YouTube said Friday it is retooling its recommendation algorithm that suggests new videos to users in order to prevent promoting conspiracies and false information, reflecting a growing willingness to quell misinformation on the world's largest video platform after several public missteps. In a blog post that YouTube plans to publish Friday, the company said that it was taking a "closer look" at how it can reduce the spread of content that "comes close to -- but doesn't quite cross the line" of violating its rules. YouTube has been criticized for directing users to conspiracies and false content when they begin watching legitimate news. The change to the company's recommendation algorithms is the result of a six-month-long technical effort. It will be small at first -- YouTube said it would apply to less than 1 percent of the content of the site -- and affects only English-language videos, meaning that much unwanted content will still slip through the cracks.

When looking for answers to legal questions, people increasingly start their searches online. But what they find isn't always very useful--prompting the law schools at Stanford University and Suffolk University to team up to harness artificial intelligence (AI) to help people identify their specific legal issues. Historically, machines have struggled to understand context in human speech. For example, if someone says, "I'm getting kicked out of my house," most people understand that the person is not being physically kicked but is rather being removed from his or her home--or, to use the legal term, evicted. But machines typically can't understand "kicked out of my house" as "evicted" without being trained through a large number of similar questions.

Devan Y'Shaun Davis-Brooks, 17, was arrested after an off-duty cop allegedly heard him making threats to shoot up his former high school while playing an online video game. A 17-year-old Texas teenager was behind bars Friday after a vigilant off-duty cop playing an online video game allegedly overheard the teen planning a shooting at his former high school. Taylor police said Devan Y'Shaun Davis-Brooks was playing a video game early Wednesday morning and told other players he was going to shoot up Taylor High School. The teen also started bragging about a previous arrest, also for threatening his former school, cops said. Unbeknownst to the teenager, however, he was playing the game with an off-duty Fort Worth police officer, who reportedly heard the entire threat and immediately called Taylor police.

The pace of technological change is accelerating every year. How can you empower your teams to perform in such a fast-paced environment? To help you prepare for the changes to come in 2019, we've put together a list of the top learning and development trends you need to know about. You may find yourself using multiple devices on any given day. Training must be accessible and responsive across multiple devices; learners should be provided with a seamless, flexible learning experience on any device and at any time.

With CBSE introducing artificial intelligence as an elective paper, students and teachers must be very excited to know how can AI help the students' performance grow. It has been decided that the subject would be introduced in classes 8, 9 and 10 as a skill subject. Artificial intelligence is the ability of a machine to think, learn and perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making skills. Capabilities demonstrated by machines, including computers, from playing chess to operating cars and beyond, fall within the domain of artificial intelligence. The rapid spread of education among the masses in the industrial era made the'one-size-fits-all' method of learning the most convenient one for training subsequent generations of the workforce due to lack of resources.

On paper alone you would never guess that I grew up poor and hungry. My most recent annual salary was over $700,000. I am a Truman National Security Fellow and a term member at the Council on Foreign Relations. My publisher has just released my latest book series on quantitative finance in worldwide distribution. None of it feels like enough. I feel as though I am wired for a permanent state of fight or flight, waiting for the other shoe to drop, or the metaphorical week when I don't eat. I've chosen not to have children, partly because--despite any success--I still don't feel I have a safety net. I have a huge minimum checking account balance in mind before I would ever consider having children. If you knew me personally, you might get glimpses of stress, self-doubt, anxiety, and depression.

A new technique could reduce the time and discomfort of adjusting to a new prosthetic knee. A collaboration between researchers from North Carolina State University, the University of North Carolina and Arizona State University has resulted in a new technique that enables more rapid "tuning" of powered prosthetic knees, allowing patients to comfortably walk with a new prosthetic device in minutes, rather than hours after the device is first fitted After receiving the prosthetic knee, the device is tuned to tweak 12 different control parameters to accommodate the specific patient and address prosthesis dynamics like joint stiffness throughout the entire gait cycle. Traditionally, a practitioner works directly with the user to modify a handful of parameters in a process that could take several hours. However, by using a computer program that utilizes reinforcement learning--a type of machine learning--to modify all 12 parameters simultaneously, the new system allows patients to use their powered prosthetic knee to walk on a level surface after approximately 10 minutes of use. "We begin by giving a patient a powered prosthetic knee with a randomly selected set of parameters," Helen Huang, co-author of a paper on the work and a professor in the Joint Department of Biomedical Engineering at NC State and UNC, said in a statement.

Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient descent (SGD) on data generated by another network, called the teacher. We show how for this problem, the dynamics of SGD are captured by a set of differential equations. In particular, we demonstrate analytically that the generalisation error of the student increases linearly with the network size, with other relevant parameters held constant. Our results indicate that achieving good generalisation in neural networks depends on the interplay of at least the algorithm, its learning rate, the model architecture, and the data set.

Stochastic Gradient Descent (SGD) has played a central role in machine learning. However, it requires a carefully hand-picked stepsize for fast convergence, which is notoriously tedious and time-consuming to tune. Over the last several years, a plethora of adaptive gradient-based algorithms have emerged to ameliorate this problem. They have proved efficient in reducing the labor of tuning in practice, but many of them lack theoretic guarantees even in the convex setting. In this paper, we propose new surrogate losses to cast the problem of learning the optimal stepsizes for the stochastic optimization of a non-convex smooth objective function onto an online convex optimization problem. This allows the use of no-regret online algorithms to compute optimal stepsizes on the fly. In turn, this results in a SGD algorithm with self-tuned stepsizes that guarantees convergence rates that are automatically adaptive to the level of noise.

In the era of big data, the classical paradigm is to build huge data centers to collect and process users' data. This centralized access to resources and datasets simplifies some procedures, such as building predictive models with machine learning, but also comes with important drawbacks. From the company point of view, the need to gather and analyze the data centrally induces high infrastructure costs. As the server represents a single point of entry, it must also be secure enough to prevent attacks that could put the entire user database in jeopardy. On the user end, disadvantages include limited control over one's personal data as well as possible privacy risks, which may come from the aforementioned attacks but also from potentially loose data governance policies on the part of the companies.