Human intelligence has generally been studied by focusing on two primary levels: cognitive science, which examines the mind, and neuroscience, which focuses on the brain. Both approaches have influenced artificial intelligence (AI) research, leading to the development of various cognitive architectures with emergent behaviors.23 In this article, we propose an approach inspired by human cognition, specifically drawing on cognitive theories about human reasoning and decision making. We are inspired by the book Thinking, Fast and Slow by Daniel Kahneman,20 which categorizes human thought processes into two systems: System 1 (fast thinking) and System 2 (slow thinking).37 System 1, or "thinking fast," is responsible for intuitive, quick, and often unconscious decisions.

We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (X\% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.

The Winograd Schema Challenge (WSC), a seemingly well-thought-out test for machine intelligence, has been proposed to shed light on developing systems that exhibit human behavior. Since its introduction, it aimed to pivot the focus of the AI community from the technology to the science of AI. While common and trivial for humans, studies show that it is still challenging for machines, especially when they have to deal with novel schemas, that is, well-designed sentences that require the resolving of definite pronouns. As researchers have become increasingly interested in the challenge itself, this presumably necessitates the availability of an extensive collection of Winograd schemas, which goes beyond what human experts can reasonably develop themselves, especially after proposed ways of utilizing them as novel forms of CAPTCHAs. To address this necessity, we propose a novel framework that explicitly focuses on how humans and machines can collaborate as teammates to design novel schemas from scratch. This is being accomplished by combining two recent studies from the literature: i) Winventor, a machine-driven approach for the development of large amounts of Winograd schemas, albeit not of high quality, and ii) WinoFlexi, an online crowdsourcing system that allows crowd workers to develop a limited number of schemas often of similar quality to that of experts. Our proposal crafts a new road map toward developing a novel collaborative platform that amplifies human and machine intelligence by combining their complementary strengths.

The 35th conference on Neural Information Processing Systems (NeurIPS2021) featured eight invited talks. Continuing our series of round-ups, we give a flavour of the next three presentations. In his talk, Peter focussed on the phenomenon of benign overfitting, one of the surprises to arise from deep learning: that deep neural networks seem to predict well, even with a perfect fit to noisy training data. The presentation began with a broader perspective on theoretical progress inspired by large-scale machine learning problems. Peter took us back to 1988, and to a NeurIPS paper by Eric Baum and David Haussler who were interested in the question of generalization for neural networks.

This multi-part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, artificial general intelligence, and the difference between video game AI and real AI. As legend has it, a reporter once asked Mahatma Ghandi what he thought of Western Civilization. His response was "I think it would be a good idea." The same sentiment could be applied to artificial intelligence if you compare it directly to human intelligence.

As artificial intelligence (AI) systems are getting ubiquitous within our society, issues related to its fairness, accountability, and transparency are increasing rapidly. As a result, researchers are integrating humans with AI systems to build robust and reliable hybrid intelligence systems. However, a proper conceptualization of these systems does not underpin this rapid growth. This article provides a precise definition of hybrid intelligence systems as well as explains its relation with other similar concepts through our proposed framework and examples from contemporary literature. The framework breakdowns the relationship between a human and a machine in terms of the degree of coupling and the directive authority of each party. Finally, we argue that all AI systems are hybrid intelligence systems, so human factors need to be examined at every stage of such systems' lifecycle.

No conference on artificial intelligence (AI), machine learning or robotics would be complete without its fair share of technologists, programmers and engineers. But scan the list of attendees at the 2020 Rise of AI Summit, a hybrid (digital and physical) event this week in Berlin (November 17-18, 2020) and the number of people from health insurance companies, banks and venture capitalists is astonishing. As one of the founders of the event, CEO of Asgard Capital, Fabian Westerheide, said in his opening remarks on "The Next Decade of AI, we are in a'renaissance' of the technology." Westerheide says we're seeing a "refurbishment of ideas from the 1960s, 70s and 80s," combined with the amount of data we have now and today's processing power. He calls it "old ideas, new execution, and new capital."

Artificial intelligence (AI) is one of the signature issues of our time, but also one of the most easily misinterpreted. The prominent computer scientist Andrew Ng's slogan "AI is the new electricity"2 signals that AI is likely to be an economic blockbuster--a general-purpose technology3 with the potential to reshape business and societal landscapes alike. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.4 Such provocative statements naturally prompt the question: How will AI technologies change the role of humans in the workplaces of the future? An implicit assumption shaping many discussions of this topic might be called the "substitution" view: namely, that AI and other technologies will perform a continually expanding set of tasks better and more cheaply than humans, while humans will remain employed to perform those tasks at which machines ...

Using machines to augment human activity is nothing new. Egyptian hieroglyphs show the use of horse-drawn carriages even before 300 B.C. Ancient Indian literature such as "Silapadikaram" has described animals being used for farming. And one glance outside shows that today people use motorized vehicles to get around. Where in the past human beings have augmented ourselves in physical ways, now the nature of augmentation also is more intelligent. Again, all one needs to do is look to cars – engineers are seemingly on the cusp of self-driving cars guided by artificial intelligence.

Hybrid Intelligence (HI) is the combination of human and machine intelligence, expanding human intellect instead of replacing it. HI takes human expertise and intentionality into account when making meaningful decisions and perform appropriate actions, together with ethical, legal and societal values. Our goal is to design Hybrid Intelligent systems, an approach to Artificial Intelligence that puts humans at the centre, changing the course of the ongoing AI revolution. By providing intelligent artificial collaborators that interact with people we strengthen our human capacity for learning, reasoning, decision making and problem solving. This interaction has the potential to amplify both human and machine intelligence by combining their complementary strengths.