A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of the DCM where the goal is to maximize the probability of recommending satisfactory items, such as web pages. The main challenge of our learning problem is that we do not observe which attractive item is satisfactory. We propose a computationally-efficient learning algorithm for solving our problem, dcmKL-UCB; derive gap-dependent upper bounds on its regret under reasonable assumptions; and also prove a matching lower bound up to logarithmic factors. We evaluate our algorithm on synthetic and real-world problems, and show that it performs well even when our model is misspecified. This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.

Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$, usually with much fewer columns than $A$, such that $A \approx HH^T$. SymNMF can be used for data analysis and in particular for various clustering tasks. In this paper, we propose simple and very efficient coordinate descent schemes to solve this problem, and that can handle large and sparse input matrices. The effectiveness of our methods is illustrated on synthetic and real-world data sets, and we show that they perform favorably compared to recent state-of-the-art methods.

The latest robotic cockroach can jump more than five feet in the air, and flip itself over to continue scurrying. Using a new method for storing energy and a height-adjustable trigger, the robo-roach can achieve more ground than those which rely solely on crawling. Though the enhanced jumping capabilities have been built into a small package for the project, the concept has potential to be scaled up for much larger robotics systems. The bug crawls across a desk before opening its'wings' to jump high in the air. The JumpRoACH features a height-adjustable trigger, allowing it to jump between 1.1 and 1.62 meters (3.6 – 5.2 feet) JumpRoACH has six feet for crawling and can move at a speed of up to .62 meters (2 feet) per second.

By analyzing Rembrandt's oeuvre, machine learning has simulated a new work by the celebrated artist Rembrandt, who died 347 years ago, has'created' a new work all thanks to machine learning. By studying how the major Impressionist painter created his trademark spotlight effect in many of his portraits, the team at Microsoft were able to render a painting that Rembrandt, in flesh, could have painted himself. The process began by building a 150 gigabyte-pile of data on Rembrandt's'artistic DNA.' Scans, including 2D and 3D, were upscaled with deep learning algorithms. The'Next Rembrandt' is a portrait of a man with white collar. After studying his portraits made between 1632 and 1642, the following attributes for the likely subject were discovered: Caucasian male with facial hair, in his thirties, with a white collar on black clothing, a hat and with the head tilted to the right. The team then used algorithms to compute Rembrandt's ideal proportions in his paintings.

Automated software assistants, or chatbots, are moving into the mainstream, helping you more efficiently order tacos, manage human resources tasks and generate sales leads from a messaging interface. As the latest plot point on artificial intelligence's long timeline, chatbots simulate natural language to make it seem as if you are engaging in a discussion with a human concierge who is acting on your behalf. Chatbots picked up steam thanks to messaging platforms such as WeChat and Slack, while Facebook, Microsoft and Google are building similar capabilities to bolster their platforms. Now businesses outside of the technology sector have begun building their own bots to strengthen connections among chat-happy millennials. Yet all of the bot-builders must clear a key hurdle: offering enough personalization to help their bots evolve from a nifty chat tool to an essential digital assistant.

A student in Ashok Goel's class last semester had a question: How long could the computer programs, or "agents," they were building take to solve problems? Since it was an online course, the student posted the question to the group discussion board. One teaching assistant replied, pointing to a portion of the assignment that set a 15 minute limit. The student clarified that their agent was running a little slow, and could take a bit longer. "It's fine if your agent takes a few minutes to run," she wrote. "If it's going to take more than 15 minutes to run, please leave notes in the submission about how long we should expect it to take.

With the Hollywood blockbuster Transcendence playing in cinemas, with Johnny Depp and Morgan Freeman showcasing clashing visions for the future of humanity, it's tempting to dismiss the notion of highly intelligent machines as mere science fiction. But this would be a mistake, and potentially our worst mistake in history. Artificial-intelligence (AI) research is now progressing rapidly. Recent landmarks such as self-driving cars, a computer winning at Jeopardy! and the digital personal assistants Siri, Google Now and Cortana are merely symptoms of an IT arms race fuelled by unprecedented investments and building on an increasingly mature theoretical foundation. Such achievements will probably pale against what the coming decades will bring.

It has taken me time to warm to the notion of robotics and Artificial Intelligence. But this year I have been working on a project which has created a need for me to hang out with IBM's NAO Robots, and immerse myself in the world of Artificial Intelligence, on a regular basis. And in doing so, I have collected my favourite use cases for organisations today. Dangerous and Dull Today in industries ranging from manufacturing, to mining, to the the aeronautics industry, there are tasks which, on one extreme are so dull and boring it is almost impossible to recruit resource to support, and on the other extreme, so dangerous that lives are regularly lost. Robots, infused with Artificial Intelligence (AI) are an awesome alternative to dull and dangerous .

Can a machine be creative? Google thinks so, and it has an entire team dedicated to teaching machines how to view the world a little more like us emotional humans. Think about computers as if they were children and it's simple to understand how coders can teach them to learn. Artificial intelligence is, at the beginning, very basic and simple. Human moderators instruct computers, showing them how to think and thus teach themselves.

This is a post based on the talk given at Warsaw Data Science Meetup on April 12th 2016. It's difficult to make predictions, especially about the future. Nowadays, whenever we hear about a recent breakthrough in science, technology or business, it is more than likely that the so-called'big-data' is part of the success. Whether it's a new medical algorithm to discover early stages of cancer or a new method for reducing traffic in big cities, massive amounts of digital data are very often a'secret ingredient'. As a matter of fact, everyone of us has probably heard at least a couple of the estimates on how much data human mankind creates every second, every minute or every hour.