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.

We consider binary classification problems in the presence of a teacher, who acts as an intermediary to provide a learning algorithm with meaningful, well-chosen examples. This setting is also known as curriculum learning [1, 2, 3] or self-paced learning [4, 5, 6] in the literature. Existing practical methods [4, 7] that employ such a teacher operate by providing the learning algorithm with easy examples first and then progressively moving on to more difficult examples. Such a strategy is known to improve the generalization ability of the learning algorithm and/or alleviate local minima problems while optimizing non-convex objective functions. In this work, we propose a new method to quantify the notion of easiness of a training example.

Scholars have examined the lawmaker's choice between rules and standards for decades. This paper, however, explores the possibility of a new form of law that renders that choice unnecessary. Advances in technology (such as big data and artificial intelligence) will give rise to this new form – the micro-directive – which will provide the benefits of both rules and standards without the costs of either. Lawmakers will be able to use predictive and communication technologies to enact complex legislative goals that are translated by machines into a vast catalog of simple commands for all possible scenarios. When an individual citizen faces a legal choice, the machine will select from the catalog and communicate to that individual the precise context-specific command (the micro-directive) necessary for compliance.

As you're choking down your latest serving of Trump Clinton Brexit Racism Terrorism Wealth Gap Climate Change Casserole, you could use some good news. Let's start with The Inevitable, the new best-seller by Kevin Kelly, one of our wisest technological prognosticators. "This is the moment that folks in the future will look back at and say, 'Oh to have been alive and well back then!'" Kelly writes. "There has never been a better time with more opportunities, more openings, lower barriers, higher benefit/risk ratios, better returns, greater upside than now. In the mid-2010s, we're getting the first sneak peeks at a bouquet of technologies that can vastly improve the lives of most people on the planet and solve some of our hardest problems--even climate change. Just consider for a moment how much everyday life has been transformed since 2007, when smartphones, social networks and cloud computing took off at about the same time.

When Stanford University student Joshua Browder began accumulating numerous parking tickets in London for minor violations last year, he realized he needed to do something. "After the fourth ticket, my parents said to me, 'You're on your own. We're not going to help you anymore,'" the Britain native told NBC News. Not wanting to pay, and upset with "local governments trying to get away with murder," the computer science and economics student wrote an appeal letter. To his, and his parents', surprise, it worked.

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data. It is popular in machine learning and artificial intelligence text books to first consider the learning styles that an algorithm can adopt. There are only a few main learning styles or learning models that an algorithm can have and we'll go through them here with a few examples of algorithms and problem types that they suit. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result. When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods.