Ordinarily I greet Facebook events with a big, indifferent yawn. But the Facebook F8 developer show earned my eyeballs (full video replays here). The big question is: why bother? F8's centerpiece announcements pertained to Facebook Messenger – not exactly the app enterprise IT managers are eager to incorporate. But Facebook Messenger is no longer just a place to stalk annoy your old schoolmates. With the debut of several new bot-powered Messenger services, the commercial viability of AI (artificial intelligence) services is a pressing debate.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One of the places machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, called computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet. This is useful to scientists and policy-makers because it can help them develop plans for how to live and survive in our changing world.

Royal Bank of Scotland (RBS), an organization whose brands provide financial products and services to personal and business customers in the UK and beyond, is putting artificial intelligence (AI) to work in its customer support department. Luvo, a web chat application, is being used to help EBS customer support staff when they aren't sure of an answer to a customer's question. Luvo is capable of fielding questions by sifting through a vast amount of data very quickly. If an answer can't be determined, the application directs the question to a human who can answer more challenging questions. Think of Luvo as live chat, but instead of a human responding, it's a machine. The AI application was originally tested in a two-month trial of 1,200 employees who managed relationships with small businesses, before it was deemed successful.

Congratulations are in order for the folks at Google Deepmind who have mastered Go. However, some of the discussion around this seems like giddy overstatement. Wired says Machines have conquered the last games and Slashdot says We know now that we don't need any big new breakthroughs to get to true AI. The truth is nowhere close. For Go itself, it's been well-known for a decade that Monte Carlo tree search (i.e.

I have trained a SVM and logistic regression classifier on my dataset for binary classification. Both classifier provide a weight vector which is of the size of the number of features. I can use this weight vector to select the 10 most important features. For doing that I have turned the weights into t-scores by doing a permutation test. I did 1000 permutations of the class labels and at each permutation I calculated the weight vector.

After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.

After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.

An artificial intelligence system (AI) at the Massachusetts Institute of Technology has taken an IQ test designed for a young child. The results, written up by MIT Review, revealed that its IQ was equivalent to that of a four-year-old. Increasingly machines are getting better at specific tasks such as playing chess, recognising pictures and making complex commutations. But general intelligence is still proving elusive for most of them. The MIT machine - dubbed ConceptNet4 - was put through its paces by researchers from the University of Illinois, led by Stellan Ohlsson.

Beth Kutscher is based in Mountain View, Calif., and covers healthcare innovation and digital health. Before joining Modern Healthcare in 2012, she was a content editor and healthcare reporter for the New York-based Mergermarket Group. She previously was news and online editor for Pharmaceutical Executive and deputy news editor and healthcare reporter for PRWeek. She has a bachelor's degree in English from Cornell University.

I'm trying to get into neural networks. There have been a couple big breakthroughs in the field in recent years and suddenly my side project of messing around with programming languages seemed short sighted. It almost seems like we'll have real AI soon and I want to be working on that. While making my first couple steps into the field it's hard to keep that enthusiasm. A lot of the field is still kinda handwavy where when you want to find out why something is used the way it's used, the only answer you can get is "because it works like this and it doesn't work if we change it."