Inês Hipólito is a highly accomplished researcher, recognized for her work in esteemed journals and contributions as a co-editor. She has received research awards including the prestigious Talent Grant from the University of Amsterdam in 2021. After her PhD, she held positions at the Berlin School of Mind and Brain and Humboldt-Universität zu Berlin. Currently, she is a permanent lecturer of the philosophy of AI at Macquarie University, focusing on cognitive development and the interplay between augmented cognition (AI) and the sociocultural environment. Neurourbanism as a Novel Approach in Global Health,' funded by the Berlin University Alliance.

In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles.

Classifier-Free Guidance (CFG) [37] has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75% preference for GPT4All using CFG over baseline.

It is with great sadness that I am sharing that Joanne Pransky, the World's First Robotic Psychariatrist, and who Isaac Asimov called the real Susan Calvin passed away recently. I had several delight conversations with her, including an interview and moderated panel. Joanne was a tireless advocate for robotics AND for women in robotics. She didn't have advanced degrees in robotics but had worked in the robotics industry and then in robot trade journals- she had quite the eye for finding really useful technology versus hype. Her enthusiasm and passion for constantly learning was an inspiration to me and I was privileged to know her as a friend.

The word creativity originally described a concept from human psychology, but in the realm of computational creativity (CC), it has become much more. The question of what creativity means when it is part of a computational system might be considered core to CC. Pinning down the meaning of creativity, and concepts like it, becomes salient when researchers port concepts from human psychology to computation, a widespread practice extending beyond CC into artificial intelligence (AI). Yet, the human processes shaping human-inspired computational systems have been little investigated. In this paper, we question which human literatures (social sciences, psychology, neuroscience) enter AI scholarship and how they are translated at the port of entry. This study is based on 22 in-depth, semi-structured interviews, primarily with human-inspired AI researchers, half of whom focus on creativity as a major research area. This paper focuses on findings most relevant to CC. We suggest that which human literature enters AI bears greater scrutiny because ideas may become disconnected from context in their home discipline. Accordingly, we recommend that CC researchers document the decisions and context of their practices, particularly those practices formalizing human concepts for machines. Publishing reflexive commentary on human elements in CC and AI would provide a useful record and permit greater dialogue with other disciplines.

This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work as mobile base stations). The primary objective of the proposed algorithm is to establish dependable mobile access networks for cellular vehicle-to-everything (C-V2X) communication, thereby facilitating the realization of high-quality intelligent transportation systems (ITS). The reliable mobile access services can be achieved in following two ways, i.e., i) energy-efficient UAV operation and ii) reliable wireless communication services. For energy-efficient UAV operation, the reward of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of rewards and 60GHz mmWave radio is used for mobile access. This paper considers the 60GHz mmWave access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted in this paper. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.

Welcome to our June 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we hear about solving problems by combining deep learning and automated reasoning, find out how to learn physics from videos, and congratulate the IJCAI award winners. What does solving a Sudoku puzzle have to do with protein design? Marianne Defresne reveals all in this blog post where she talks about work, with Sophie Barbe and Thomas Schiex, combining deep learning with automated reasoning to solve complex problems. In their work 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes, Haotian Xue, Antonio Torralba, Joshua Tenenbaum, Daniel Yamins, Yunzhu Li and Hsiao-Yu Tung present a framework for learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids.

Republican presidential candidate Francis Suarez appeared to admit during a Tuesday morning radio interview about national security that he does not know what a Uyghur is. The admission from Suarez came during an appearance on The Hugh Hewitt Show, where Hewitt asked Suarez, "Will you be talking about the Uyghurs in your campaign?" "The what," Suarez, the current mayor of Miami, responded. Republican presidential candidate and Mayor of Miami Francis Suarez delivers remarks at the Faith and Freedom Road to Majority conference on June 23, 2023, in Washington, DC. (Drew Angerer/Getty Images) "What's a Uyghur," Suarez inquired further. Moving on from the question due to Suarez's inability to identify what a Uyghur is, Hewitt told the mayor, "You've got to get smart on that."

This study aims to investigate the possible relationship between the mechanisms of social influence and the choice of airline, through the use of new tools, with the aim of understanding whether they can contribute to a better understanding of the factors influencing the decisions of consumers in the aviation sector. We have chosen to extract user reviews from well-known platforms: Trustpilot, Google, and Twitter. By combining web scraping techniques, we have been able to collect a comprehensive dataset comprising a wide range of user opinions, feedback, and ratings. We then refined the BERT model to focus on insightful sentiment in the context of airline reviews. Through our analysis, we observed an intriguing trend of average negative sentiment scores across various airlines, giving us deeper insight into the dynamics between airlines and helping us identify key partnerships, popular routes, and airlines that play a central role in the aeronautical ecosystem of Catania airport during the specified period. Our investigation led us to find that, despite an airline having received prestigious awards as a low-cost leader in Europe for two consecutive years 2021 and 2022, the "Catanese" user tends to suffer the dominant position of other companies. Understanding the impact of positive reviews and leveraging sentiment analysis can help airlines improve their reputation, attract more customers, and ultimately gain a competitive edge in the marketplace.

John Goodenough, who shared the 2019 Nobel prize in chemistry for his pioneering work developing the lithium-ion battery that transformed technology with rechargeable power for devices ranging from cellphones and computers to pacemakers and electric cars, has died at 100, the University of Texas announced on Monday. Goodenough died on Sunday at an assisted living facility in Austin, Texas, the university announced. No cause of death was given. The American was "was a leader at the cutting edge of scientific research throughout the many decades of his career", said Jay Hartzell, president of the University of Texas at Austin, where Goodenough was a faculty member for 37 years. Goodenough was the oldest person to receive a Nobel prize when he shared the award with British-born American scientist M Stanley Whittingham and Japan's Akira Yoshino.