New York University's Information Law Institute in collaboration with the White House is slated to host an extensive public symposium on Thursday, July 7 in a bid to focus on the near-term influence of AI (artificial intelligence) technologies across the economic and social systems. A brand-new series of workshops and an inter-agency working group that will delve into understanding more about the advantages and hazards of artificial intelligence was announced by the White House on May 3, 2016. There is a lot of excitement surrounding artificial intelligence and how to create computers that are capable of intelligent behavior. Following years of consistent but sluggish progress on making computers smarter at mundane tasks, a slew of developments in the research community and industry have recently sparked momentum and investment in the advancement of this work. The Social and Economic Implications of AI Technologies in the Near-Term will concentrate on problems of the next five to 10 years, particularly focusing on four themes; What impact AI will have on social inequality, ethics, healthcare and labor.

Up until recently, artificial Intelligence (AI) was regularly associated with sci-fi films and dystopian futures -- but a shift has occurred and it already plays a much more commonplace and integral role in our everyday lives. From betting on the Kentucky Derby, to helping us interact with our banks, emails, and families through AI assistants such as Alexa and Siri, AI is becoming ever more important. It is also becoming more integral to businesses. The 2016 Accenture Technology Vision report showed that 70 percent of corporate executives are making more investments in AI-related technologies than they were two years ago, with 55 percent stating that they plan on using machine learning, deep learning, as well as embedded AI solutions like Amelia. Businesses are using this technology to fundamentally change the way they operate and to drive a new, more productive relationship between people and machines.

People across the world are turning amateur photos into elaborate works of art with a new viral app that relies on AI technology to let users instantly transform mundane images into Picasso paintings. Prisma, an app that has attracted 1 million daily users as of Thursday, is reinventing the concept of filtering photos with technology. While the concept of adding filters to photos has been around for years, the Prisma iOS app is unique in the way that it relies on a "combination of neural networks and artificial intelligence" to remake the image. What that means is the Prisma tools aren't the kind of art filters that Instagram uses where the filters overlay the original photo. Instead, Prisma goes through different layers and recreates the photo from scratch, according to the app makers, who are based in Moscow.

The challenge asks computers to make sense out of specific sentences with grammar that humans can understand, but that may be obtuse to machines. For instance, in the sentence "The city councilmen refused the demonstrators a permit because they feared violence," computers aren't able to parse who the word "they" is actually talking about. In contrast, human readers can understand it because of context clues. That's exactly the type of thinking researchers are looking to improve, namely with deep learning. The contest featured a grand prize of 25,000 for entrants who could achive 90 percent accuracy with similar sentences, and the best came from Quan Liu, a researcher from the University of Science and Technology of China as well as Nicos Issak, a researcher from the Open University of Cypress.

The O'Reilly AI Conference, happening September 26-27 in New York, will cover all the most essential--and intriguing--topics in applied AI. You'll learn how to implement AI in your projects, uncover AI's limitations and untapped opportunities, and explore how AI will change the business landscape.

The Vermont countryside provides a beautiful backdrop to the headquarters of the Terasem Movement Foundation, where Bruce Duncan is busy working on changing the future as we know it. Seeker Stories correspondent, Laura Ling, visited Terasem and Duncan to get a sense of what could be around the corner in artificial intelligence. There, Ling met Bina48, a social robot considered to be one of the most advanced of her kind in the world. She runs on a type of software called a character engine, which allows her to interact with humans at an unprecedented level. It may even allow her to evolve.

A team of researchers has created an anonymity network methodology that they believe is more efficient and more secure than existing anonymous networks such as Tor. The research team consists of computer science researchers from the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT) and the École Polytechnique Fédérale de Lausanne in Switzerland. The anonymity architecture uses a routing protocol known as a net network, or mixnet, to make it difficult to trace communications. "Each server permutes the order in which it receives messages before passing them on to the next," according to a statement describing the secure network method. The researchers will present details of the network, named Riffle, at the Privacy Enhancing Technologies Symposium in Germany next week.

We hear plenty of stories about AI being used in medicine, whether it's discovering new drugs or helping diagnose diseases based on symptoms which may be imperceptible to even expert physicians. One area we've not previously heard about machines working in, however, is in the role of "resource nurse" in a hospital. A nurse in this position in the labor and delivery ward is responsible for making decisions about the rooms patients should be assigned to, or which physician should perform a C-section. That's work that researchers in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) attempted to replicate recently -- with a Nao robot trained to learn how these scheduling choices are made and make similar decisions on its own. "What we were able to show is that a system with only a few dozen training examples from people performing a task very well was able to make decisions which appear to be reasonable," Professor Julie Shah, of of the authors of the study, told Digital Trends.

So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, provided tips on how to be successful in large scale machine learning.