Sethi describes a cognitive analytics application in a health care setting: Imagine you walk into the emergency room with red eyes and a fever. Cognitive systems in a triage room can analyze your vitals, correlate them with your medical and travel histories, and predict with accuracy whether you have the common flu, the Zika virus or some other illness. As this health care example illustrates, cognitive technologies are able to understand the world around us, read signs and understand what's happening – but in a highly focused context to complete a narrow but important task. "The goal of many cognitive systems is to provide assistance to humans without human assistance," says Schabenberger. "But it is important to think about who is being assisted by automated systems." In the health care example above, the doctor and nurse are being assisted as much as the patient.

What sets the present generation of cognitive computing solutions apart from the past is the advent of hugely more powerful and cost-effective computer systems that can process information at high speed, says David Powers, professor of computer science and director for the Centre of Knowledge and Interaction Technology at Flinders University. Combined with big data collections forged from enterprise databases, social networks, sensors – even CCTV – plus the advances in machine learning and deep neural networks, this means it's now possible to use cognitive platforms to generate far more useful insight. Powers, who has been working in the field of artificial intelligence and cognitive science for close to 40 years, says that the support rendered by cognitive computing platforms already ranges from the automation of contact centres to handle routine queries, to training young surgeons using haptic devices, which give tactile feedback, before the doctor is let loose with a scalpel on a human body. He describes the present generation of cognitive platforms as offering "triage" style support, dealing with the routine automatically and supporting humans through more complex challenges. He has been involved with a series of university spin-off businesses such as Clevertar.com.

Last month, the Genie Cognitive Computing Factory ("Genie") was released for public use. Genie (an acronym for General Evolving Networked Intelligence Engine) easily allows both experts and non-experts alike to create artificial intelligence agents (called "genies") to analyze Big Data, recognize objects, events, and patterns (a.k.a. "classify data"), make predictions & decisions, take appropriate adaptive actions, and evolve within the genie's environment. The intelligent objects created using the platform are called "genies". Genies have a bunch of unique features.

She's a blast to have around. She's especially fun to shop with. That's when her bubbly, happy-go-lucky personality really comes through. Usually, we walk down an aisle – any aisle will do – and all of a sudden you can see her get excited about something she sees – a product, a sale price, whatever. I tease her that she's like a squirrel – always getting distracted by the next "shiny object". Rats do! She'll kill me if I compare her to a rat, though!)

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer beha... read more

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AIC 2016 is the fourth international appointment of the International Workshop Series AIC on "Artificial Intelligence and Cognition". The AIC series started in 2013, stemming from the need of creating an international scientific forum for the discussion and the presentation of the theoretical and applied research developments in the field of cognitively inspired Artificial Intelligence. In recent years, after a period of partial fragmentation of the research directions between the AI and Cognitive Science community, the area of cognitively inspired artificial systems has attracted a renewed attention both from academia and industry and the awareness about the need for additional research in this interdisciplinary field is gaining again widespread acceptance. In this spirit, the AIC 2016 workshop aims at putting together researchers coming from different domains (e.g., artificial intelligence, cognitive science, computer science, engineering, philosophy, social sciences, etc.) working on the interdisciplinary field of cognitively inspired artificial systems. AIC 2016 will be an official workshop of the BICA 2016, 7th Conference on Biologically Inspired Cognitive Architectures and will be one of the international events that will take place in July in New York under the HLAI framework, a joint effort between the major Artificial Intelligence conferences and academic events explicitly targeting work towards the computational creation of human-level Artificial Intelligence.

At this point is there any need for us to get involved with the old-world stages of motivation and vision and discovery and finally an action decision? Perhaps at the first conscious trigger point for us, say a particularly uncomfortable episode during which we feel really mad at the boss and perhaps text something about this to a trusted colleague, our personal AI simply lights up the screen of the smartphone and advises us that we now have a new job working in the data science group at a global social networking firm for twice the annual take-home that we now make plus stock options. Our AI has been negotiating with the firm's AI for at least the last several seconds and they have determined that our professional qualifications, personality type, geographical preferences, curiosity and predilection for innovation make a superior match with the firm's hiring profiles. The plane ticket is already booked, our 401K already transferred.

The move to analogies as teaching methods is a major step in "cognitive computing," which allows computers to learn and even reprogram themselves. Cognitive computing is the big evolutionary step to true artificial intelligence. The new research is being carried out by Northwestern University, using a new approach called the Structure Mapping Engine (SME) which is capable of analogic problem solving, including "moral dilemmas". So far, the results are pretty straightforward -- computers retrieve memories to find analogic situations -- Case A resembles Example C, but not Cases B or D, etc. This type of very basic, very important learning is roughly kindergarten level for humans.

This is the fourth in a continuing series of stories previewing sessions of importance to cloud application developers at the Cloud Expo conference, which takes place June 7 to 9 at the Jacob Javits Center in New York. IT analyst Denis Pombriant explains why platforms are on the rise and how to choose the right one. He also highlights the importance of having an ecosystem and puts a spotlight on issues in cloud development. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.