Prior research in human-centric AI has primarily addressed single-modality tasks like pedestrian detection, action recognition, and pose estimation. However, the emergence of large multimodal models (LMMs) such as GPT-4V has redirected attention towards integrating language with visual content. Referring expression comprehension (REC) represents a prime example of this multimodal approach.

Generations in Dialogue: Bridging Perspectives in AI is a podcast from AAAI featuring thought-provoking discussions between AI experts, practitioners, and enthusiasts from different age groups and backgrounds. Each episode delves into how generational experiences shape views on AI, exploring the challenges, opportunities, and ethical considerations that come with the advancement of this transformative technology. In the second episode of this new series from AAAI, host Ella Lan chats to Professor Andreea Bobu about choosing her research direction, working with humans in-the-loop, things to consider when working with data, system design challenges, the gap between what we think we're programming and what we've actually programmed, privacy and personalisation, and advice for early-career researchers interested in human-centric AI. Andreea Bobu is an Assistant Professor at MIT and leads the Collaborative Learning and Autonomy Research (CLEAR) Lab, where she develops autonomous agents that learn to act for, with, and around people. Her research focuses on aligning robot behavior with human expectations by studying how agents can acquire the right supervision--whether from direct human input or priors--build shared task representations with users, and address misalignment from differing human models.

Prior research in human-centric AI has primarily addressed single-modality tasks like pedestrian detection, action recognition, and pose estimation. However, the emergence of large multimodal models (LMMs) such as GPT-4V has redirected attention towards integrating language with visual content. Referring expression comprehension (REC) represents a prime example of this multimodal approach. In response, we present HC-RefLoCo (Human-Centric Referring Expression Comprehension with Long Context), a benchmark that includes 13,452 images, 24,129 instances, and 44,738 detailed annotations, encompassing a vocabulary of 18,681 words. Each annotation, meticulously reviewed for accuracy, averages 93.2 words and includes topics such as appearance, human-object interaction, location, action, celebrity, and OCR.

A new technology, broadly deployed, raises profound questions about its impact on American society. Government agencies wonder whether this technology should be used to make automated decisions about Americans. Academic experts call attention to concerns about fairness and accountability. Comments from the public are requested. A White House press conference is announced.

This paper examines the interdisciplinary research question of how to integrate Computational Argumentation, as studied in AI, with Cognition, as can be found in Cognitive Science, Linguistics, and Philosophy. It stems from the work of the 1st Workshop on Computational Argumentation and Cognition (COGNITAR), which was organized as part of the 24th European Conference on Artificial Intelligence (ECAI), and took place virtually on September 8th, 2020. The paper begins with a brief presentation of the scientific motivation for the integration of Computational Argumentation and Cognition, arguing that within the context of Human-Centric AI the use of theory and methods from Computational Argumentation for the study of Cognition can be a promising avenue to pursue. A short summary of each of the workshop presentations is given showing the wide spectrum of problems where the synthesis of the theory and methods of Computational Argumentation with other approaches that study Cognition can be applied. The paper presents the main problems and challenges in the area that would need to be addressed, both at the scientific level but also at the epistemological level, particularly in relation to the synthesis of ideas and approaches from the various disciplines involved.

The European Vision for AI event, held on 22 April 2021, provided an opportunity for the public to hear from members of the European artificial intelligence (AI) community and representatives from the European Commission and parliament. The morning-long session was organised by the VISION project partners in cooperation with four networks of AI centres of excellence (AI4Media, ELISE, TAILOR, Humane-AI-Net). These networks were launched within the European Union's Horizon 2020 Programme in September 2020 and are bringing together scientists across Europe. This event followed hot on the heels of the announcement from the European Commission regarding proposed new rules and actions for artificial intelligence. During the morning, the speakers provided some context and details around this and there was plenty of interesting discussion on potential paths forward for AI in Europe.

May 2, 2021 Exhibition @ BOZAR Brussels What is the relationship between art and artificial intelligence (AI)? Can AI become a tool for the contemporary artist? Does AI look at a painting the same way we do?

The Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is a document prepared by the High-Level Expert Group on Artificial Intelligence (AI HLEG). This independent expert group was set up by the European Commission in June 2018, as part of the AI strategy announced earlier that year. The AI HLEG presented a first draft of the Guidelines in December 2018. Following further deliberations by the group in light of discussions on the European AI Alliance, a stakeholder consultation and meetings with representatives from Member States, the Guidelines were revised and published in April 2019. In parallel, the AI HLEG also prepared a revised document which elaborates on a definition of Artificial Intelligence used for the purpose of its deliverables.

Our virtual lives lie in the hands of algorithms that govern what we see and don't see, how we perceive the world and which life choices we make. Artificial intelligence decides which movies are of interest to you, how your social media feeds should look like, and which advertisements have the highest likelihood of convincing you. These algorithms are either controlled by corporations or by governments, each of which tend to have goals that differ from the individual's objectives. In this article, we dive into the world of human-centric AI, leading to a new era where the individual not only controls the data, but also steers the algorithms to ensure fairness, privacy and trust. Breaking free from filter bubbles and detrimental echo chambers that skew the individual's worldview allows the user to truly benefit from today's AI revolution.

Buzzwords like Artificial Intelligence (AI) and machine learning are commonly heard at conferences and industry events and they often conjure up images of robots or killing machines from the Terminator. However, panelists from the Innofest Unbound conference in Singapore all felt that technologies such as AI should not replace humans as it is commonly imagined – rather, they should augment the work of clinicians and hopefully, even enhance the patients' interactions with their doctors. A medical doctor by training and also the founder of MEDGIC, a startup which utilises AI to detect skin conditions, Dr Reid Lim feels that the use of AI should always involve doctors and not replace them. "Healthcare systems are becoming unsustainable and we need AI to help automate some things and to help alleviate the burden on doctors. AI is not new and it seems strange that some people are only beginning to grasp the use AI." "A lot of radiologists are already using Computer Aided Diagnosis (CAD) for mammography and it has been happening for some time. So the idea is for us as a tech startup to pursue what we call human-centric AI. We try to make AI as explainable as possible and we always want humans to be involved in the whole process," he added.