From Tony Stark's Jarvis to Apple's Siri, artificial intelligence (AI) is ubiquitous in fiction and real life. Albeit with different levels of skill, AI is supposedly built to maximize chances of reaching a goal, therefore supporting humans. But what would happen if robots, AI systems, and humanoids went rogue? For computer scientist Roman Yampolskiy, the possibilities are endless. Partially funded by SpaceX CEO Elon Musk, Yampolskiy and Pistono's study is conducted for the same reason that DARPA asked techies to turn household items into weapons.

Deep learning, frequently referred to as artificial intelligence, a branch of machine learning utilizing multiple layers of neurons to model high-level abstractions in data, has outperformed humans in tasks including image, text and voice recognition, autonomous driving and others, and is now being applied to drug discovery and biomarker development. In a study published in Molecular Pharmaceutics, a prestigious journal published by the American Chemical Society, scientists from Insilico Medicine in collaboration with Datalytic Solutions and Mind Research Network trained deep neural networks to predict the therapeutic use of large number of multiple drugs using gene expression data obtained from high-throughput experiments on human cell lines. Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased. When the networks got confused and guessed the therapeutic use of the drugs incorrectly, the drugs often had dual use, indicating the possibility of using DNNs for drug repurposing. This is the first known application of deep learning to drug discovery using transcriptional response data.

The innovations protected by the new patent tie together graphs and graphical annotations with the corresponding narrative in natural language reports that the Arria NLG software generates. For example, placing one's cursor on a graphical element can cause the corresponding report section to be highlighted for ease of reference. The patented features will be deployed to strengthen the integration of language and graphical elements in the Company's products. As with the Company's other recent patents, the covered features are desirable in any NLG system, not only Arria NLG's advanced Reiter & Dale model language generation system. The new patent is part of a broad diversification plan that Arria NLG has instituted to enhance versatility and improve ease of use, as well as to increase the analytic power and actionable conclusions of its NLG products.

Leading global IT services company HCL Technologies has been named winner of the'Best Innovation in Natural Language Processing (NLP)' award at the AIconics Awards during the "AI Summit" in London recently. AIconics are the world's only independently judged awards celebrating the drive, innovation and hard work in the international Artificial Intelligence (AI) community. "This award gives testimony to the innovation and pragmatic implementation of'Natural Language Processing' in the 21st century enterprise," Kalyan Kumar, executive vice president at HCL Technologies, said in a statement. HCL was also named as a finalist in the "Best Intelligent Assistant" category which showcases companies making ground-breaking advancements in virtual assistants and advanced voice/text recognition capabilities. The driving force behind HCL's success at the AIconics Awards is "HCL DryICE" -- the company's innovative autonomics and orchestration framework.

This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

The Daily Roundup is our comprehensive coverage of the VR industry wrapped up into one daily email, delivered directly to your inbox. Artificial Intelligence has the potential to disrupt so many different dimensions of our society that the White House Office of Science & Technology Policy recently announced a series of four public workshops to look at some of the possible impacts of AI. The first of these workshops happened at the University of Washington on Tuesday, May 24th, and I was there to cover how some of these discussions may impact the virtual reality community. The first AI public workshop was focused on law and policy, and I had a chance to talk to three different people about their perspectives on AI. I interviewed the White House Deputy U.S. Chief Technology Officer Edward Felten about how these workshops came about, and the government's plan for addressing the issue.

Noida, India and London, United Kingdom, May 27th 2016 – HCL Technologies, a leading global IT services company, has been named the winner of the Best Innovation in Natural Language Processing (NLP) award at the AIconics Awards. The AIconics are the world's only independently judged awards celebrating the drive, innovation and hard work in the international Artificial Intelligence Community organised by AIBusiness and was hosted during The AI Summit on the 5th May. HCL was also named as a finalist in the Best Intelligent Assistant category, which showcases companies making ground-breaking advancements in virtual assistants and advanced voice/text recognition capabilities. This year's awards featured 5 categories, recognising industry leaders across a broad spectrum of AI technologies. In winning the Best Innovation in NLP award, the AIconics Awards judges recognised that HCL is ahead of the curve when it comes to NLP, at a time when many businesses are looking towards productivity and automation of roles and tasks.

Human-level intelligence is familiar in biological "hardware" – it happens inside our skulls. Technology and science are now converging on a possible future where similar intelligence can be created in computers. While it is hard to predict when this will happen, some researchers suggest that human-level AI will be created within this century. Freed of biological constraints, such machines might become much more intelligent than humans. What would this mean for us?

What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun--but not "he" or "she." The algorithm went on to pick the funniest captions for thousands of the New Yorker's cartoons, and in most cases, it matched the intuition of its editors. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz. Google released the word2vec tool, and Facebook followed by publishing their speed optimized deep learning modules.

On May 10, the world-class admin behind John Chambers's success was honored with one of the top awards in her field: Debbie Gross received the Colleen Barrett Award for Administrative Excellence. Clearly, Debbie is a force. This CNBC story gives us a peek into her life keeping John at the top of his game. People are rightly saying she's a role model for next-generation administrators. But what people aren't saying is that some next-gen admins are made not of flesh and blood like Debbie but of compute cycles.