After many false beginnings, artificial intelligence (AI) has made astonishing progress in the past few years. AI, sometimes referred to as cognitive computing, refers to computers learning how to complete tasks traditionally done by humans. Thanks to a versatile technique called "deep learning," large neural computer networks modeled on the architecture of the human brain, can be trained to do all kinds of tasks if given enough data. This rapid progress has led to concerns about job losses in the legal sector. Some argue that advances in AI will result in the creation of an "iParalegal" that sifts through a firm's knowledge management system, picking out and synthesizing relevant case law and performing tasks correlating to previous matters using transaction data that proved successful. AI would utilize game theory algorithms to sort out negotiations, propose litigation tactics, and complete due diligence and discovery processes in seconds.

Andrew Ng [Co-Founder of Coursera, Stanford Professor, Chief Scientist at Baidu, and All-Around Machine Learning Expert] is writing a book during the summer of 2016. The book is titled, Machine Learning Yearning. It you visit the site and signup quickly you can get draft copies of the chapters as they become available. Andrew is an excellent teacher. His MOOCs are wildly successful, and I expect his book to be excellent as well.

For more than 65 years, computer scientists have studied whether robots' behavior could become indistinguishable from human intelligence. But while we've focused on machines, have we ignored changes to our own capabilities? In a book due to be published next year, Being Human in the 21st Century, a law professor and a philosopher argue that we've overlooked the equally important, inverse question: Are humans becoming more like robots? In 1950, computer scientist Alan Turing put forward what's now known as the "Turing Test." Essentially, Turing proposed that a key test of machine thinking is whether someone asking the same questions to both a human and a robot could tell which is which. This has since become an important method to evaluate artificial intelligence, with regular Turing Test competitions to determine the extent of robots' growing ability to mimic human behavior.

Back on the judging panel is Professor Noel Sharkey, a robotics expert who featured on the programme for five years. Sharing memories of his time on the show, he told the BBC he was initially hesitant to take part... I was a very reluctant judge for the first series of Robot Wars. I was concerned that being on a popular TV show might damage my reputation as a university professor and as an educator. Even after the first series, I thought that I should opt out - but then something quite remarkable happened.

You can actually enter machine learning with a lot of different states of mathematical knowledge. There are a lot of courses which try to reduce the requirements you need as much as possible and come from a very applied side (most of the MOOCs). Here you need the basic math to calculate some results, but it is more just well if we optimize this function then this will be the solution etc. You will be ready to apply a lot of the methods out there and maybe twist them so that they fit your dataset but it won't help you so much if you want do derive new models yourself or understand where these models came from. If you really want to understand the mathematical framework behind a lot of methods (which is quite beautiful, for example kernels/svms) then you probably will need a lot of high math and should just start with doing some linear algebra or analysis book (to find good starter books go to some good university website, check their linear algebra 1 or analysis 1 courses and see which books the professors recommend).

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Insights powered by machine learning and enhanced user profiles are among improvements to Jive's Interactive Intranet and Customer Community solutions. Taking a cue from Facebook and other social channels that attempt to surface information most relevant to users, Jive announced some new tweaks to its Interactive Intranet and Customer Community collaboration solutions. Elisa Steele, CEO at Jive Software, said in a statement that the idea is to "bring employees, customers and partners together within one unified WorkHub." The new enhancements make work "more visible, searchable and memorable." One of the most notable new features is a Recommender Engine that uses machine learning to deliver relevant experiences and content to users.

Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space and interactions are more likely to form between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space, with a quadratic convergence rate. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.