Reducing data in the data center has been a mentality in the past, but the Internet of Things (IoT) demands more, more, and more still. Withholding information from analytics systems is in essence selling IoT systems short; actively seeking it, on the other hand, invites challenges perhaps never-before-seen by even the most seasoned data scientists. In this interview with Chris O'Connor, General Manager of Watson Internet of Things Offerings at IBM, he discusses how the power of cognitive computing is being harnessed through the company's Watson platform – now exposed to developers through a set of application programming interfaces (APIs) – to turn the IoT data deluge into increasingly valuable insights. For those unfamiliar, can you briefly describe Watson, and then fill us in on what it's been up to since its Jeopardy! O'CONNOR: Watson is a true learning platform.

Today, we create a staggering amount of information: formulas, tweets, poetry, photos, videos and many more. Even our discoveries and diagnosis generate data. Can we draw meaning from it? That let's do things we've never done before. Overcome obstacles that used to stop us.

Radical advances in artificial intelligence, along with greater processing power, are pushing cognitive computing and deep learning into the mainstream. Since the dawn of computing, the goal of engineers, designers and developers has been to imbue machines with greater intelligence so they can think more like humans. Today, marked leaps in processing power and incredible advances in artificial intelligence (AI) are pushing the concept from the pages of science fiction novels to our homes and workplaces. "The growing complexity of computing and information--and the need for more intelligent automation--is leading to the next wave of transformation, including cognitive systems," says Paul Brody, technology sector strategy leader for the Americas at consulting firm EY. Systems such as IBM's Watson, as well as interfaces such as Apple's Siri, Microsoft's Cortana and Google Voice, are transforming the way data and information are routed to people.

Instead of building smarter computers to bring miraculous solutions, most of these problems are better solved by smarter application of computing. For example, trying to predict the optimal time to trigger a specific stock purchase could be accomplished with a relatively simple model, but imagine a computer trying to determine which stock to purchase. The problem involves a dramatic increase in complexity because the increased number of variables to consider is so much higher. That kind of complexity requires new approaches, and the dynamic nature of the decisions that have to be made doesn't permit months of research to reach an acceptable level of accuracy.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we take steps toward an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which examples of moral judgments and their justifications influence the moral development both of individuals and of the society as a whole. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

Recent advances in cognitive computing componentry combined with other factors are leading to commercially viable cognitive systems. From chips to smart phones to public and private clouds, industrial strength “cognition as a service” is beginning to appear at all scales in business and society. Furthermore, in the age of zettabytes on the way to yottabytes, the designers, engineers, and managers of future smart systems will depend on cognition as a service. Cognition as a service can help unlock the mysteries of big data and ultimately boost the creativity and productivity of professionals and their teams, the productive output of industries and organizations, as well as the GDP (gross domestic product) of regions and nations. In this and the next decade, cognition as a service will allow us to re-image work practices, augmenting and scaling expertise to transform professions, industries, and regions.

A general tension-reduction (GTR) model was recently considered to derive quantum probabilities as (universal) averages over all possible forms of non-uniform fluctuations, and explain their considerable success in describing experimental situations also outside of the domain of physics, for instance in the ambit of quantum models of cognition and decision. Yet, this result also highlighted the possibility of observing violations of the predictions of the Born rule, in those situations where the averaging would not be large enough, or would be altered because of the combination of multiple measurements. In this article we show that this is indeed the case in typical psychological measurements exhibiting question order effects, by showing that their statistics of outcomes are inherently non-Hilbertian, and require the larger framework of the GTR-model to receive an exact mathematical description. We also consider another unsolved problem of quantum cognition: response replicability. It is has been observed that when question order effects and response replicability occur together, the situation cannot be handled anymore by quantum theory. However, we show that it can be easily and naturally described in the GTR-model. Based on these findings, we motivate the adoption in cognitive science of a hidden-measurements interpretation of the quantum formalism, and of its GTR-model generalization, as the natural interpretational framework explaining the data of psychological measurements on conceptual entities.

Although Switzerland is a small country, it is home to many internationally renowned universities and scientific institutions. The research landscape in Switzerland is rich, and AI-related themes are investigated by many teams under diverse umbrellas. This column sheds some light on selected developments and trends on AI in Switzerland as perceived by members of the Special Interest group on Artificial Intelligence and Cognitive Science (SGAICO) organizational team, which has brought together researchers from Switzerland interested in AI and cognitive science for over 30 years.

This paper presents the first functional evaluation of spontaneous, uncued retrieval from long-term memory in a cognitive architecture. The key insight is that current deliberate cued retrieval mechanisms require the agent to have knowledge of when and what to retrieve --- knowledge that may be missing or incorrect. Spontaneous uncued retrieval eliminates these requirements through automatic retrievals that use the agent's problem solving context as a heuristic for relevance, thus supplementing deliberate cued retrieval. Using constraints derived from this insight, we sketch the space of spontaneous retrieval mechanisms and describe an implementation of spontaneous retrieval in Soar together with an agent that takes advantage of that mechanism. Empirical evidence is provided in the Missing Link word-puzzle domain, where agents using spontaneous retrieval out-perform agents without that capability, leading us to conclude that spontaneous retrieval can be a useful mechanism and is worth further exploration.

Inspired by the extraordinary computing power promised by quantum computers, the quantum mind hypothesis postulated that quantum mechanical phenomena are the source of neuronal synchronization, which, in turn, might underlie consciousness. Here, I present an alternative inspired by a classical computing method with quantum power. This method relies on special distributed representations called hyperstrings. Hyperstrings are superpositions of up to an exponential number of strings, which -- by a single-processor classical computer -- can be evaluated in a transparallel fashion, that is, simultaneously as if only one string were concerned. Building on a neurally plausible model of human visual perceptual organization, in which hyperstrings are formal counterparts of transient neural assemblies, I postulate that synchronization in such assemblies is a manifestation of transparallel information processing. This accounts for the high combinatorial capacity and speed of human visual perceptual organization and strengthens ideas that self-organizing cognitive architecture bridges the gap between neurons and consciousness.