#MachineLearning

Recent research from the University of California San Diego and University of Wisconsin–Madison reveals critical insights about public expectations for algorithmic decision-making in high-stakes contexts. The study, presented at the 2025 ACM CHI conference, explored how ordinary people react when multiple high-accuracy machine learning models reach different conclusions for identical applications. The findings challenge both current industry practices and academic assumptions about fair automated decision-making, with direct implications for Thailand’s rapidly expanding use of AI systems in financial services, employment, and government programs.

A new study by computer scientists at the University of California San Diego and the University of Wisconsin–Madison warns that relying on a single “best” machine learning (ML) model for high‑stakes decisions — from loan approvals to hiring — can undermine perceived fairness, and that ordinary people prefer human arbitration when equally good models disagree. The research, presented at the 2025 ACM CHI conference, explored how lay stakeholders react when multiple high‑accuracy models reach different conclusions for the same applicant and found strong resistance to both single‑model arbitrariness and to solutions that simply randomize outcomes; instead participants favored wider model searches, transparency and human decision‑making to resolve disagreements UC San Diego report and the authors’ paper Perceptions of the Fairness Impacts of Multiplicity in Machine Learning (CHI 2025) presents the detailed results and recommendations.

Recent research published in Quanta Magazine reveals a growing consensus among computational neuroscientists and artificial intelligence (AI) researchers: AI, despite its name and inspiration, is fundamentally unlike the human brain—but that’s not a flaw, it’s an opportunity for new frontiers in both technology and neuroscience. This divergence, explored in the article “AI Is Nothing Like a Brain, and That’s OK” (Quanta Magazine, 2025), is now informing efforts to both make AI more efficient and gain deeper understanding of our own minds.

A new framework released in early May by a leading fintech group product manager is making waves throughout the global business and technology communities, urging organizations to reconsider the automatic use of large language models (LLMs) for every artificial intelligence (AI) application. The article, recently published by VentureBeat, cautions that LLMs—despite their popularity—are not always the best fit for all customer needs and often prove costly and imprecise compared to other machine learning (ML) or rules-based solutions (VentureBeat).

A groundbreaking wave of neuroscience research is redefining what it means to think—and, crucially, why artificial intelligence (AI) still falls far short of the intricacies of the human mind. A newly published feature in Salon highlights the distinct evolutionary adaptations that make the human brain more than a glorified computer, challenging decades-old assumptions fundamental to AI development and the neural network concept that underlies machine learning models.

For years, popular understanding—and much of AI research—has treated the brain as a vast network made up of nearly identical neurons whose intelligence emerges through the patterns of their collective firing. This view inspired so-called artificial neural networks, computer systems designed to solve problems by mimicking the way brains process information. Such analogies, cemented over decades, fostered the belief that if machines could imitate the structure and connectivity of brains, they might one day match, or even surpass, human intellect. But recent scientific discoveries show this metaphor misses the mark in fundamental ways, with profound implications for both neuroscience and the future of AI.

A groundbreaking study using artificial intelligence (AI) has taken a scientific leap in answering an age-old question: why do some people stick with regular exercise, while others quickly fall off the wagon? By analyzing the habits and characteristics of nearly 12,000 individuals, a research team from the University of Mississippi has identified three surprisingly robust predictors of exercise adherence—how much time you spend sitting, your gender, and your education level—shedding new light on what keeps people committed to their fitness goals. This finding could open the door to more individualized health advice and smarter public health campaigns worldwide, including here in Thailand (Neuroscience News, Science Daily).

A groundbreaking new study using artificial intelligence (AI) and machine learning has revealed the most crucial factors that keep people committed to their exercise routines: how much time they spend sitting, their gender, and their education level. Published in the prestigious journal Scientific Reports, this research analyzed health data from nearly 12,000 individuals, offering fresh insights into what helps people meet physical activity guidelines—a finding with important implications for Thailand, where sedentary lifestyles are increasingly common.