It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Friday, May 30, 2025
Borders and beyond: Excavating life on the medieval Mongolian frontier
New archaeological findings along a little-known medieval wall in eastern Mongolia reveal that frontier life was more complex than previously believed. Excavations show evidence of permanent habitation, agriculture, and cultural exchange, suggesting that these walls were not solely defensive structures but part of a broader system of regional control and interaction during the Jin dynasty.
A team of international archaeologists led by Professor Gideon Shelach-Lavi of the Department of Asian Studies at the Hebrew University of Jerusalem has uncovered new insights into life along one of Asia’s most enigmatic medieval frontiers. Their findings, recently published in the journal Antiquity, focus on a little-known section of the Medieval Wall System and reveal that the main function of this section was not military defense. In fact, excavation reviled that in this part of the Medieval Wall System there was now standing linear wall but only a relatively shallow trench that starched over 300 km long. Researchers now believe that the main function of this line, that included also walled forts, was managing movement of nomadic populations, controlling local unrest, regulating trade, marking territory, and shaping regional interactions.
The Medieval Wall System is a vast network of trenches, earthen walls, and fortified enclosures constructed between the tenth and thirteenth centuries across parts of Mongolia, China, and Russia. Despite its impressive scale, many segments remain poorly understood. Since 2018, the collaborative research project The Wall: People and Ecology in Medieval Mongolia and China based in the Hebrew university—funded by the European Research Council—has worked to map, excavate, and interpret these monumental features. The 2023 field season focused on the Mongolian Arc, a remote frontier zone running through Mongolia’s Sukhbaatar and Dornod provinces parallel to the current border with China.
“Our goal was not only to understand how these walls were built, but to uncover what life was like for the people who lived near them,” explained Professor Shelach-Lavi. “This goes beyond military history—it’s about reconstructing everyday experiences on the edges of imperial power.”
The team’s excavation centered on a fortified enclosure known as MA03 in Sukhbaatar Province, dated by radiocarbon analysis to the period of the Jin dynasty (twelfth to thirteenth century). Although traditionally thought to serve defensive purposes, the shallow trench near MA03 lacked a substantial wall, suggesting that it functioned more as a territorial marker or checkpoint than a military barrier. Within the enclosure, the researchers uncovered stone architecture, an advanced heating system, and a range of artifacts—including animal bones, pottery, iron tools, and a broken iron plough. These remains point to a permanent settlement engaged in herding, hunting, and agriculture, challenging the common perception of the region as exclusively nomadic. The heating system, similar to those found in medieval China and Korea, further suggests cultural exchange and adaptation to Mongolia’s severe winters.
One of the most striking discoveries was a mid-fifteenth-century burial inserted long after the enclosure had been abandoned. The grave, which contained well preserved textiles, wooden objects, and metal artifacts, was dug directly into the collapsed remains of the enclosure wall.
“This tells us that even centuries later, the site still held meaning,” said Professor Shelach-Lavi. “It remained visible in the landscape and may have been remembered—or even revered—by later communities.”
The findings contribute to a growing body of research suggesting that ancient frontier walls across Eurasia served not just military ends, but also administrative and symbolic functions. In the context of Mongolia—long associated with mobile pastoralism—the study reveals a more complex and adaptable way of life.
“Our research reminds us to look beyond capital cities and royal courts,” said Professor Shelach-Lavi. “People lived, worked, traded, and built communities along these borderlands.
Understanding their lives helps us understand the broader dynamics that shaped Eurasian history.”
Generative AI critic Gary Marcus, speaks at the Web Summit Vancouver 2025 tech conference in Vancouver Canada - Copyright AFP
Don MacKinnon
Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence’s great skeptic, playing a counter-narrative to Silicon Valley’s AI true believers.
Marcus became a prominent figure of the AI revolution in 2023, when he sat beside OpenAI chief Sam Altman at a Senate hearing in Washington as both men urged politicians to take the technology seriously and consider regulation.
Much has changed since then. Altman has abandoned his calls for caution, instead teaming up with Japan’s SoftBank and funds in the Middle East to propel his company to sky-high valuations as he tries to make ChatGPT the next era-defining tech behemoth.
“Sam’s not getting money anymore from the Silicon Valley establishment,” and his seeking funding from abroad is a sign of “desperation,” Marcus told AFP on the sidelines of the Web Summit in Vancouver, Canada.
Marcus’s criticism centers on a fundamental belief: generative AI, the predictive technology that churns out seemingly human-level content, is simply too flawed to be transformative.
The large language models (LLMs) that power these capabilities are inherently broken, he argues, and will never deliver on Silicon Valley’s grand promises.
“I’m skeptical of AI as it is currently practiced,” he said. “I think AI could have tremendous value, but LLMs are not the way there. And I think the companies running it are not mostly the best people in the world.”
His skepticism stands in stark contrast to the prevailing mood at the Web Summit, where most conversations among 15,000 attendees focused on generative AI’s seemingly infinite promise.
Many believe humanity stands on the cusp of achieving super intelligence or artificial general intelligence (AGI) technology that could match and even surpass human capability.
That optimism has driven OpenAI’s valuation to $300 billion, unprecedented levels for a startup, with billionaire Elon Musk’s xAI racing to keep pace.
Yet for all the hype, the practical gains remain limited.
The technology excels mainly at coding assistance for programmers and text generation for office work. AI-created images, while often entertaining, serve primarily as memes or deepfakes, offering little obvious benefit to society or business.
Marcus, a longtime New York University professor, champions a fundamentally different approach to building AI — one he believes might actually achieve human-level intelligence in ways that current generative AI never will.
“One consequence of going all-in on LLMs is that any alternative approach that might be better gets starved out,” he explained.
This tunnel vision will “cause a delay in getting to AI that can help us beyond just coding — a waste of resources.” – ‘Right answers matter’ –
Instead, Marcus advocates for neurosymbolic AI, an approach that attempts to rebuild human logic artificially rather than simply training computer models on vast datasets, as is done with ChatGPT and similar products like Google’s Gemini or Anthropic’s Claude.
He dismisses fears that generative AI will eliminate white-collar jobs, citing a simple reality: “There are too many white-collar jobs where getting the right answer actually matters.”
This points to AI’s most persistent problem: hallucinations, the technology’s well-documented tendency to produce confident-sounding mistakes.
Even AI’s strongest advocates acknowledge this flaw may be impossible to eliminate.
Marcus recalls a telling exchange from 2023 with LinkedIn founder Reid Hoffman, a Silicon Valley heavyweight: “He bet me any amount of money that hallucinations would go away in three months. I offered him $100,000 and he wouldn’t take the bet.”
Looking ahead, Marcus warns of a darker consequence once investors realize generative AI’s limitations. Companies like OpenAI will inevitably monetize their most valuable asset: user data.
“The people who put in all this money will want their returns, and I think that’s leading them toward surveillance,” he said, pointing to Orwellian risks for society.
“They have all this private data, so they can sell that as a consolation prize.”
Marcus acknowledges that generative AI will find useful applications in areas where occasional errors don’t matter much.
“They’re very useful for auto-complete on steroids: coding, brainstorming, and stuff like that,” he said.
“But nobody’s going to make much money off it because they’re expensive to run, and everybody has the same product.”
Stevens team teaches AI models to spot misleading scientific reporting
Using AI to flag unscientific claims could empower people to engage more confidently with media reports
Hoboken, N.J., May 28, 2025 — Artificial intelligence isn’t always a reliable source of information: large language models (LLMs) like Llama and ChatGPT can be prone to “hallucinating” and inventing bogus facts. But what if AI could be used to detect mistaken or distorted claims, and help people find their way more confidently through a sea of potential distortions online and elsewhere?
As presented at a workshop at the annual conference of the Association for the Advancement of Artificial Intelligence, researchers at Stevens Institute of Technology present an AI architecture designed to do just that, using open source LLMs and free versions of commercial LLMs to identify potential misleading narratives in news reports on scientific discoveries.
“Inaccurate information is a big deal, especially when it comes to scientific content — we hear all the time from doctors who worry about their patients reading things online that aren’t accurate, for instance,” said K.P. Subbalakshmi, the paper’s co-author and a professor in the Department of Electrical and Computer Engineering at Stevens. “We wanted to automate the process of flagging misleading claims and use AI to give people a better understanding of the underlying facts.”
To achieve that, the team of two PhD students and two Masters students led by Subbalakshmi, first created a dataset of 2,400 news reports on scientific breakthroughs. The dataset included both human-generated reports, drawn either from reputable science journals or low-quality sources known to publish fake news, and AI-generated reports of which half were reliable and half contained inaccuracies. Each report was then paired with original research abstracts related to the technical topic, enabling the team to check each report for scientific accuracy. Their work is the first attempt at systematically directing LLMs to detect inaccuracies in science reporting in public media according to Subbalakshmi.
“Creating this dataset is an important contribution in its own right, since most existing datasets typically do not include information that can be used to test systems developed to detect inaccuracies ‘in the wild’” Dr. Subbalakshmi said. “These are difficult topics to investigate, so we hope this will be a useful resource for other researchers.”
Next, the team created three LLM-based architectures to guide an LLM through the process of determining a news report’s accuracy. One of these architectures is a three-step process. First, the AI model summarized each news report and identified the salient features. Next, it conducted sentence-level comparisons between claims made in the summary and evidence contained in the original peer-reviewed research. Finally, the LLM made a determination as to whether the report accurately reflected the original research.
The team also defined “dimensions of validity” and asked the LLM to think about these five “dimensions of validity” — specific mistakes, such as oversimplification or confusing causation and correlation, commonly present in inaccurate news reports. “We found that asking the LLM to use these dimensions of validity made quite a big difference to the overall accuracy,” Dr. Subbalakshmi said and added that these dimensions of validity can be expanded upon, to better capture domain specific inaccuracies, if needed.
Using the new dataset, the team’s LLM pipelines were able to correctly distinguish between reliable and unreliable news reports with about 75% accuracy — but proved markedly better at identifying inaccuracies in human-generated content than in AI-generated reports. The reasons for that aren’t yet clear, although Dr. Subbalakshmi notes that non-expert humans similarly struggle to identify technical errors in AI-generated text. “There’s certainly room for improvement in our architecture,” Dr. Subbalakshmi says. “The next step might be to create custom AI models for specific research topics, so they can ‘think’ more like human scientists.”
In the long run, the team’s research could open the door to browser plugins that automatically flag inaccurate content as people use the Internet, or to rankings of publishers based on how accurately they cover scientific discoveries. Perhaps most importantly, Dr. Subbalakshmi says, the research could also enable the creation of LLM models that describe scientific information more accurately, and that are less prone to confabulating when describing scientific research.
“Artificial intelligence is here — we can’t put the genie back in the bottle,” Dr. Subbalakshmi said. “But by studying how AI ‘thinks’ about science, we can start to build more reliable tools — and perhaps help humans to spot unscientific claims more easily, too.”
About Stevens Institute of Technology Stevens Institute of Technology is a premier, private research university situated in Hoboken, New Jersey. Since our founding in 1870, technological innovation has been the hallmark of Stevens’ education and research. Within the university’s three schools and one college, more than 8,000 undergraduate and graduate students collaborate closely with faculty in an interdisciplinary, student-centric, entrepreneurial environment. Academic and research programs spanning business, computing, engineering, the arts and other disciplines actively advance the frontiers of science and leverage technology to confront our most pressing global challenges. The university continues to be consistently ranked among the nation’s leaders in career services, post-graduation salaries of alumni and return on tuition investment.
Article Title
CoSMis: A Hybrid Human-LLM COVID Related Scientific Misinformation Dataset and LLM pipelines for Detecting Scientific Misinformation in the Wild
Horses ‘mane’ inspiration for new generation of social robots
Interactive robots should not just be passive companions, but active partners–like therapy horses who respond to human emotion–say University of Bristol researchers.
Equine-assisted interventions (EAIs) offer a powerful alternative to traditional talking therapies for patients with PTSD, trauma and autism, who struggle to express and regulate emotions through words alone.
The study, presented at the CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems held in Yokohama, recommends that therapeutic robots should also exhibit a level of autonomy, rather than one-dimensional displays of friendship and compliance.
Lead author Ellen Weir from Bristol’s Faculty of Science and Engineering explains: “Most social robots today are designed to be obedient and predictable - following commands and prioritising user comfort.
“Our research challenges this assumption.”
In EAIs, individuals communicate with horses through body language and emotional energy. If someone is tense or unregulated, the horse resists their cues. When the individual becomes calm, clear, and confident, the horse responds positively. This ‘living mirror’ effect helps participants recognise and adjust their emotional states, improving both internal well-being and social interactions.
However, EAIs require highly trained horses and facilitators, making them expensive and inaccessible.
Ellen continued: “We found that therapeutic robots should not be passive companions but active co-workers, like EAI horses.
“Just as horses respond only when a person is calm and emotionally regulated, therapeutic robots should resist engagement when users are stressed or unsettled. By requiring emotional regulation before responding, these robots could mirror the therapeutic effect of EAIs, rather than simply providing comfort.”
This approach has the potential to transform robotic therapy, helping users develop self-awareness and regulation skills, just as horses do in EAIs.
Beyond therapy, this concept could influence human-robot interaction in other fields, such as training robots for social skills development, emotional coaching, or even stress management in workplaces.
A key question is whether robots can truly replicate - or at least complement - the emotional depth of human-animal interactions. Future research must explore how robotic behaviour can foster trust, empathy, and fine tuning, ensuring these machines support emotional well-being in a meaningful way.
Ellen added: “The next challenge is designing robots that can interpret human emotions and respond dynamically—just as horses do. This requires advances in emotional sensing, movement dynamics, and machine learning.
“We must also consider the ethical implications of replacing sentient animals with machines. Could a robot ever offer the same therapeutic value as a living horse? And if so, how do we ensure these interactions remain ethical, effective, and emotionally authentic?”
Caption
Diagram showing how Equine-Assisted Interventions (EAIs) work
Diagram showing how Equine-Assisted Interventions (EAIs) work.
This compelling book sheds light on one of the most defining yet often overlooked forces in Japanese society: dōchō atusryoku (conformity pressure). Far beyond a matter of individual psychology, this book argues that conformity acts as an important force in shaping politics, governance, and the legal system in Japan. It is a force that binds people together, enforces unspoken rules, and even fills the gaps where laws or clear leadership are absent.
At the heart of this analysis is the concept of “sekken”—the collective social gaze or the “public” or “society” that influences behavior through expectations rather than laws. The book traces how this traditional social framework has transformed into modern-day norms such as “reading the air” (kuuki wo yomu), making Japanese society particularly vulnerable to widespread, invisible social pressure. These pressures extend into every aspect of life, from daily interactions to national responses during crises.
Drawing on both historical and contemporary examples, the book investigates how social conformity has impacted Japan during key moments: the wartime era, the economic miracle of the post-war years, the COVID-19 pandemic between 2020 and 2022, and recent societal reactions to high-profile cases of sexual violence. These cases vividly illustrate how social pressure can influence not only individual behavior but also institutional responses and national policy.
Building on classic works like Ruth Benedict’s The Chrysanthemum and the Sword and Nakane Chie’s Japanese Society, which explains the vertical structure of interpersonal relationships in Japan, this book offers a fresh lens through which to understand Japan’s unique social dynamics. It resonates with Karel van Wolferen’s seminal 1989 analysis in The Enigma of Japanese Power, where Japan is described as a "stateless nation" ruled by diffuse systems rather than clear lines of accountability.
For readers interested in Japanese culture, society, group dynamics, or legal and political systems, this book offers both accessible explanations and deep analytical insights. It doesn’t just describe how Japanese people act—it asks why, and reveals the underlying social currents that shape behavior in ways outsiders (and even insiders) may not always see.
Essential reading for anyone who wants to understand the true mechanics of Japanese society—not through language or laws, but through the unseen forces that move people and shape the nation.
Keywords: Japan, dchō asuryoku (conformity pressure), kūki (atmosphere or mood), social forces, social influence, social relationships, social psychology, social control, sociology, group dynamics, collectivism, law and society, seken, the COVID-19 pandemic, social pressure, a historic sexual assault scandal, war-time Japan, situational justice, politics, corporate practice, corporate seken, economic success, entertainment industry, media, positive and negative effects, governance
Author: NISHIKAWA, Yukiko Ph.D., is a professor at Doshisha University, Kyoto, Japan. Her research interests include politics and society in Japan, Japan’s diplomacy, and security and politics in East and Southeast Asia. She has published several books on Japan and Southeast Asia. Her main publications include International Norms and Local Politics in Myanmar (Routledge, 2022); Globalization and Local Conflicts in Africa and Asia (editor: Springer, 2022); Political Sociology of Japanese Pacifism (Routledge, 2018); Human Security in Southeast Asia (Routledge, 2010); Japan’s Changing Role in Humanitarian Crises (Routledge, 2005).
Communication challenges may result from a mismatch in perspectives between autistic and non-autistic individuals. While social interactions and behaviors differ across cultures, their impact on socio-cognitive abilities remains unclear. A cross-cultural analysis by researchers from Japan reveals differences in the mentalizing performance of Japanese and British autistic and non-autistic adults, highlighting the need for culturally sensitive measures to diagnose autism accurately.
Autism spectrum disorders are associated with difficulties in social communication, long attributed to individual socio-cognitive deficits. As a consequence of this perspective, stigma and pressure to conform to neurotypical social norms often lead to mental health challenges among autistic individuals. Emerging theories suggest that communication difficulties may rather arise from mismatches in perspectives between autistic and non-autistic partners. Addressing this mismatch collaboratively could transform the understanding of autism and improve communication outcomes.
Social behavior also varies significantly across cultures. Gestures, eye contact, and body language that are considered appropriate in one culture may be perceived differently in another. Nevertheless, current socio-cognitive assessments largely reflect Western norms, limiting their applicability to non-Western populations.
To bridge this gap, researchers from Japan conducted a cross-cultural analysis to compare mentalizing difficulties, or challenges in understanding the thoughts and feelings of others, in British and Japanese autistic and non-autistic adults. Led by Dr. Bianca Schuster, a researcher at Waseda University, Japan, with co-authors Associate Professor Yuko Okamoto and Professor Rieko Osu from Waseda University, Professor Hirotaka Kosaka from the University of Fukui, and Dr. Masakazu Ide from the National Rehabilitation Center for Persons With Disabilities, the study highlights the importance of considering neurodivergent perspectives rather than attributing difficulties solely to autistic individuals.
Explaining the rationale behind their work, Dr. Schuster says, “Autistic and non-autistic people have different experiences and therefore perceive and interact with the world differently. Such a mismatch in perspectives can lead to difficulties in understanding each other’s respective social cues – a problem termed the 'double empathy problem.’ This theory has received a lot of attention in recent years, but there are still very few studies that have formally tested it.” Their findings were published in Volume 16 of Molecular Autism on May 14, 2025.
The researchers used animations showing social scenes, depicted by moving triangles, and asked participants to interpret what was happening. They found that non-autistic British adults struggled to interpret animations created by their autistic peers. In contrast, British autistic adults demonstrated similar performances when interpreting animations made by both autistic and non-autistic people. The fact that British autistic adults did not perform better with animations made by their own neurotype may reflect that, in comparison to neurotypical groups, the perspectives of British neurodivergent individuals may be too varied.
Conversely, Japanese autistic and non-autistic adults interpreted animations created by their own and the respective other group with comparable accuracy. Notably, cross-cultural analyses revealed that while there was no difference in performance between Japanese and British non-autistic adults, Japanese autistic participants outperformed both groups of British participants. In addition, animations created by Japanese autistic adults were interpreted with higher accuracy by all autistic participants. Nevertheless, motor performance was comparable across all participants.
Overall, these findings support a paradigm shift toward treating autism as a different way of experiencing and interpreting the world, while recognizing it as a social disability shaped by challenges within a predominantly neurotypical environment. An inclusive environment that values socially diverse behaviors can support autistic individuals and enhance their mental well-being. Furthermore, the observed results do not likely mean that Japanese people are better at mentalizing than British individuals, because real-world difficulties in communication and social understanding do exist between autistic and non-autistic people in Japan, too. Instead, the findings may indicate that the task used in the current study may not be sensitive enough to detect mentalizing differences in the Japanese culture, highlighting the need to develop more culturally sensitive research and diagnostic tools.
“Cultural differences related to the diagnosis of autism may be subtle but can still lead to misclassification of cases and therefore have a significant impact on the lives of individuals. The results of this study highlight the urgent need for culturally inclusive research and the development of diagnostic criteria and tools that accurately reflect and respect the diverse manifestations of autism in different cultural contexts.” Dr. Schuster adds.
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Reference Authors: B. A. Schuster1,2,3,Y. Okamoto2,4, T. Takahashi1, Y. Kurihara1, C. T. Keating5,6, J. L. Cook5, H. Kosaka7, M. Ide8, H. Naruse7, C. Kraaijkamp5, and R. Osu1 DOI: 10.1186/s13229-025-00659-z Affiliations: 1School of Human Sciences, Waseda University 2Waseda Institute for Advanced Study, Waseda University 3Department of Cognition, Emotion, and Methods in Psychology, University of Vienna of Vienna 4Department of Sports Science, Faculty of Health and Sports Science, Juntendo University 5Centre for Human Brain Health and School of Psychology, University of Birmingham 6Department of Experimental Psychology, University of Oxford 7University of Fukui 8National Rehabilitation Center for Persons With Disabilities
About Waseda University Located in the heart of Tokyo, Waseda University is a leading private research university that has long been dedicated to academic excellence, innovative research, and civic engagement at both the local and global levels since 1882. The University has produced many changemakers in its history, including nine prime ministers and many leaders in business, science and technology, literature, sports, and film. Waseda has strong collaborations with overseas research institutions and is committed to advancing cutting-edge research and developing leaders who can contribute to the resolution of complex, global social issues. The University has set a target of achieving a zero-carbon campus by 2032, in line with the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015. To learn more about Waseda University, visit https://www.waseda.jp/top/en
About Dr. Bianca Schuster from Waseda University Dr. Bianca Schuster is a researcher at Waseda University, Japan, and the University of Vienna, Austria. She is interested in the computational and neurochemical processes that shape our everyday social behavior. She conducts computational modeling to investigate how humans utilize uncertainties in their prior knowledge and new information to draw maximally optimal conclusions in social settings. Additionally, Dr. Schuster uses psychopharmacological approaches to better understand how certain neurotransmitters, like dopamine, modulate these social precision-weighting processes in healthy individuals and disorders such as Parkinson’s. She is also investigating how motor function and cultural differences affect social cognition.
