New technologies are expected to disrupt healthcare in the near future. Some of these are categorized as "cognitive computing" and take the shape of dynamic and probabilistic machines that are capable of learning from huge chunks of data for prescribing the best course of action. Hoping for an insight into how cognitive computing could impact healthcare, Affairs Today secured an interview with Dr. Sean McClure, Director and Data Scientist at Space-Time Insight. Affairs Today: What is your experience with cognitive computing systems in healthcare companies? Sean McLure: Cognitive computing, or software that uses machine learning to automate decisions normally made by humans, is playing an ever-larger role in healthcare.

As applications become smarter and more adaptive, user experience is about to undergo a massive change, one that will make the way we interact with our computers and devices a lot more natural. For IBM, whose Watson cognitive computing platform powers a variety of applications, the goal is to turn human-computer interaction into a conversation that's similar to one between two people. "I would say a sentence and Watson would understand not just what I'm saying but what's the intention of what I'm saying," says Melanie Butcher, the program director of the Commerce UX Design Studio at IBM. She's helping to develop products that could allow a marketer to plan and execute an entire campaign through a conversation with a Watson-powered software assistant. But this isn't just a vision of a Star Trek computer that understands questions and voice commands based on context; it's one that also allows for applications that feed information gathered from the user's behaviour back into the app to help guide the user through its functions. "If somebody is in a screen and they're kind of clicking around and they click on help and they search for something but they close it right away, it's clear that they're a little frustrated," she says. The application could then use that information to offer help based on what the user was searching for and what they were trying to do before they got frustrated.

Recently, new ideas about'artificial intelligence' and'cognitive computing systems' in education have been advanced by major computing and educational businesses. More particularly, what understandings of the human teacher and the learner are assumed in the development of such systems, and with what potential effects? The focus here is on the education business Pearson, which published a report entitled Intelligence Unleashed: An argument for AI in education in February 2016, and the computing company IBM, which launched Personalized Education: from curriculum to career with cognitive systems in May 2016. Pearson's interest in AI reflects its growing profile as an organization using advanced forms of data analytics to measure educational institutions and practices while IBM's report on cognitive systems makes a case for extending its existing R&D around cognitive computing into the education sector. AI has been the subject of serious concern recently, with warnings from high-profile figures including Stephen Hawking, Bill Gates and Elon Musk, while awareness about cognitive computing has been fuelled by widespread media coverage of Google's AlphaGo system, which beat one of the world's leading Go players back in March. Commenting on these recent events, the philosopher Luciano Floridi has noted that contemporary AI and cognitive computing, however, cannot be characterized in monolithic terms as some kind of'ultraintelligence'; instead it is manifesting itself in far more mundane ways through an'infosphere' of'ordinary artefacts that outperform us in ever more tasks, despite being no cleverer than a toaster': The success of our technologies depends largely on the fact that, while we were speculating about the possibility of ultraintelligence, we increasingly enveloped the world in so many devices, sensors, applications and data that it became an IT-friendly environment, where technologies can replace us without having any understanding, mental states, intentions, interpretations, emotional states, semantic skills, consciousness, self-awareness or flexible intelligence.

Prime time television viewers have undoubtedly noticed the slew of recent commercials featuring IBM's Watson computing platform in conversation with celebrities such as Bob Dylan, Carrie Fisher, Serena Williams, and Stephen King. These ads showcase continuing advances in Watson's speech capabilities and intelligence applied to various disciplines, which were initially exhibited in Watson's championship performance on the Jeopardy! The public and much of the press tend to think of such computing capabilities as "artificial intelligence" (a.k.a. AI), although that term can bring with it connotations of technology run amuck, à la HAL 9000 in the film 2001: A Space Odyssey, The Terminator's Skynet, and many other popular depictions. Outside the realm of fiction, technology business leader Elon Musk has tweeted that AI is "potentially more dangerous than nukes," and physicist Stephen Hawking warned "development of full artificial intelligence could spell the end of the human race."

Google has developed some awesome mobile applications that realize visual and audio recognition. Google just recently announced an open source Natural Language Understanding (NLU) system called SyntaxNet. This NLU system is built upon Google's TensorFlow, an open source neural network framework. Google has been able to achieve an overall 90 percent accuracy rate with their system. This is quite an accomplishment from just ten years ago, where part of speech tagging consisted of simply identifying entity extraction (verbs, nouns, etc.).

So now that we've got all of this basic terminology out of the way and you can see what machine learning and intelligence are really all about, we can start thinking of the possibilities from a practical perspective. It should come as no surprise that ML is already in widespread use. One popular use case is fraud detection in financial transactions, and the industry is only getting started with the possibilities. Crooks can get quite creative when it comes to gaming the system, and this why we need intelligent systems that continually monitor people's buying behavior. The easy detections are the ones where there is an obvious outlier in the data.

I was chatting with an old friend yesterday and he reminded me of a conversation we had nearly 50 years ago. I tried to explain to him what I did for living and he was trying to understand why getting computers to understand was more complicated than key word analysis. I explained about concepts underlying sentences and explained that sentences used words but that people really didn't use words in their minds except to get to the underlying ideas and that computers were having a hard time with that. Fifty years later, key words are still dominating the thoughts of people who try to get computers to deal with language. But, this time, the key word people have deceived the general public by making claims that this is thinking, that AI is here, and that, by the way we should be very afraid, or very excited, I forget which.

With over 500 companies developing cognitive systems, we're seeing patterns emerge around the creation of cognitive systems at the business unit, business process, and application levels. By selecting a subset of these companies to compare, we can see a few of the leading business units and processes that are going cognitive. We also discover how various Watson services are combined to create to address these business needs. These topics and more were covered in the recent Emerging Cognitive Patterns webinar. A few highlights are discussed briefly below but refer to the full webinar slide deck for complete details.

But is the Big Data movement going to make a difference? I'm strongly in favor of cognition, computing, and computing that is smarter rather than dumber. But is the Cognitive Computing movement likely to make a difference?

Leading industry experts are launching a Cognitive Computing Consortium to focus on furthering innovation in cognitive computing. The consortium is an interactive forum for researchers, developers, and practitioners of cognitive computing and its allied technologies. The consortium was co-founded by Sue Feldman, CEO, Synthexis; and Hadley Reynolds, principal analyst at NextEra Research, to fill a gap in the industry. Its mission is to enable professionals to exchange ideas and insights to conduct research and to educate buyers, users and the public on cognitive computing technologies, their uses, and potential impacts. The group was inspired to form after vendors told various experts that they needed an unbiased source to which they can refer potential clients for validation, advice, and background information.