Chat apps are very popular and arguably, most people would prefer engaging in a chat conversation rather than make a phone call. Going by recent buzz, it's apparent that tech giants are planning to bring chat conversations to advertisers and marketers. But there are many startups out there already using chat conversations to simplify user queries. Yellow Messenger is one such Bangalore-based startup. Instead of a team of people answering your queries however, it has put in place an Artificial Intelligence-powered interface that converses with users and responds to their queries related to shopping, real estate, recharge, etc.

The cause of poor performance in machine learning is either overfitting or underfitting the data. In this post you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it. Overfitting and Underfitting With Machine Learning Algorithms Photo by Ian Carroll, some rights reserved. Supervised machine learning is best understood as approximating a target function (f) that maps input variables (X) to an output variable (Y). This characterization describes the range of classification and prediction problems and the machine algorithms that can be used to address them.

When the artificial intelligence, AlphaGo, beat Go champion Lee Seedol at his own game, it created history. AlphaGo didn't just beat Lee at Go, it won four games out of five, an unprecedented victory that many hadn't expected. AlphaGo's neural networks took thousands of Go matches, played by human players, and added its own learning on top of that, to come out with moves that even a legend like Lee Seedol didn't expect. But is AlphaGo the breakthrough in machine learning that the world has been waiting for? We talked to David Silver, Research Scientist, Google Deep Mind, to learn the same.

Central to the success of The AI Summit is the core focus on meeting and deal making. Our partners want tangible outcomes in terms of business development from the event; personal, pre-scheduled, 1:1 meetings using AI technology are the engine room for delivering this. Our emphasis on networking between delegates and solution providers starts the moment you join the event as a partner and continues well beyond the event's delivery. First of all, as you join the conference we'll ask you for your'wish-list'- your top targets that you would like to meet at The AI Summit. The top companies and related job titles, and we'll then go out and our research and invite the relevant individuals to attend on a complimentary basis while also securing a meeting with your organisation as part of their participation.

Have you ever had that moment when suddenly the things that you write, talk and worry about are happening to you? Surely they always happen to other people? I have been writing about artificial intelligence and my worry that business leaders don't think they need to understand it – it will only affect future generations. My white paper, March of the robots … into the boardroom, confirmed that the technology lethargy is worse even than I thought. Now, I find that technology is moving faster than even the experts had thought possible and is encroaching on areas of skills that had been thought of as exclusively human. So what has happened and will this finally mean bosses wake up and start thinking about the implications for their businesses – both as opportunities and risk?

Recently there's been quite a lot of talk about artificial intelligence. More specifically, the news has been covered with self learning and life like A.I./robots. From Microsofts @TayAndYou and Hanson Robotics's Sophia to Cornell's Self Walking Robot and ICTA, Australia's Self Cooking Robot…it has become mainstream. There's no stopping the coming of self learning/intelligent robots so I think it's best we discuss their potentially incredible uses, as well as their potentially devastating flaws. It's really important that we take AI seriously.

Daniel Faggella Crunch Network Contributor Daniel Faggella is founder of TechEmergence, a news and advice website for entrepreneurs and investors interested in the intersection of technology and the mind. How to join the network "Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." Everything You Know About Artificial Intelligence is Wrong. Will the Singularity Artificial General Intelligence winners be Hedge Fund Managers, the Military and Spy Agencies?

Three Day Rule, a matchmaking service with headquarters in Santa Monica, has raised 650,000 from the Dallas Angels Network investment group and 550,000 from several wealthy individuals. The company's 20 matchmakers -- spread across seven cities -- use software and intuition to find potential partners for customers, who pay about 4,000 for three months of assistance. Three Day Rule identifies matches from its own database, which includes people who have signed up free of charge at networking events, industry conferences and on the firm's website. But the pool goes beyond that. JDate, Christian Mingle and OkCupid allow Three Day Rule to choose from people listed on those dating websites too.

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Twitter thrives on its ability to understand both your tweets and the hot topic of the day, and it needs every bit of help it can get -- including from computers. Accordingly, the social network just snapped up Whetlab, a startup that makes it easier to implement machine learning (aka a form of artificial intelligence). The two companies are shy about what the acquisition means besides an improvement to Twitter's "internal machine learning efforts." However, the likely focus is on highlighting the content that's most relevant to you based on your activity and who you follow, as well as hiding abusive tweets before you have to reach for the "block" option. Whetlab's technology could get the ball rolling on these robotic discovery techniques much faster than before, and give you a custom-tailored Twitter experience that requires little effort on your part.