market is estimated to grow from USD 419.7 Million in 2014 to USD 5.05 Billion by 2020, at a CAGR of 53.65% from 2015 to 2020. The report aims at estimating the size and future growth potential of the market across different segments such as technology, application, and region. The base year considered for the study is 2014 and the market size is forecasted from 2015 to 2020. With the rise in adoption of AI in the media & advertising, retail, finance, and healthcare sectors, the machine learning and natural language processing technologies are expected to play a key role in propelling the growth of the AI market in the next five years. The research methodology used to estimate and forecast the artificial intelligence market begins with capturing data on key market player revenues through secondary research.

Futurist Ray Kurzweil predicted in 1990 that a computer would beat a human world champion chess player by 1998. In 1997, that actually happened with IBM's Deep Blue. Since then, artificial intelligence (AI) has continued to advance rapidly, making now a good time to brush up on what is considered the next wave of highly disruptive technology. AI consists of many sub disciplines such as natural language processing, computer vision, knowledge representation and reasoning. The technology is making its way into a broad range of industries from marketing with behavioural targeting, to healthcare with accurate and early detection of complex diseases, to infrastructure with smarter urban planning.

I'm implementing a machine learning structure to try and predict fraud on financial systems like banks, etc... This means that there is a lot of different data that can be used to train the model eg. I'm having trouble deciding which structure is the best for this problem. I have some experience with decision trees but currently I have started to question if a neural network would be better for this kind of problem. Also if any other method would be best please feel free to enlighten me.

THEY started off by wounding our pride. Will AI end up taking our jobs – or even our lives? Twenty years ago, IBM's Deep Blue beat Garry Kasparov at chess – then seen as the gold standard of human intellect. Now a new wave of AI seems poised to take over a wide range of human tasks, potentially putting huge numbers of people out of work. And an unlikely alliance of philosophers, technologists and movie-makers has stoked fears that the next generation of AI might snuff out humanity.

"No Dr., or How I Learned to Stop Debugging and Love the Robot" In this talk, Guy will dicuss what developers must know to explore the power of machine learning services in the cloud. Using data to build machine learning models is a powerful alternative for heuristic or handwritten rules. This power is not limited to people with Ph.D. or MSc. in machine learning, statistics or computer science, but can be used successfully by competent developers. You will learn how to get started and how to think in machine learning terms when developing your next smart application. To gain background on machine learning in the cloud before the meetup, consider reading Guy's blog posts on machine learning.

The threat of terrorism has greatly accelerated the exchange of data between European states. Social media has become indispensable, both for investigative purposes and to fight propaganda. The "Fraternity Taskforce", a group of some 20 investigators, has probing into the Paris attacks of 13 November 2015 since late last year. But this team, based at Europol headquarters in The Hague, has no high-tech surveillance equipment or bullet-proof vests. Its main weapon and its biggest resource is data, vast quantities of data. The European police organisation's focus on terrorism has quickly taken off with this investigation.

Firstly, AI is an incredibly vibrant field. We're discovering ways of evolving the technology and applying it to solve profound social and business problems – problems where previous generations of computing systems were not able to provide much benefit. It has a tremendous ability to amplify our own cognitive strengths – it contributes to my ability to make better decisions, to see the world through a lens I would have otherwise been blind to. There are tremendous opportunities and we are only at the threshold of what is possible. Watson is being developed as a tool that can help build and grow businesses – what do you see as the potential for AI in this field? Watson is transforming the way businesses approach their operations and fuelling their growth with tools that help them understand, reason, learn and interact in a way that has clear and obvious benefits to the human condition.

Is 2016 the year of AI? Big data? The answer, said CB Insights, may lie in media coverage. Whereas traditionally, many predictors relied on tracking investments in different areas or talking to specialists to uncover hot areas in tech, CB Insights' new tool called Trends now analyzes media reporting on different topics to uncover the next big thing. Why Dick's Sporting Goods decided to play its own game in e commerce Dick's Sporting Goods has long partnered with eBay Enterprise on its e -commerce platform. Learn the benefits and risks of this multi -million dollar IT bet.

Before giving smart machines the ability to make decisions, people need to make sure the goals of the robots are aligned with those of their human owners. Google can see a future where robots help us unload the dishwasher and sweep the floor. The challenge is making sure they don't inadvertently knock over a vase -- or worse -- while doing so. Researchers at Google, along with collaborators at Stanford University, the University of California at Berkeley, and OpenAI -- an artificial intelligence development company backed by Elon Musk -- have some ideas about how to design robot minds that won't lead to undesirable consequences for the people they serve. They published a technical paper on Tuesday outlining their thinking.