#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 from researchers at the University of California San Diego and the University of Wisconsin–Madison, presented at the 2025 ACM CHI conference, examines how the public wants decisions made when multiple high-accuracy AI models disagree. The findings are especially relevant to Thailand as AI use grows in finance, employment, and government services.

The study centers on multiplicity—the reality that many models can achieve similar accuracy but still produce different predictions for the same case. This raises ethical questions for organizations choosing which model to deploy, particularly for loans, jobs, or social services. Data resonates with Thailand’s push to sharpen AI risk management guidelines in finance, signaling regulators’ attention to fairness in automated decisions.

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.

A growing consensus among computational neuroscientists and AI researchers is that artificial intelligence is not a direct replica of the human brain. That distinction isn’t a flaw; it’s an invitation to new frontiers in technology and science. The latest reflections come from a broader conversation sparked by research and journalism across leading institutions, underscoring how AI can be optimized while deepening our understanding of human cognition.

Thai readers have watched AI tools become integral to healthcare, education, and everyday life. Yet new insights remind us that AI’s powers rest on models that simplify how the brain works, even as they enable remarkable capabilities. The results invite a practical view: AI can boost efficiency and problem-solving without claiming to replicate human thinking, emotion, or memory.

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 new framework released in early May by a leading fintech product manager is sparking debate in global business and tech circles. It urges organizations to pause before automatically adopting large language models (LLMs) for every AI need. The piece, summarized by VentureBeat, notes that while LLMs are popular, they are not always the best fit, and can be costly and less precise than alternative machine learning (ML) or rules-based solutions.

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.

New neuroscience findings are reshaping what we mean by “thinking.” They show that artificial intelligence, though powerful, still lags far behind the human brain’s complexity and adaptability. A recent feature highlights how evolutionary advances give humans unique capabilities that machines struggle to replicate, challenging long-standing AI assumptions rooted in neural network models.

Why this matters for Thai readers. As Thailand accelerates digital transformation in health, education, and business, understanding how intelligence works—biological and artificial—helps shape better policies and practical AI applications. These insights also matter for how AI is used in Thai classrooms, hospitals, and public services, where accuracy, empathy, and cultural context matter.

A new AI-driven study sheds light on why some people stick to exercise while others abandon routines. Using data from nearly 12,000 individuals, researchers found three strong predictors of meeting weekly activity guidelines: daily sedentary time, gender, and educational attainment. The results suggest tailored health advice and smarter public campaigns could help people stay active, including in Thailand.

Thailand faces rising health risks from obesity, heart disease, and diabetes. Urban life and demanding work schedules can limit time for movement, while long commutes and crowded cities encourage sedentary habits. Thailand’s Health Promotion Foundation reports that only about 40% of Thais exercise regularly, underscoring the need for effective, culturally relevant strategies. The study’s insights offer a practical framework for policymakers and health professionals working toward healthier communities.

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.

A new study using artificial intelligence and machine learning identifies the three strongest predictors of sticking with an exercise routine: sedentary time, gender, and education level. Published in Scientific Reports, the research analyzed health data from nearly 12,000 individuals to understand who meets physical activity guidelines. The findings carry meaningful implications for Thailand, where sedentary lifestyles are rising amid urban life.

Regular exercise is essential for preventing chronic diseases. In Thailand, long hours at desks and routine traffic congestion increase the risk of inactivity-related illnesses such as diabetes and heart disease. Thai health authorities recommend at least 150 minutes of moderate activity or 75 minutes of vigorous activity each week. Yet many Thai people struggle to reach these targets, underscoring the need for effective, culturally informed strategies.

A team of researchers is advancing brain health insights by linking brain stiffness to aging and neurodegenerative risk. Led by a biomedical engineering associate professor and an electrical engineering assistant professor, the group uses magnetic resonance elastography (MRE) together with artificial intelligence to estimate the brain’s biological age. This approach could improve early detection and intervention for conditions like Alzheimer’s disease, dementia, and other neurodegenerative disorders, with potential relevance for Thailand’s aging population.