With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important topic in social network analysis. Machine learning methods can potentially help provide better understanding of this phenomenon, but they must address several key challenges: the rapidly changing vocabulary involved in cyber- bullying, the role of social network structure, and the scale of the data. In this study, we propose a model that simultaneously discovers instigators and victims of bullying as well as new bullying vocabulary by starting with a corpus of social interactions and a seed dictionary of bullying indicators. We formulate an objective function based on participant-vocabulary consistency. We evaluate this approach on Twitter and Ask.fm data sets and show that the proposed method can detect new bullying vocabulary as well as victims and bullies.

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In practice data are often multi-way, or measured over multiple dimensions. For example, metabolite abundance may be measured over multiple regions or tissues, or gene expression may be measured over multiple time points, for the same subjects. We propose a framework for linear classification of high-dimensional multi-way data, in which coefficients can be factorized into weights that are specific to each dimension. More generally, the coefficients for each measurement in a multi-way dataset are assumed to have low-rank structure. This framework extends existing classification techniques, and we have implemented multi-way versions of SVM and DWD. We describe informative simulation results, and apply multi-way DWD to data for two very different clinical research studies. The first study uses metabolite magnetic resonance spectroscopy data over multiple brain regions to compare patients with and without spinocerebellar ataxia, the second uses publicly available gene expression time-course data to compare treatment responses for patients with multiple sclerosis. Our method improves performance and simplifies interpretation over naive applications of full rank linear classification to multi-way data. An R package is available at https://github.com/lockEF/MultiwayClassification .

Should a self-driving car kill its passengers for the greater good – for instance, by swerving into a wall to avoid hitting a large number of pedestrians? Surveys of nearly 2,000 US residents revealed that, while we strongly agree that autonomous vehicles should strive to save as many lives as possible, we are not willing to buy such a car for ourselves, preferring instead one that tries to preserve the lives of its passengers at all costs. Driving our own cars might be a enjoyable pursuit, but it's also responsible for a tremendous amount of misery: it locks out the elderly and physically challenged and is the primary cause of death, worldwide, for people aged 15 to 29. Every year, over 30,000 traffic-related deaths and millions of injuries, costing close to a trillion dollars, take place in the US alone (worldwide, the numbers approach 1.25 million fatalities and 20 to 50 million injuries a year). And, according to numerous studies, human error has been responsible for at least a staggering 90 percent or more of these accidents.

As readers of my blog will know, I want to learn more about machine learning. I've managed to run some samples, and I've built my own first little samples. It feels like the next step is to understand more about the different algorithms, for example when to pick which one and how to tune the parameters to achieve the best results. To learn more, I've started to watch the first hours of the awesome courses below. The courses are a great introduction to machine learning and very different from most other videos I found which often seem to assume you are already a data scientist.

In the coming decade, as X Prize strives to achieve its impact mission through incentive competitions and crowd-sourcing, we see tremendous opportunity in this emerging generation of problem solvers to use AI to solve humanitys grandest challenges," X Prize CEO Marcus Shingles said in a statement. "The IBM Watson AI X Prize is intended to promote and progress the notion of AI for impact among the global bold innovator crowd, both the established community of practitioners, as well as encourage newcomers to experiment and ultimately demonstrate how AI can be used as a tool for good.

IBM Watson joined forces with the X Prize Foundation to launch an open 5 million challenge to build an artificial intelligence app for healthcare that could also be used in other industries, including education, energy, the environment, global development or even exploration. "In the coming decade, as X Prize strives to achieve its impact mission through incentive competitions and crowd-sourcing, we see tremendous opportunity in this emerging generation of problem solvers to use AI to solve humanity's grandest challenges," X Prize CEO Marcus Shingles said in a statement. "The IBM Watson AI X Prize is intended to promote and progress the notion of'AI for impact' among the global bold innovator crowd, both the established community of practitioners, as well as encourage newcomers to experiment and ultimately demonstrate how AI can be used as a tool for good." Unlike previous X Prizes, including the Tricorder X Prize, in which companies are vying to develop a handheld medical scanner, and the original Ansari X Prize for suborbital flight, this contest allows the participants to define their own goals and to focus on solving different problems. "Rather than set a single, universal goal for all teams, this competition allows teams to define their own challenges and demonstrate their solutions, encouraging myriad problem-solving approaches," Amir Banifatemi, X Prize lead for the IBM Watson AI competition, said in a statement.

PARIS – A tropical fish can tell one human face from another despite lacking a brain section that homo sapiens and other "smart" animals use for this task, scientists said. The astonishing ability was demonstrated in experiments with eight archerfish, a tropical species best known for spitting pressurized water jets to shoot prey out of the air. Instead of aiming at bugs, the sharpshooting fish were taught to spit at pictures of human faces displayed on a computer monitor suspended over their aquarium. "We were pleasantly surprised at the speed at which the fish learned as well as their high degree of accuracy," said study co-author Cait Newport of the Oxford University's department of zoology. The fish, which require excellent vision for hunting, were first introduced to two faces, and conditioned to spit at one of them in exchange for a food reward.

This image, from a 1962 internal General Electric document, conveyed the idea of random access storage using a set of "pigeon holes" in which data could be placed. Fifty-three years ago a small team working to automate the business processes of the General Electric Company built the first database management system. The Integrated Data Store--IDS--was designed by Charles W. Bachman, who won the ACM's 1973 A.M. Turing Award for the accomplishment. Before General Electric, he had spent 10 years working in engineering, finance, production, and data processing for the Dow Chemical Company. He was the first ACM A.M. Turing Award winner without a Ph.D., the first with a background in engineering rather than science, and the first to spend his entire career in industry rather than academia.

Bots (short for software robots) have been around since the early days of computers. One compelling example of bots is chatbots, algorithms designed to hold a conversation with a human, as envisioned by Alan Turing in the 1950s.33 The dream of designing a computer algorithm that passes the Turing test has driven artificial intelligence research for decades, as witnessed by initiatives like the Loebner Prize, awarding progress in natural language processing.a Many things have changed since the early days of AI, when bots like Joseph Weizenbaum's ELIZA,39 mimicking a Rogerian psychotherapist, were developed as demonstrations or for delight. Today, social media ecosystems populated by hundreds of millions of individuals present real incentives--including economic and political ones--to design algorithms that exhibit human-like behavior. Such ecosystems also raise the bar of the challenge, as they introduce new dimensions to emulate in addition to content, including the social network, temporal activity, diffusion patterns, and sentiment expression. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior. Social bots have inhabited social media platforms for the past few years.7,24

A team from the University of Oregon have developed a system that can read people's thoughts via brain scans, and rebuild the faces they were visualising in their heads. The study, led by Brice Kuhl and Hongmi Lee from the University of Oregon, used artificial intelligence (AI) that analysed brain activity in an attempt to reconstruct one of a series of faces that participants were seeing. It's not an exact science, but the AI did get close. "We can take someone's memory – which is typically something internal and private – and we can pull it out from their brains," Kuhl told Vox. "Some people use different definitions of mind reading, but certainly, that's getting close," Kuhl told Vox. Kuhl and his colleague Lee recently published a paper in The Journal of Neuroscience with a conclusion straight out of science fiction: Kuhl and Lee created images directly from memories using an MRI, some machine learning software, and a few hapless human guinea pigs.