When a fish holds its position against a current in a river, its brain must figure out how fast to swim and how to steer to offset the water flow. Most fish use vision to register the world sliding past, detect optic flow speed and direction, and their brains turn these signals into compensatory swimming. The retina captures signals of optic-flow direction, central pretectal neurons interpret direction, and spinal nerves drive muscle contractions. The catch is that one cannot easily change the living brain to test how these circuits work. Although advances in imaging now allow detailed recording, and even manipulation, of neurons alongside behavior, rewiring connections to ask what a particular link actually does remains almost impossible in the living, complex animal.

The talk presented in this podcast, "Where do Ethics Belong in Artificial Intelligence?", It was presented at Oregon State University by Houssam Abbas (assistant professor of electrical engineering) and Alicia Patterson (assistant professor of philosophy) as part of an AI seminar series. "Engineering Out Loud" is a podcast from the College of Engineering at Oregon State University. It is for anyone who wants to know more about how engineering is changing the world. Hear from researchers about how they are tackling humanity's biggest challenges, including enabling access to clean water, preparing and recovering from natural hazards, and improving human the health and safety.

Autonomous vehicles (AVs) have been tested as taxis for decades in San Francisco, Pittsburgh and around the world, and trucking companies have enormous incentives to adopt them. But AV companies rarely share the crash-and safety-related data that is crucial to improving the safety of their vehicles - mostly because they have little incentive to do so. Is AV safety data an auto company's intellectual asset or a public good? It can be both - with a little tweaking, according to a team of Cornell researchers. The team has created a roadmap outlining the barriers and opportunities to encourage AV companies to share the data to make AVs safer, from untangling public versus private data knowledge, to regulations to creating incentive programs.

Artificial intelligence is accelerating a new era of food innovation, connecting data from farm to consumer to improve formulation, processing, and health outcomes. Recent advances in deep learning, natural language processing, and multi-omics integration make it possible to understand and optimize food systems with unprecedented depth. However, AI adoption across the food sector remains uneven due to heterogeneous datasets, limited model and system interoperability, and a persistent skills gap between data scientists and food domain experts. To address these challenges and advance responsible innovation, the AI Institute for Next Generation Food Systems (AIFS) convened the inaugural AI for Food Product Development Symposium at University of California, Davis, in October 2025. This white paper synthesizes insights from the symposium, organized around five domains where AI can have the greatest near-term impact: supply chain; formulation and processing; consumer insights and sensory prediction; nutrition and health; and education and workforce development. Across the areas, participants emphasized the importance of interoperable data standards, transparent and interpretable models, and cross-sector collaboration to accelerate the translation of AI research into practice. The discussions further highlighted the need for robust digital infrastructure, privacy-preserving data-sharing mechanisms, and interdisciplinary training pathways that integrate AI literacy with domain expertise. Collectively, the priorities outline a roadmap for integrating AI into food manufacturing in ways that enhance innovation, sustainability, and human well-being while ensuring that technological progress remains grounded in ethics, scientific rigor, and societal benefit.

UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evaluations insufficient. Capturing these dynamics requires longer-term studies, but challenges in deployment, evaluation design, and data collection have made such longitudinal research difficult to implement. Our workshop aims to tackle these challenges and prepare researchers with practical strategies for longitudinal studies. The workshop includes a keynote, panel discussions, and interactive breakout groups for discussion and hands-on protocol design and tool prototyping sessions. We seek to foster a community around longitudinal system research and promote it as a more embraced method for designing, building, and evaluating UIST tools.

Compared with conventional psychological models, which use simple math equations, Centaur did a far better job of predicting behavior. Accurate predictions of how humans respond in psychology experiments are valuable in and of themselves: For example, scientists could use Centaur to pilot their experiments on a computer before recruiting, and paying, human participants. In their paper, however, the researchers propose that Centaur could be more than just a prediction machine. By interrogating the mechanisms that allow Centaur to effectively replicate human behavior, they argue, scientists could develop new theories about the inner workings of the mind. But some psychologists doubt whether Centaur can tell us much about the mind at all.

Researchers at Stanford University recently tested out some of the more popular AI tools on the market, from companies like OpenAI and Character.ai, The researchers found that when they imitated someone who had suicidal intentions, these tools were more than unhelpful -- they failed to notice they were helping that person plan their own death. "[AI] systems are being used as companions, thought-partners, confidants, coaches, and therapists," says Nicholas Haber, an assistant professor at the Stanford Graduate School of Education and senior author of the new study. "These aren't niche uses – this is happening at scale." AI is becoming more and more ingrained in people's lives and is being deployed in scientific research in areas as wide-ranging as cancer and climate change.

A team of researchers at Rice University and Baylor College of Medicine has developed a new strategy for identifying hazardous pollutants in soil, even ones that have never been isolated or studied in a lab. The new approach, described in a study published in Proceedings of the National Academy of Sciences, uses light-based imaging, theoretical predictions of compounds' light signatures and machine learning (ML) algorithms to detect toxic compounds like polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil. A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems. Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.

Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. To develop voice assistants like Siri and Alexa, companies spend years investigating what sounds like a human voice and what doesn't. But what we've ended up with is just one possibility of the kinds of voices that we could be interacting with. In this episode, we talked to sound engineer Frederik Juutilainen, and assistant professor at the University of Copenhagen, Stina Hasse Jørgensen, about their participation in [multi'vocal], an experimental research project that created an alternative voice assistant by asking people at a rock festival in Denmark to speak into a portable recording box. We talk about voice assistants' inability to stutter, lisp and code switch, and whether a voice can express multiple personalities, genders and ages.

Some of the earliest work on artificial intelligence (AI) saw mathematics as a major target and key to making breakthroughs quickly. In 1961, leading computer scientist and AI pioneer John McCarthy argued at the Fifth Symposium in Pure Mathematics that the job of checking mathematical proofs would likely be "one of the most interesting and useful applications of automatic computers." McCarthy saw the possibility for mathematicians to try out different ideas for proofs quickly that the computers then tested for correctness. More than 60 years later, such a proof assistant has yet to appear. But recent developments in both mathematics and computer science may see a breakthrough sooner rather than later.