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.

A decade after its widespread adoption, it's safe to say that U.S. schools utterly failed to acclimate our students to social media and to anticipate the profound damage it could do. The result of widespread social media use among students (said the U.S. surgeon general in a recent advisory) is increased anxiety, stress, and depression. As a school technology specialist working with a population of middle- and high-schoolers, I see firsthand parents' desperation when I host standing-room-only sessions about social media and mental health. With the swift emergence of A.I., educators have an opportunity to do better. This summer, K–12 schools must get to work drafting academic policies governing the use of A.I. and facilitating professional development for teachers about the new technology.

Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of data-driven persona development performed today is based on qualitative data from a limited set of interviewees and transformed into personas using labour-intensive manual techniques. In this study, we propose a method that employs the modelling of user stereotypes to automate part of the persona creation process and addresses the drawbacks of the existing semi-automated methods for persona development. The description of the method is accompanied by an empirical comparison with a manual technique and a semi-automated alternative (multiple correspondence analysis). The results of the comparison show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters, while lowering the labour associated with finding the clusters in the dataset. The output of the method also represents the largest variances in the dataset identified by the multiple correspondence analysis.

Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threats to human life. The quality assurance of MLSSs is considered a challenging task and currently is a hot research topic. Objective: This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of quality issues in MLSSs. Method: We conduct a set of interviews with practitioners/experts, to gather insights about their experience and practices when dealing with quality issues. We validate the identified quality issues via a survey with ML practitioners. Results: Based on the content of 37 interviews, we identified 18 recurring quality issues and 24 strategies to mitigate them. For each identified issue, we describe the causes and consequences according to the practitioners' experience. Conclusion: We believe the catalog of issues developed in this study will allow the community to develop efficient quality assurance tools for ML models and MLSSs. A replication package of our study is available on our public GitHub repository.

Advanced programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sample efficiency in reproducibly finding optimal hyperparameter values of machine learning (ML) models. Yet, ML practitioners often apply less sample-efficient HPO methods, such as grid search, which often results in under-optimized ML models. As a reason for this behavior, we suspect practitioners choose HPO methods based on individual motives, consisting of contextual factors and individual goals. However, practitioners' motives still need to be clarified, hindering the evaluation of HPO methods for achieving specific goals and the user-centered development of HPO tools. To understand practitioners' motives for using specific HPO methods, we used a mixed-methods approach involving 20 semi-structured interviews and a survey study with 71 ML experts to gather evidence of the external validity of the interview results. By presenting six main goals (e.g., improving model understanding) and 14 contextual factors affecting practitioners' selection of HPO methods (e.g., available computer resources), our study explains why practitioners use HPO methods that seem inappropriate at first glance. This study lays a foundation for designing user-centered and context-adaptive HPO tools and, thus, linking social and technical research on HPO.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. THE COURT: Please be seated. Let's try to keep the temperature down in here. We don't need a repeat of yesterday. It'll just be Mr. Blatz and myself today. Sorry, it's hard to tell with … are you with us? ORWELL: Omni-dimensional Recursively Written Entity for Language Learning present and ready, Your Honor. THE COURT: You can just say ORWELL. Are we ready to proceed? LIU: Your Honor, we'd like to call the Defendant to the stand. Mr. Blatz will handle examination. THE COURT: We have the wiring sorted out? Please refrain from using the monitor on the Defendant's table until you're off the stand.

For this week's TIME100 Most Influential Companies cover story about OpenAI and its CEO Sam Altman, TIME's former editor-in-chief Edward Felsenthal sat down with a number of company executives in early May, including two sessions with Altman, transcribed below. The conversations have been condensed and edited for clarity. Sam Altman: One thing I use it for every day is help with summarization. I can't really keep up on my inbox anymore, but I made a little thing to help it summarize for me and pull out important stuff from unknown senders, and that's very helpful. I used it to translate an article for someone I'm meeting next week, to prepare for that. This is sort of a funny thing, I used it to help me draft a tweet that I was having a hard time with. Not as much as it might have seemed from the outside.