Cognitive computers will analyze a patient's speech or written words to look for tell-tale indicators found in language, including meaning, syntax and intonation. Combining the results of these measurements with those from wearables devices and imaging systems (MRIs and EEGs) can paint a more complete picture of the individual for health professionals to better identify, understand and treat the underlying disease, be it Parkinson's, Alzheimer's, Huntington's disease, PTSD or even neurodevelopmental conditions such as autism and ADHD. At IBM, scientists are using transcripts and audio inputs from psychiatric interviews, coupled with machine learning techniques, to find patterns in speech to help clinicians accurately predict and monitor psychosis, schizophrenia, mania and depression. Today, it only takes about 300 words to help clinicians predict the probability of psychosis in a user.

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The March issue of Health Affairs demonstrates the potential of health care delivery system innovation to improve value for both patients and clinicians. Technology innovations such as machine learning and artificial intelligence systems are promising breakthroughs to improve diagnostic accuracy, tailor treatments, and even eventually replace work performed by clinicians, especially that of radiologists and pathologists. Machine-learning systems infer patterns, relationships, and rules directly from large volumes of data in ways that can far exceed human cognitive capacities. As the computational underpinning of tools such as e-mail spam filters, product and content recommendations, targeted advertisements, and, more recently, autonomous vehicles, machine learning is already ubiquitous in many economic sectors. Yet, machine-learning applications are still used sparingly today in the delivery of care.

Aaron Reich, senior director of technology innovation at Avanade, told iTWire in an interview that what was powerful about AI and its cognitive computing capability was that it was more than just direct language translation. "Machines today can understand intent and nuances, making translation much more natural than in the past," he said. "However, it's not just about the tool; it's also about how it is embedded in an organisation's culture." Reich (below) is part of the innovation and incubation team at Avanade, a global professional services company providing IT consulting and services focused on the Microsoft platform. For the past few years he has been responsible for growth of Avanade's Windows Azure business.

We hear the term all of the time. It's being bandied about and is an old idea with a new name, but what actually is cognitive computing and why is it such a big deal today? Cognitive computing is --according to the experts--the "simulation of human thought processes in a computerized model." Cognitive computing is used to create systems that are fully automated and that are fully capable of real problem solving without the human intervention that we have come to expect from such systems. In fact, the dawn of computing couldn't' foresee anything such as we're seeing today.

Machine learning--Machine learning is arguably the most commonly found manifestation of cognitive computing, so it's not surprising it's available in so many forms. There is both supervised and unsupervised machine learning (the former of which requires human intervention and the latter of which learns on its own, according to Nanduri), as well as that centered upon automation and that centered upon recommendations. "When we think about how we're going to build machine learning into a workflow, we try to think hard about whether this is a recommendation problem or an automation problem," Eliot Knudsen, data science lead at Tamr (tamr.com), "It's a little subtle but tends to be important in framing the work we do." Deep learning--Deep learning and neural network techniques bear similarity to machine learning ones yet involve a degree of inferences and learning by examples--rather than in accordance with training based on predefined rules--that creates a profound difference.

Informatica CIO Graeme Thompson looks at the rise of the machines and the role of the CIO in unlocking machine learning success. We live in an age characterised by the need to move fast, to learn from our mistakes, adapt and respond to change and predict future outcomes, so that we can gain competitive edge. Yet the pace at which business moves today and the availability of information means organisations are expected to make decisions and deliver change at a rate unheard of just a few years ago. We as humans don't have the processing power to accept all available inputs, apply the lessons we learn about what does and doesn't work quickly enough to drive incremental improvements, let alone big, disruptive innovations. Most current prediction models have their roots in statistics innovations dating back to the 19th and 20th centuries.

According to IBM CEO Ginni Rometty, it isn't going to be long ( 5 years-- according to the statement at ThinkForum) before every decision that is made by business is partly made by a cognitive system. These systems are touted as being systems that can learn, can understand and can help to define best practice in business. Last summer, when Rometty was speaking at Thinkforum in Sydney Australia, it didn't sound as though she considered it fiction on any level and in fact, much of what she's discussing already exists and is working to save us time and money. "Every industry has its Uber or Tesla, and many people say they are going to be a technology company of some kind. An important question is: When everyone is digital, who wins? "Digital for all has to be the foundation, but it's not the destination.

Artificial Intelligence (AI) represents machine-based intelligence, typically manifest in "cognitive" functions that humans associate with other human minds. There are a range of different technologies involved in AI including Machine Learning, Natural Language Processing, Deep Learning, and more. Cognitive Computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. Key industry verticals covered include use of AI in Internet related services and products, Financial Services, Medical and Bio-informatics, Manufacturing, and Telecommunications. Some of the key application areas covered include Marketing and Business Decision Making, Workplace Automation, Predictive Analysis and Forecast, Fraud Detection and Classification.

We are entering a new era in the history of humanity -- the era of cognitive computing. Gartner predicts, "The smart machine era will be the most disruptive in the history of IT." In this new era, breakthroughs in technologies, such as natural language processing, semantic analysis, automatic reasoning and machine learning will produce computers that learn, reason and interact with humans naturally. These systems will work alongside humans and possess human qualities, such as common sense and the ability to pick up on emotional cues. In terms of intelligent systems that understand unstructured data, reason and learn, the future is already here. Cognitive computing technologies can already assist decision making and help humans solve complex problems.