Professors at the University of California, Berkeley have fully immersed themselves and their students into the world of virtual and augmented reality. The group is currently working to develop ISAACS (Immersive Semi-Autonomous Area Command System), a software which will help humans discover a robot's intentions. "If there's a robot over there - scary or not - you look at this robot and have no idea what this robot is trying to do. You have no idea what this robot can do," said Dr. Allen Yang, executive director for the Center for Augmented Cognition at UC Berkeley in an interview. ISAACS will be accessible via AR headsets and has the capability of determining a robots intentions just by looking at it.

That crazy video game series about high school students who like to befriend and battle demons is getting another release today. And it might very well be the best one yet. We're talking about Persona, of course, a series of narrative-based role-playing games featuring edgy, relatable characters that make playing through each story an absolute joy. Originally released for the original PlayStation as Revelations: Persona back in 1996, it's since grown into a massively popular franchise that attracts both casual and hardcore players. It's come a long way since the first major spinoff from the Shin Megami Tensei role-playing series from which it was born.

Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database search operators with an information theoretic ranking function called predictive relevance. Predictive relevance can be calculated by a fast sparse matrix algorithm based on posterior samples from CrossCat, a nonparametric Bayesian model for high-dimensional, heterogeneously-typed data tables. The result is a flexible search technique that applies to a broad class of information retrieval problems, which we integrate into BayesDB, a probabilistic programming platform for probabilistic data analysis. This paper demonstrates applications to databases of US colleges, global macroeconomic indicators of public health, and classic cars. We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.

It has been heavily criticised for its woeful response to removing jihadi videos and other shocking pages from the internet. But Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. The tech giant has long been condemned for its inability to control what is posted on its online platforms. Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. Google now wants the computers which monitor content being uploaded through YouTube and other channels to understand the nuances of what makes a video offensive.

It's now becoming common for me to hear that product owners/managers, technical managers and designers are turning to popular online courses to learn about machine learning (ML). I always encourage it -- in fact, I did one of those courses myself (and blogged about it). However, it's not always clear how much benefit someone whose goal is to design, support, manage, or plan for products that use machine learning will get from doing an online course in ML. These courses throw you into the deep end, asking you to start programming classifiers, when many non-technical team mates are only looking for sufficient knowledge to be able to work in teams that are creating an ML-driven product. It's a bit like wanting to drive a car, and'therefore' signing up to a course on combustion engines -- probably a little bit too detailed for practical day-to-day driving!

Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

AI is touted to change the world in the same way electricity did. But are we moving too fast? While AI has created impact in some sectors, there are some documented instances of enterprises claiming to have automated systems, but actually relying on humans behind the scenes. For our future to stay bright, the real AI startups will need to stand up. On March 22, 2017, Andrew NG, who had been heading Artificial Intelligence (AI) efforts at Chinese tech giant Baidu, announced that he was resigning from the company.

This article is by Featured Blogger Isaac Sacolick from his blog Social, Agile, and Transformation. AI is coming along with artistic impression. There is Paul and e-David that can draw, an AI that can write a Beatles inspired song, followed by an AI that wrote a Christmas song. There's been a lot of work on natural language processing, but natural language understanding remains elusive. It's hard to keep up with all of AI's creative works and how fast AI will go from pattern-based expressions to truly creative ones.

A new global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise. The threat that automation will eliminate a broad swath of jobs, across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. But here's what we've been overlooking: Many new jobs will also be created -- jobs that look nothing like those that exist today. In Accenture's global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs.

Higher Educational Failure - Colleges and universities are still educating the majority of its students for the last century, not this one. Students graduating from medical school, law school, education, journalism, and dozens of other majors have no training in how robotics or AI will impact them. Higher education needs to rethink curriculum for the future. Massive, Permanent Underclass - More than 26m people in the US perform some form of manual labor as their sole source of income. Older Americans are, because of the high costs of health insurance and benefits, with no long-term loyalty by a company, much harder to reemploy.