The rise of'deep learning' has caused a lot of excitement around the revolutionary capabilities of these artificially intelligent agents. But it's also raised fear and suspicion about what exactly is going on inside each algorithm. One way for us to gain some understanding of our silicon-based friends (or foes?) is for them to disclose their framework of decision-making in a way that we humans can understand – by using the concept of personality. My research explores how some of these deep learning agents can be better understood through their'personalities' – like whether they are'greedy', 'selfish' or'prudent'. We are now at the dawn of a new era in AI technology – a so-called fourth industrial revolution that will reshape every industry.

What to know about SingularityNET (AGI)? Blockchain technology has become one of the most in-demand technologies worldwide. Among the innovations that prove its advancement is the launching of SingularityNet. Many will confuse it with a typical marketplace, but SingularityNet is a decentralized marketplace for Artificial Intelligence (AI). The businesses associated with AI are increasing daily; however, there's a significant difference between the people developing AI tools (researchers and academics) and the businesses that want to make use of the technology for specific needs.

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.

For seven years, AI researchers have been struggling with an unusual challenge: shooting cartoon birds at cartoon pigs. An annual competition tests their ability to craft an AI agent that can play the popular video game Angry Birds. This month two researchers posted a paper on arXiv.org It's an example of the kind of weird obstacles that all AI researchers face as they attempt to adapt cutting-edge technologies to some very human endeavors. Teams around the world are tackling much more sophisticated problems, persevering to overcome the obstacles on the path to our shiny technology-enhanced future.

We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions. For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. We prove that the solutions to these surrogate problems converge to a stationary point of the original nonconvex problem. Furthermore, to extend our theoretical results, we apply our algorithm to examples of optimal control and multi-agent reinforcement learning with safety constraints.

We have nearly 30 years' experience in research and teaching. Our efforts focus on four complimentary fields: Artificial Intelligence, Data Science, Computer Science and Applied Mathematics. The department's contributions to areas such as multi-agent systems, (medical) signal and image processing, game theory and AI search techniques are internationally recognized. DKE maintains a large and worldwide network of public and private collaborators, and our staff is firmly rooted in multiple national and international research networks. Next to our research, we take pride in our education.

A bank's AI goals should look beyond cost reduction, welcome though that is. This technology has the capacity to do much more. Indeed, it can completely transform on institution from the core." In our recent Banking on AI article, we gave an overview of the rapid pace of AI evolution in the banking sector and looked at the limitless opportunity to automate functions and augment the banking workplace. Microsft's Dynamics 365 AI solutions feature the most impactful and proprietary AI capabilities in global technology, and support banking organisations to deliver meaningful customer experience.

The research on coalitional games has focused on how to share the reward among a coalition such that players are in-centivised to collaborate together. It assumes that the (deterministic or stochastic) characteristic function is known in advance. This paper studies a new setting (a task allocation problem) where the characteristic function is not known and it is controlled by some private information from the players. Hence, the challenge here is twofold: (i) incentivize players to reveal their private information truthfully, (ii) incentivize them to collaborate together. We show that existing reward distribution mechanisms or auctions cannot solve the challenge. Hence, we propose the very first mechanism for the problem from the perspective of both mechanism design and coalitional games.

Our research aims to develop intelligent collaborative agents that are human-aware - they can model, learn, and reason about their human partner's physiological, cognitive, and affective states. In this paper, we study how adaptive coaching interactions can be designed to help people develop sustainable healthy behaviors. We leverage the common model of cognition - CMC [26] - as a framework for unifying several behavior change theories that are known to be useful in human-human coaching. We motivate a set of interactive system desiderata based on the CMC-based view of behavior change. Then, we propose PARCoach - an interactive system that addresses the desiderata. PARCoach helps a trainee pick a relevant health goal, set an implementation intention, and track their behavior. During this process, the trainee identifies a specific goal-directed behavior as well as the situational context in which they will perform it. PARCcoach uses this information to send notifications to the trainee, reminding them of their chosen behavior and the context. We report the results from a 4-week deployment with 60 participants. Our results support the CMC-based view of behavior change and demonstrate that the desiderata for proposed interactive system design is useful in producing behavior change.

In Towards a theory of superminds, I describe a theory of collective intelligence based on the active inference framework pioneered by Karl Friston. As required by active inference, that theory implies that all collectives (such as teams and organizations) operate on the basis of an implicit collective model of the world -- which provides them with the ability to make sense of observations and to predict outcomes of alternative courses of action ("policies" in Friston-speak). If we buy into the theorem that any Markov blanket (causally atomic subsystem) can always be described as performing some form of approximate active inference, this isn't polemical at all. Yet, as I've come to understand, there is something deeply counterintuitive in the conclusion. The fact that needs to be explained is that, unlike people, animals, unicellular beings, and even artificial agents, collectives are composed of clearly distinct and autonomous constituents -- and yet are capable of acting as collectives. This collective action can be as simple as the coherent motion of a flock of birds, or as complex as the financial markets incorporating sophisticated information about expectations of the future into asset prices (or for that matter, a corporation acting out a complex business strategy and production structure); the salient point is that the constituents are autonomous (and, at least for the examples involving humans, will tell you they follow their own free will), and yet the collective can be very accurately described as an agent in its own right. It turns out that fully resolving this apparent contradiction requires gaining a thorough understanding of what these models are, which I will attempt to do in this post.