The past couple of decades have seen technology forge ahead relentlessly, enabling us to communicate in unprecedented ways, work more smartly and from any location, carry our phones in our pocket and much more. And artificial intelligence has evolved somewhat too, alongside all of the'usual' tech that we use daily, bringing with it a more urgent perceived need to consider the threat that it might pose. Bill Gates has come out to state that he's firmly in the'AI is a threat camp', so too has Stephen Hawking, one of the greatest thinkers of our time. But not everyone is in agreement with them. At the Dublin Web Summit, I attended a talk – the name now escapes me unfortunately when it comes to who it was by – and when the speaker, a high profile tech name (that much I do know) was asked about the threat posed by AI, he said something that I found interesting.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (sixth in a series of seven) is a look at robotics. Be sure to read the first five posts if you haven't already: When the World Is Wired: The Magic of the Internet of Everything Where Artificial Intelligence Is Now and What's Just Around the Corner The Near Future of VR and AR: What You Need to Know Drones Have Reached at Tipping Point--Here's What Happens Next How 3D Printing Is Transforming the Way We Make Things An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., 3D Printing), but try to predict the future when AI, robotics, VR, drones, and computation are all doubling, morphing and recombining…You have a very exciting (read: unpredictable) future. This post is the result of an interview with Rodney Brooks on the top five recent robotics breakthroughs (2012-2015) and the top five anticipated robotics breakthroughs (2016-2018). Rodney is the Panasonic Professor of Robotics at MIT.

This week humankind was delivered a body blow by an artificial intelligence (AI) called AlphaGo that beat Go's world champion, Lee Sedol, so is it now time for humans to let the machines rule the world? Not just yet--while this adds to a growing list of machines that have beaten the best humans at chess, checkers and backgammon, Lee Sedol won a game back against AlphaGo, so there is still hope for us. The ancient Chinese strategy game Go has substantially more moves to consider each turn than chess. With the two players having to look several moves ahead with more possible outcomes than there are atoms in the universe before deciding what move to make. For each move in a game such as Go, the AI uses a tree search that plays out scenarios, notes which lead to the most victories, and then works back to find out the next move that will lead to the best scenario.

Twenty years ago, Louis Gerstner transformed IBM by emphasizing consulting and technology services -- not just technology -- to solve customer problems. Today, as the wave of digitization continues to grow and envelope all the world's enterprises, IBM is at a crucial juncture once again. CEO Ginni Rometty is leading the company into new areas, betting big on its Watson software and cloud computing. But these new services have yet to grow fast enough to supplant the profit declines in the company's eroding legacy products. This time, the transformation IBM faces is far more difficult, for two reasons.

Geoffrey Hinton, an artificial intelligence (AI) expert who splits his time between Google and the University of Toronto, believes machines could match human abilities in five years. Hinton, known as the godfather of "deep learning," said the most powerful machines are still about a million times smaller than the human brain. They only have the equivalent of around a billion synapses (the connections between the neurons in the brain), compared to 1,000 trillion in the human brain. But machines are becoming more sophisticated every year. When asked to predict how long it will take before machines possess human-level abilities, Hinton said: "More than five years. I refuse to say anything beyond five years because I don't think we can see much beyond five years."

Look at it just sitting there on a wall. South Korea's robots are future-proof. A rescue drone by the Korean Advanced Institute Of Science and Technology is a flying wall-climbing fireproof building inspection machine. Named CAROS, for Climbing Aerial RObot System, looks like a normal quadcopter. It flies to and fro like a quadcopter, then turns perpendicular to the ground and attaches itself to surfaces it needs to inspect.

H2O.ai is on a mission to empower everyone with the tools to easily develop high performance, production-ready, machine learning models and we are inviting you to join us on this journey! H2O is an open source, distributed machine learning platform with interfaces in R, Python, Scala, Java, REST/JSON, as well as a web interface called Flow. H2O can train models on a large Hadoop or Spark cluster just as easily as training models on a laptop. H2O.ai is the startup headquartered in Mountain View, CA, behind the H2O software product. Data Science Intern, you'll be tasked to apply your analytical skills to industry-focused data science applications, in addition to development of the H2O software itself.

Text clustering is a widely used techniques to automatically draw out patterns from a set of documents. This notion can be extended to customer segmentation in the digital marketing field. As one of its main core is to understand what drives visitors to come, leave and behave on site. One simple way to do this is by reviewing words that they used to arrive on site and what words they used ( what things they searched) once they're on your site. Another usage of text clustering is for document organization or indexing (tagging).

However, mastery in the subject can only be achieved by adding profundity to one's knowledge. One such facet involves learning how to deal with the assumptions and drawbacks of the various algorithms being used. In a post for KDnuggets, Ex-Google engineer Cheng-Tao Chu goes into seven mistakes to avoid for the aspiring Machine Learning expert. Among his seven points, Chu talks about picking a suitable evaluation metric for your model that fits the domain in which it is being applied, being cognizant of and dealing with outliers carefully, and avoiding models which tend to overfit when dealing with data where the number of features outnumbers the number of data points.

The app contains Online content since adding offline content will make the app heavy. It consumes very small amount of data since it is only notes and diagrams which are being fetched.