The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in the presence of missing information. Unlike conventional clustering techniques where every feature is known for each point, our algorithm can handle cases where a few feature values are unknown for every point. For this more challenging problem, we provide theoretical guarantees for clustering using a $\ell_0$ fusion penalty based optimization problem. Furthermore, we propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. It is observed that this algorithm produces solutions that degrade gradually with an increase in the fraction of missing feature values. We demonstrate the utility of the proposed method using a simulated dataset, the Wine dataset and also an under-sampled cardiac MRI dataset. It is shown that the proposed method is a promising clustering technique for datasets with large fractions of missing entries.

Dr. Roberto Paredes joined in 2000 the Computer Science Department of the Universidad Politécnica de Valencia (UPV), where he is until now serving as an Associate Professor. His current fields of interest include Statistical Pattern Recognition, Machine Learning, Biometrics, Large-scale problems, Multimedia Retrieval and Relevance Feedback. Dr. Paredes is the head of the PRHTL Research Centre and former President of the Spanish AERFAI Association. Moreover he is now the CTO and Co-founder of Solver Machine Learning, a spin-off of the UPV. Nowadays he is focused mainly in Deep Learning techniques and some of his DL solutions are applied to different sectors where Solver Machine Learning is working on.

Artificial intelligence software and robots are powerful in pattern recognition, predictive analytics, heavy computations, and handling repetitive tasks. Thanks to these capabilities, machines are gradually replacing humans in many occupations and activities, to extent of a growing concern about the impact of automation on the job market. While the power of AI is indisputable, the question arises how far will automation go and what will be its impact on employees, organizations, and business processes. The main question is -- will AI become the next boss for the majority of employees? Most experts agree that the majority of occupations will be partly or fully automated in the near future. In practice, this means that employees will be either fully replaced by machines, or begin working with them as assistants, trainers, or subordinates.

In the past, a bad meal or poor service could cost a restaurant a customer, and perhaps that customer's family and friends. Today, thanks to social media and the exponential distributive power of viral networks, a single diner's experience of cutting into an overcooked steak or encountering a surly waiter can influence thousands and quickly become a PR nightmare that impacts revenue and damages brand reputation. Despite the risk of negative publicity on social media, many restaurant chains are passive about developing strategies to respond to snarky reviews and hostile comments. One rationale is that it's impossible to monitor vast social networks for any and every negative mention of a restaurant brand. Another is that giving rewards or freebies to complainers sets a precedent for additional giveaways. Yet evidence suggests that restaurants that do respond proactively to complaints on social media and take steps to make things right with disgruntled customers are perceived favorably.

Sexism is so deeply ingrained in the way we think about the world, we've actually passed it on to our computers, according to a new University of Virginia and University of Washington study. Artificial intelligence is more likely to label people who are cooking, shopping, and cleaning as women and people who are playing sports, coaching, and shooting as men. UVA computer science professor Vicente Ordóñez got the idea for the experiment when he noticed that his image-recognition software was associating photos of kitchens with women. After training software using two photo collections that researchers use to create image-recognition software, including one supported by Facebook and Microsoft, he and his colleagues found that not only do these collections contain gender bias--they multiply that bias when they pass it on to the software. The program these photo sets produced actually labeled a man a "woman" because he was standing by a stove.

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.

Advances in artificial intelligence mean applications increasingly can take on image recognition capabilities that allow them to identify objects, detect the age of human faces and screen out adult content. The Department of Homeland Security has worked for several years to implement a biometric monitoring system to verify travelers in U.S. airports, and they recently found success with a Customs and Border Protection pilot. The system uses facial recognition software to compare photos of passengers against a database, allowing DHS officials to identify travelers who have overstayed visas or are wanted in criminal investigations. These developments underscore the need for the government to remain abreast of ways to manage complex technology and maintain standards of living. Image recognition software has real-world implications for local governments and can help officials efficiently integrate and manage assets.

The Aim of the Conference The aim of the conference is to bring together researchers from all over the world who deal with machine learning and data mining in order to discuss the recent status of the research and to direct further developments. Basic research papers as well as application papers are welcome. Paper submissions should be related but not limited to any of the following topics: association rules case-based reasoning and learning classification and interpretation of images, text, video conceptional learning and clustering Goodness measures and evaluaion (e.g. Long Paper The paper must be formatted in the Springer LNCS format. They should have at most 15 pages.

So I decided to sit down and take advantage of the free course on it that Coursera is running. I'm starting with a pretty strong background working with data and finding practical applications, so this seems like the next step. Machine learning has taken Gary Kasparov, once at the peak of professional chess, and turned him into an author and political opponent of Putin (not exactly bright career paths). Go, a complex Asian strategy game, was the next to fall in May of this year. The latest instance occurred on Friday, with an AI program defeating a human player in a first person shooter eSport competition.

Not long ago, my aunt sent her colleagues an email with the subject, "Math Problem! She thought her solution was obvious. Her colleagues, though, were sure their solution was correct--and the two didn't match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn.