MIT's scientists claim they can teach a new concept to a computer using a single example rather than thousands. They make use of an algorithm that takes advantage of "Bayesian Program Learning," or BPL. This is when a computer creates its own additional examples after being fed data, and then determines which ones fit the pattern best. The researchers behind BPL say they're attempting to recreate the way humans are able to learn a new task after seeing it done once. "The gap between machine learning and human learning capacities remains vast," one of the authors of the research paper, which was published last week in the journal Science, told GeekWire.

Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm has had immeasurable practical success, but it is not the complete story for understanding and inferring the preferences of people. First, peoples' preferences and their observable manifestations as ratings evolve over time along general patterns of trajectories. Second, an individual person's preferences evolve over time through influence of their social connections. In this paper, we develop a unified process model for both types of dynamics within a state space approach, together with an efficient optimization scheme for estimation within that model. The model combines elements from recent developments in dynamic matrix factorization, opinion dynamics and social learning, and trust-based recommendation. The estimation builds upon recent advances in numerical nonlinear optimization. Empirical results on a large-scale data set from the Epinions website demonstrate consistent reduction in root mean squared error by consideration of the two types of dynamics.

This interactive course will teach network security professionals machine learning techniques and applications for network data. This course is a continuation of the skills taught in the Crash Course in Data Science for Hackers. Students will learn various machine learning methods, applications, model selection, testing, and interpretation. Participants will write code to prepare and explore their data and then apply machine learning methods for discovery.

It has already been cheerfully offering advice to customers hoping to buy a phone in Tokyo, but Pepper the'emotional' robot is now about to enrol in school. The expressive humanoid, which has been developed by Japanese corporation SoftBank Robotics, is designed to identify and react to human emotions. It is now due to attend classes at Shoshi High School in Waseda, in the Fukushima Prefecture of Japan – making it the first time a robot will'study' alongside human students. Pepper the robot has become the world's first humanoid to enroll into a high school. Pepper is intended to be used for customer service in banks, shops and for greeting people. However, SoftBank has said it – or he as they company seems to prefer – could become a companion in people's homes in the future too.

If you enjoyed Jesse's presentation at ODSC's last Boston Big Data Conference come to ODSC East this May to hear out his colleagues. Rather than start with the statement of Bayes' Theorem, I want to use an old math teacher trick (which I realize many students hate) of trying to derive it from scratch, without stating what we're trying to derive. Rather, we'll start by modifying a problem that I described in an earlier post on probability distributions1. Bayes' gives you a way of determining the probability that a given event will occur, or that a given condition is true, given your knowledge of another related event or condition. All the examples that I've read or heard about seemed somewhat contrived and unrelated to the sorts of data analysis I was interested in.

A robot developed by the National Institute of Informatics is now smart enough to be accepted into most Japanese universities - but not the notoriously selective University of Tokyo. This artificial intelligence is called the Todai Robot Project, and aims to pass the entrance exam for the University of Tokyo in 2021. For the first time in its development, the AI program achieved an above-average score on a college entrance exam, which covered maths, physics, and english among other subjects. University of Tokyo, also called'Todai,' requires prospective students to take the general admissions test, the National Center Test for University Admissions, along with its own infamously difficult test The University of Tokyo, also referred to as'Todai,' is notorious for its extremely difficult entrance exam. Prospective students must take a general college entrance exam, the National Center Test for Admissions, along with the Todai test.

Madeline MacDonald says she was an 18-year-old freshman at Brigham Young University when she was sexually assaulted by a man she met on an online dating site. She reported the crime to the school's Title IX office. That same day, she says, BYU's honor code office received a copy of the report, triggering an investigation into whether MacDonald had violated the Mormon school's strict code of behavior, which bans premarital sex and drinking, among other things. Now MacDonald is among many students and others, including a Utah prosecutor, who are questioning BYU's practice of investigating accusers, saying it could discourage women from reporting sexual violence and hinder criminal cases. Tens of thousands have signed an online petition calling on the university to give victims immunity from honor code violations committed in the lead-up to a sexual assault.

Photographs used in Tsvetkov's study. You may think you do, but a recent experiment by a Russian photographer suggests otherwise. In a project entitled, "Your face is big data," Rodchenko Art School student, Egor Tsvetkov, began by photographing about 100 people who happened to sit across from him on the subway at some point. He then used FindFace, a facial-recognition app that taps neural-network technology, to try to track them down on Russian social media site VK. It was ridiculously easy to find 60 to 70 per cent of the subjects aged between 18 and 35 or so, he found, although for older people it was more difficult.

Parents, listen up: Put your kids in engineering and computer science classes. A recent Bureau of Labor Statistics report says "software development skills continue to be the most in-demand" STEM (science, technology, engineering, and math) related jobs in the United States, and the White House projects that there will be over one million unfilled jobs in STEM related fields by 2020. And perhaps one of the easiest ways to encourage this interest is with toys. Toys and kits that are designed to teach kids hacking and basic programming skills abound, and they cater to a range of ages and skill levels. "It's important that we create learning experiences for kids that help to see what's possible for them, what they can do, who they can be, and the changes that they can make to what's around them," said Eric Rosenbaum, who is an electronics kit designer and has a PhD from MIT's Lifelong Kindergarten group.

The great irony is that AI technology being honed and implemented right now could actually help increase diversity within the field itself, as tech companies leverage machine learning programs to pinpoint unconscious gender bias in the workplace. A slate of machine learning programs on the market utilize data and algorithms to spot diversity blind spots and help companies fill in the gaps. But eradicating bias isn't just politically correct; increasing gender diversity could change the face of AI research as well. There's a new theory floating around the engineering and computer science industries that women are far more likely to enroll and stay invested in the field if the work being produced is more societally meaningful. Programs that focus on humanistic applications for the greater good perform remarkably better where diversity is concerned: A new UC Berkeley Ph.D. program in development engineering boasted a 50 percent female enrollment rate in its inaugural 2014 class, and MIT's D-Lab, which aims to build technology to improve the lives of the impoverished, is 74 percent female.