Clustering is an underspecified task: there are no universal criteria for what makes a good clustering. This is especially true for relational data, where similarity can be based on the features of individuals, the relationships between them, or a mix of both. Existing methods for relational clustering have strong and often implicit biases in this respect. In this paper, we introduce a novel similarity measure for relational data. It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph. We experimentally evaluate how using this similarity affects the quality of clustering on very different types of datasets. The experiments demonstrate that (a) using this similarity in standard clustering methods consistently gives good results, whereas other measures work well only on datasets that match their bias; and (b) on most datasets, the novel similarity outperforms even the best among the existing ones.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

Adelyn Zhou is the chief marketing officer at Topbots, a research and education firm focused on connecting AI and bot technologies with Fortune 500 companies. A marketing and technology leader who has worked with Amazon, Nextdoor, Eventbrite, Oscar Health and others, she has been recognized as one of the top 50 people in growth as well as one of the top 30 people to follow in AI by IBM Watson. Adelyn is active participant in the largest bot group on Facebook (24,000 members) and writes for VentureBeat and Forbes. She graduated with honors from Harvard University, and received her MBA from Harvard Business School. I started my career in marketing at multiple fast growing technology companies.

International Business Machines Corp. and Salesforce.com Inc. agreed to mingle their artificial-intelligence technologies in a bid to boost sales of the powerful data-analytics offerings. The companies Monday announced plans to offer integrated AI services that weave the broad humanlike conversation and learning capabilities of IBM's Watson with Salesforce's more sales-oriented Einstein technology. The new offerings, available in the second half of the year, are aimed at helping a wide variety of companies better target products and services at customers. Ginni Rometty, CEO of IBM, said that she believes that believes artificial intelligence will help to create jobs and that clients will have a "symbiotic relationship" with AI. IBM IBM, 0.23% and Salesforce CRM, 0.34% believe the partnership will work because their AI technologies have different capabilities.

At the last meetup we mentioned that we have created a survey to understand your experience of the meetup, what you want get from it and how we should develop it in the future. We (the meetup organisers) would be very grateful if you could take a few minutes to complete the survey. We will leave it live for a couple more weeks and let you know some of the main findings at the next meetup (which will be in Accenture on 27th March). Just click the bit.ly/mldublin link to get started or paste this link into a browser: bit.ly/mldublin

A study of 100 people from a variety of ages and technical aptitudes conducted at business school Bentley University in Massachusetts found that just as with other humans, people form first impressions of chatbots that stick. The study was conducted as part of the school's Human Factors and Information Design program, by students working under Bentley adjunct lecturer Meena Kothandaraman, founder of the twig fish research practice, and in partnership with NeuraFlash, a Boston-based firm that uses AI for Salesforce consulting. The takeaway for botbuilders is obvious: However sophisticated your bot, or whether it hangs out with Maroon 5, its first greeting and responses to new contacts will determine whether they find it an automated annoyance or a new best friend.

Sports analytics is routinely used to assign values to such things as shots taken or to compare player performance, but a new automated method based on deep learning techniques - developed by researchers at Disney Research, California Institute of Technology and STATS, a supplier of sports data - will provide coaches and teams with a quicker tool to help assess defensive athletic performance in any game situation. The innovative method analyzes detailed game data on player and ball positions to create models, or "ghosts," of how a typical player in a league or on another team would behave when an opponent is on the attack. It is then possible to visually compare what a team's players actually did during a defensive play versus what the ghost players would have done. "With the innovation of data-driven ghosting, we can now, for the first time, scalably quantify, analyze and compare detailed defensive behavior," said Peter Carr, research scientist at Disney Research. "Despite what skeptics might say, you can indeed measure defense."

A human study participant sends feedback on a robot's success rate as it sorts objects. For many of us, picking up on someone else's disapproval can lead to anxiety or defensiveness, but for a new mind-monitoring robot, unspoken criticism is the very best kind. In their pursuit of a seamless brain-bot interface for letting humans control machines with their minds, researchers from MIT's Computer Science and Artificial Intelligence Laboratory have created a feedback system that lets robots detect and correct their errors based solely on a human's unspoken observation. Based on brain activity from an electroencephalography (EEG) monitor, the system detects when hooked-in persons notice an error in its sorting work, and adjusts its behavior accordingly within milliseconds. An illustration shows the communication loop between human subjects and ErrP-sensitive robots.

Baxter is but a child, with bright eyes and a subtle grin. It sits at a table and cautiously lifts a can of spray paint, then dangles it over a box marked "WIRE." The error seems to smack Baxter across the face--its eyebrows furrow and blush appears on its cheeks. It swings its arm to an adjacent box marked "PAINT" and drops in the can with a clunk and that spray-paint rattle. "Good," says a voice off-screen, as Baxter's face reverts to a grin.

Unpicking the secrets of the brain's reward system has earned three neuroscientists a reward of their own. Wolfram Schultz, Peter Dayan, and Ray Dolan have today been awarded the €1 million Brain Prize by Denmark's Lundbeck Foundation. The prize recognises researchers who have made vital contributions to understanding how our brains work. Together, their research has revealed how reward systems in the brain that involve the signalling chemical dopamine influence our behaviour and survival, playing important roles in decision-making, gambling, drug addiction, psychopathic tendencies, and schizophrenia. "This is the biological process that makes us want to buy a bigger car or house, or be promoted at work," says Wolfram Schultz, at the University of Cambridge.