Some of the brightest minds in machine learning and deep learning are gathered this week in Barcelona for the annual Neural Information Processing Systems (NIPS) conference. This is the 30th year for the NIPS conference, and the main event was sold out well in advance of the opening. That says a lot about the importance of this event for machine learning researchers and data scientists – and the increasing industry focus on this field. For the first time, Intel is a sponsor of this machine learning and deep learning conference. We are showcasing Intel technologies and initiatives that advance artificial intelligence (AI).

The deep learning process could be about to change dramatically thanks to work being carried out Cray, Microsoft and the Swiss National Supercomputing Centre. In existing architectures and conventional systems, deep learning requires a slow training process that can take months, something that can lead to significantly higher costs and delays in making scientific discoveries. Cray believes that its work with Microsoft and CSSC could have solved this problem by applying supercomputing architectures to accelerate the training process. The three worked together to scale the Microsoft Cognitive Toolkit on a Cray XC50 supercomputer at CSCS nicknamed "Piz Daint". According to the supercomputer manufacturer, deep learning problems share algorithmic similarities with applications that are traditionally run on a massively parallel supercomputer.

Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there. Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has "learned", it can classify, with a certain probability of accuracy, passing vehicles by their make and model.

Autonomous machines shining lasers at ants -- and spraying water at bewildered cats -- for the amusement of cackling grandchildren. Hobbyists are just getting started with deep-learning technologies that give them cheap, off-the-shelf capabilities that put Ronald Reagan's Star Wars program to shame. In the latest edition of the AI Podcast, NVIDIA engineer Bob Bond and Make: Magazine Executive Editor Mike Senese explain to host Michael Copeland how they've taken the once esoteric technology of deep learning and put it to work on offbeat projects that can be tackled on budgets of a few hundred bucks. "One of the big things that's happening -- and it's happening in real time right now -- is the technology is finally hitting a point where we, as consumers, have access to this type of capability," Senese says.

There is a longstanding debate among artificial intelligence experts and futurists: When, not if, AI emerges on the scene, will it help humanity or destroy it? The scenario has played out through innumerable iterations in popular culture, the most popular being The Terminator series. Steven Spielberg, riffing on the film Stanley Kubrick was going to direct before his death, presented the counterpoint, espousing a benevolent vision of AI in A.I. Then there are more nuanced, ambiguous iterations, like the recent Ex Machina. New advances in algorithmic artificial intelligence, deep learning software, automation, and nanotechnology have made it abundantly clear that Ray Kurzweil's vision of the Singularity may also be not an if, but when. In fact, responding to Kurzweil's prediction of a cloud-based neocortex in the 2030s, entrepreneur Bryan Johnson of Braintree said, "Oh, I think it will happen before that."

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While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Lumiata has developed predictive analytics tools that can discover accurate insights and make predictions related to symptoms, diagnoses, procedures, and medications for individual patients or patient groups. The Care Trio team has developed a three-pronged approach that helps doctors devise and understand the best care protocols for cancer patients.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

Artificial Intelligence To Identify Your Brain Age using MRI Scans Had you ever thought of calculating your Brain Age..? If no, then here is the chance to determine your Brain Age within seconds with the use of a mere MRI scan. At King's College of London, Giovanni Montana and a few of his pals have trained a machine which works on Artificial Intelligence and can easily calculate your brain age by just using some raw data from a MRI scan. It is called as Deep-Learning Artificial Intelligence machine. This clearly indicates the growth in Machine Learning and Data Mining Applications in today's era. Human intellectual abilities decline with increase in the age and the same is correlated with the anatomical changes in the brain.

I've written before about the apparent shift in healthcare whereby making sense of the vast quantities of data produced within the system is key to successful treatment of patients. Nowhere is this moreso than in cancer care. For instance, a team from UCL utilized deep learning earlier this year to more accurately identify cancer cells. This trend is continued with a second study, which aims to make sense of the cancer data currently sitting in the cancer registry program that's coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention. This database has records of cancer incidences across the US, but the curation of it can often be a hugely time-intensive process as it requires manual editing and annotation from experts for each file.