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.

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. The pioneers of artificial intelligence were surely off on the timing, but they weren't wrong; AI is coming.

Or to be more specific, they hope to alter your mind with music…music that is composed by an AI (but more on that later). Hewett describes the work as "Auditory brain stimulation," which is a mechanism that relies on something known as "brain entrainment" (also known as "neural entrainment"). This is a novel--and somewhat unconventional--theory that is centered on how brain waves alter in response to acoustic stimulation (sound). The basic idea is that certain sounds evoke very specific responses in the brain. In other words, listening to music can alter, or induce, certain neural oscillations--oscillations that can achieve a host of things, such as altered mood, decreased anxiety, or improved focus. Notably, those who advocate neural entrainment assert that these acoustic-induced alterations can be seen and analyzed via electroencephalogram (EEG) measurements, which is, of course, where the science comes in.

I'm trying to aggregate links and questions that would be useful for people job hunting in ML/data science.

The father of modern speech recognition, Frederick Jelinek, once famously said: 'Anytime a linguist leaves the group, the recognition rate goes up.' Based on this logic, if the domain experts – the phonologists, in his case – were to be exchanged with pure engineers, the performance of the system would improve. Would this theory apply to a system that heavily utilises machine learning? Do the domain experts increase the performance, or is it the lack of them that is best for the system? When working in a highly specialised domain, such as the legal arena, which has clear, well-defined tasks, technology is provided to support, augment and increase productivity. It is often the case that both supervised machine learning techniques (i.e.

Imagine a feedforward network with X input units (i.e. an X-dimensional vector), a hidden layer of Y units, and an output of Z units. Each of the hidden units takes X inputs and produces a single output. Now, imagine that instead of taking just the X inputs from the input layer, each hidden unit also took Y inputs that it produced in the last time step. This is what an RNN layer with Y inputs would be.

Both Amazon and Google are advancing their efforts in machine learning, a type of artificial intelligence that lets computers learn without being explicitly programmed. It's often associated with cloud computing, because it requires the considerable computing power the cloud makes easily available. Amazon's efforts will get a boost when Alexander Smola, a professor in the machine-learning department at Carnegie Mellon University, leaves that position July 1 to head Amazon's cloud machine-learning platform. Smola revealed his plans in this blog post. "This is a terrific task, and it was an offer that I could not turn down," Smola wrote.

Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study?

Andy Rubin, the Google veteran who built Android into the world's largest mobile operating system, is convinced that artificial intelligence is the next big thing. Rubin is the founder of Playground Global, a hardware and software incubator and venture capital firm overseeing at least 300 million. A veteran of past technology transitions, Rubin is now thinking about what could be the next big change to ripple through the industry. "New computing platforms happen every 10 to 12 years," he said at the Bloomberg Technology Conference, citing MS-DOS, Windows PCs and mobile as examples. "What's the next platform?... It's about data and people training AI systems to learn."

Boston: In a latest discovery by the scientists, including one of Indian-origin, have developed artificial software that can turn smartphone in to an eye tracking device. Eye-tracking technology - which can determine where in a visual scene people are directing their gaze - has been widely used in psychological experiments and marketing research, but the required pricey hardware has kept it from finding consumer applications. In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. "Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student. "Since there are no applications, there's no incentive for people to buy the devices. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.