A Research Agenda for Hybrid Intelligence:Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence

We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges

Extreme distributed systems: from large scale to complexity

Modern distributed systems can easily consist of hundreds of thousands of computers, ranging from high-end powerful machines to low-end resource-constrained wireless devices. We label them as "extreme distributed systems," as they push scalability and complexity well beyond traditional scenarios. The extremeness of these systems is now requiring that we reconsider our methods and techniques for their development, and, indeed, we are already witnessing a shift in thinking. For example, Barroso and Holze [1] have made a case for a holistic design of a data center, which they essentially see as a single computer system. In their approach, the process of designing a data center is very similar to the way we have been designing processors: we need to take into account compute elements, data and control paths, storage, power sources, heating issues, and so on. As another example, groups from Lancaster University and INRIA/IRISA in Rennes are working on the integration of component-based software development with gossip-based protocols to combine structural and emergent approaches toward large-scale distributed system development [2]. It seems to be inevitable that we should concentrate more on fully decentralized solutions, as witnessed by, for example, peer-to-peer systems. Decentralized organizations often combine local decision-making with dissemination of information in order to improve the decision-making process, exemplified by many epidemic-based and other bio-inspired approaches. In this light, we are seeing much more than just ensuring that the constituents of a distributed system are properly placed, organized, and connected: the design of a distributed system is becoming fully integrated wit

Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms

Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.</p

A feature representation learning method for temporal datasets

Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly

A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence

We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges