Machine learning is generating a tremendous amount of attention these days from the press as well as the practitioners. And rightly so – machine learning is a transformative technology. But despite the references to the topic, the money raised from venture capitalists, and the spotlight that Google is bringing to the subject, machine learning is still poorly understood outside of a core group of highly technical leaders. This has the effect of underestimating how transformative machine learning is going to be. It also has the effect of shielding business leaders from what they need to do to prepare for the era of machine learning.

Today is a big day for whatGoogle just bought a startup that takes the pain out of editing videos. Read more ... » very well could be the next big business at GoogleGoogle Acquires Fly Labs To Bring Photo Editing To Google Photos. In San Francisco, the company is holding its first cloud userMicrosoft Dynamics AX Upgrade Integrates Power BI, Machine Learning. Read more ... » conference, a perch to show off its product and win more paying customers. Keynotes will come from new enterprise boss Diane Greene and Urs Hölzle, its veteran infrastructureGoogle Just Open Sourced TensorFlow, Its Artificial Intelligence Engine.

What makes the UK such a breeding ground for businesses working with Artificial Intelligence? We asked Alexandre Flamant and John Henderson, Co-founders of the LondonAI meetups, for their thoughts. Last week, a prototype programme from Google DeepMind achieved what many commentators thought would take at least another decade: it won a five match series of the ancient Chinese board game Go against reigning world champion Lee Sedol. In doing so, AlphaGo made a number of'creative' moves that flummoxed Go experts – no human would have ever played in such a way. This is true intelligence, even if currently confined to a specific board game.

These days, Google is making headlines as its artificial intelligence (AI) AlphaGo beated top pro Go player Lee Se-dol 2:0 in a highly publicized five-game Go series. The internet search giant is expanding its AI business by taking over four robotics companies including DeepMind which designed AlphaGo. But in Korea, AI is an underdeveloped and poorly invested sector. "Korean companies have not made much progress in AI research. They still have a long way to go in terms of AI commercialization," said Jin Jeong-yeol, director of the Kohyoung Technology.

Fast, novel, automated: threats are routinely getting past traditional security tools. Security now, more than ever, needs to be top of the CEO's agenda. We are seeing a host of new, innovative threats attacking companies on a daily basis. A recent example, detected by Darktrace's'immune system' approach, highlights how machine learning can help in this new era of advanced threat. Within a week of installing threat detection software into one customer's security stack, Darktrace discovered a serious compromise.

Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting, collecting, structuring and analyzing data present serious risks. For example, a recent Wall Street Journal article entitled "Tweets Provide New Way to Gauge TV Audiences" provides evidence of a disconnect between mainstream viewers and folks who use Twitter. The chart above shows the disconnect between the most popular and most tweeted shows - the most tweeted show is not a top ten show. While Twitter data can be useful for detecting trends and sentiments for certain areas (e.g., disease surveillance, natural disaster surveillance, product sentiments, financial trading, politics) in limited circumstances using scientific methods, it can also mislead and present a false view of reality.

Vivek Wadhwa is an academic, entrepreneur, and author who holds appointments at Stanford, Duke, and Singularity University. For the past century, the price and performance of computing has been on an exponential curve. And, as futurist Ray Kurzweil observed, once any technology becomes an information technology, its development follows the same curve, so we are seeing exponential advances in technologies such as sensors, networks, artificial intelligence, and robotics. The convergence of these technologies is making amazing things possible. Yes, with every good there is a bad; wonderful things will become possible, but with them we will also create new problems for mankind.

Machine learning is rapidly becoming one of the most important trends in the enterprise. A combination of Moore's law, the rise of big data, and the evolution of technology stacks have finally delivered the promise of machine learning technologies for many enterprises. However, machine learning extends beyond a standalone industry trend and has the opportunity to power the next wave of innovation in the enterprise. "From the lead technology trends in the market, machine learning appears to be on a trajectory to power the next wave of innovation in the enterprise" The last decade has seen a renaissance of innovation in enterprise software powered by movements like the cloud, mobile, and big data. With these trends established as mainstream technologies in the enterprise, the market is turning its attention to technologies that can become the "next big thing" in enterprise software.

Given the impressive advancement of machine intelligence in recent years, many people have been speculating on what the future holds when it comes to the power and roles of robots in our society. Some have even called for regulation of machine intelligence before it's too late. My take on this issue is that there is no need to speculate – machine intelligence is already here, with greedy robots already dominating our lives. The problem with talking about artificial intelligence is that it creates an inflated expectation of machines that would be completely human-like – we won't have true artificial intelligence until we can create machines that are indistinguishable from humans. While the goal of mimicking human intelligence is certainly interesting, it is clear that we are very far from achieving it.

Over three jam packed days, and over one hundred speakers, Interaction 16 explored interaction design of many flavors. Over the years the conversation has evolved, and as Josh Seidan put it, we are no longer asking'What is interaction design?' The discipline is evolving and growing more confident in its place in the world of AI, data, conversational UI as well as web and app work. The overall conference theme was the future of interaction design, and that came through strongly. Topics like data, self-driving cars, conversational UI, algorithms all got plenty of attention.