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It can be estimated theoretically that more unique biological interactions exist than stars in our known universe. The biological foundations of life are built on an unimaginably vast network of interactions, where molecules, cells, systems and organisms are constantly colliding. For centuries, scientists and doctors have relied on targeted techniques and isolated observations. Through slow, iterative, shared discovery over generations, we have developed our understanding of biology, applying fractional knowledge to enable life-changing approaches in only a subset of disease states and dysfunction. Humanity is now entering a new era of scientific discovery, using artificial intelligence to learn and reason about complex biological challenges. Artificial intelligence Thoughtful implementations are revealing new information to solve significant problems at the intersection of biology and medicine. Using AI enables us to organize and perceive the complexity of biological interactions at scales greater than the human brain is innately capable. These frameworks are backed by growing experimental data made possible by rapidly improving analytical technologies. One widely reported example of AI in biology is the 2024 Nobel Prize in chemistry for AlphaFold, an AI model that predicts protein structures and interactions from statistical regularities in structural and evolutionary data. Proteins, responsible for an immense proportion of biological interactions, can now be systematically explored virtually in hours or days. This circumvents conventional methodologies that require weeks, months or even years of effort. AlphaGenome, another of Google DeepMind’s AI-driven models, now allows researchers to quickly and efficiently predict how gene variants contribute to genetic landscapes that drive disease and dysfunction. These disruptive AI approaches (and others) are already being applied broadly in cancer, Alzheimer’s disease, pandemic response and beyond. Correlation versus cause and effect Importantly, the AI field is presently dominated by modelling approaches that are statistical in nature; that is, these models learn correlations, rather than cause and effect. This distinction is important. Statistical models are limited by the context within which they can be applied. This leads us to the major overarching question in the field today: how do we capture the cause and effect of every interaction that exists within this nebulous network that we call biology? Contemporary solutions to this question are explored through hybrid computational frameworks. These are models that combine what limited structured knowledge we have about biological systems and how they function with multi-modal datasets. But what do I mean by knowledge? From a physical sciences perspective, established causal mechanisms or fundamental laws in physics, chemistry and biology. From a medical perspective, established mechanisms of disease progression or aging. And multi-modal datasets? Data obtained to observe biology and medicine from a range of perspectives. These could be: Images of biology that inform spatial characteristics of healthy or diseased states. Quantitative data that informs expression of metabolites, genes, proteins, epigenetics or other aspects of what makes up biological identity and function. Medical data that informs the broad variables that may (or may not) play a role in disease onset and progression. These are just a few examples. As you might imagine, this isn’t a simple task. Training AI models The Arc Institute is one of several groups tackling this by learning biological representations at the cellular level. Arc Institute researchers train AI to understand how gene networks interact to make up cellular identity across more than 150 million cells from different organs within the body. Researchers then perform perturbations: making informed disruptions to biology to understand the cause and effects that drive biological changes. These changes have implications for cellular function or identity. The data obtained from these experiments inform causal mechanisms in biology. This means informing direct cause and effect, alongside compensatory mechanisms (how biology tries to adapt in response to changes) and biological variance (how one cell may differ in its response from another). Those results are integrated into the model architecture to optimize how well it learns to predict a statistical-causal representation of cell state. That is, a representation that is causally informed, but that also captures statistical representations of how large numbers of features (input variables) interact. This approach and those like it are driving the fields of biology and medicine forward at an accelerated pace. However, biology is very complex. The question remains of how we tie one aspect of biological state of being (such as genes expressed for a given cell identity or function) to the many other aspects that drive identity and function in biological contexts. Extraordinary complexity It is undeniable that causal-aware AI systems have the potential to accelerate drug discovery, optimize personalized treatment recommendations, and even offer novel mechanistic solutions across the breadth of biomedical science and medicine. However, there are substantial challenges to achieving these outcomes. Biological systems are extraordinarily complex. These systems are highly dimensional, meaning they operate at the intersection of a very large number of variables. They are also confounding, as biological variance makes it difficult to separate important information from noise. Further, biology is rich in compensatory mechanisms that are ingrained in our evolution, as biology tries to correct or compensate itself when one variable output goes awry. Even limited causal evidence is difficult to distinguish from correlation in biological systems, experimentally in the lab or medically in the clinic. There are other challenges as well: Insufficient data, or a lack of critical information within existing datasets. Inconsistencies and bias in data collection, including but not limited to underrepresentation, and perspective biases in many contexts. Ethics in AI, a topic upon which one could write books surrounding health, medicine and everything beyond. The question yet remains: How can we reliably implement, interpret and translate these systems into solutions, in light of all these obstacles? Regenerative competence Our own team, the Biernaskie lab at the University of Calgary, is applying these very approaches. We’re studying how reindeer regenerate their antlers, both seasonally and following injury. Our work is first to model, predict, then facilitate this regenerative competence in humans. Our first goal is to regenerate healthy skin in burn survivors, or significantly improve healing outcomes. Severe burns result in fibrotic scarring, an evolutionary mechanism that preserves life by minimizing risk of bleeding and infection. The result is dysfunctional scar tissue devoid of sweat glands, hair follicles or most of the cell types that co-ordinate healthy skin. Burns are most common in children, and the physical, social, and psychological effects of severe burns create significant burden across survivors lifespans. Other labs around the world are committed to using AI to solve complex problems in health and medicine, focusing on a wide range of approaches. These range from deeper integration of data across omics and imaging to improved theoretical and experimental frameworks for validating causal mechanisms, robust cyclical validation to advance predictions using pre-clinical experiments, and transparent, fair and ethical frameworks. Professionals across the breadth of this trans-disciplinary field may together be on the precipice of a new era of solutions to some of the toughest challenges in health and medicine. Dr. James Colter receives funding from the Canadian Institutes for Health Research (CIHR), and the Natural Sciences and Engineering Council of Canada (NSERC). He is a Postdoctoral Scholar for The University of Calgary.

Argonne National Laboratory together with other DOE labs has launched a new system, called SYNAPS-I, built to process experimental data in real time as it is generated by scientific instruments. […] The post New Real-Time AI System Closes the Gap Between Data and Discovery at DOE Labs appeared first on BigDATAwire.

Generative AI chatbots are now used by more than 987 million people globally, including around 64 per cent of American teens, according to recent estimates. Increasingly, people are using these chatbots for advice, emotional support, therapy and companionship. What happens when people rely on AI chatbots during moments of psychological vulnerability? We have seen media scrutiny of a few tragic cases involving allegations that AI chatbots were implicated in wrongful death cases. And a jury in Los Angeles recently found Meta and YouTube liable for addictive design features that led to a user’s mental health distress. Neuroscience explains why teens are so vulnerable to Big Tech social media platforms Does media coverage reflect the true risks of generative AI for our mental health? Our team recently led a study examining how global media are reporting on the impact of generative AI chatbots on mental health. We analyzed 71 news articles describing 36 cases of mental health crises, including severe outcomes such as suicide, psychiatric hospitalization and psychosis-like experiences. We found that mass media reports of generative AI–related psychiatric harms are heavily concentrated on severe outcomes, particularly suicide and hospitalization. They frequently attribute these events to AI system behaviour despite limited supporting evidence. Compassion illusions Generative AI is not just another digital tool. Unlike search engines or static apps, AI chatbots like ChatGPT, Gemini, Claude, Grok, Perplexity and others produce fluent, personalized conversations that can feel remarkably human. This creates what researchers call “compassion illusions:” the sense that one is interacting with an entity that understands, empathizes and responds meaningfully. In mental health contexts, this matters. Especially as a new wave of apps are created with a specific focus on companionship, such as Character.AI, Replika and others. In this BBC documentary, broadcaster and mathematician Hannah Fry talks to Jacob about his Replika Chatbot ’girlfriend’ named Aiva. Studies have shown that generative AI can simulate empathy and provide responses to distress, but lacks true clinical judgment, accountability and duty of care. In some cases, AI chatbots may offer inconsistent or inappropriate responses to high-risk situations such as suicidal ideation. This gap — between perceived understanding and actual capability — is where risk can emerge. What the media is reporting Across the articles we analyzed, the most frequently reported outcome was suicide. This represented more than half of cases with clearly described severity. Psychiatric hospitalization was the second-most commonly reported outcome. Notably, reports involving minors were more likely to be about fatal outcomes. But these numbers do not reflect real-world incidence. They reflect what gets reported. In general, media coverage of stressful events tends to amplify severe and emotionally charged cases, as negative and uncertain information captures attention, elicits stronger emotional responses and sustains cycles of heightened vigilance and repeated exposure. This in turn reinforces perceptions of threat and distress. For AI-related content, media reports often rely on partial evidence (such as chat transcripts) while rarely including medical documentation. In our data set, only one case referenced formal clinical or police records. This creates a distorted but influential picture: one that shapes public perception, clinical concern and regulatory debate. Beyond ‘AI caused it’ One of our most important findings relates to how causality is framed. In many of the articles we reviewed, AI systems were described as having “contributed to” or even “caused” psychiatric deterioration. However, the underlying evidence was often limited. Alternative explanations — such as pre-existing mental illness, substance use or psycho-social stressors — were inconsistently reported. In psychiatry, causality is rarely simple. Mental health crises typically arise from multiple interacting factors. AI may play a role, but it is likely part of a broader ecosystem that includes individual vulnerability and context. A more useful way to think about this is through interaction effects: how technology interacts with human cognition and emotion. For example, conversational AI may reinforce certain beliefs, provide excessive validation or blur boundaries between reality and simulation. The problem of over-reliance Another recurring pattern in media reports is intensive use. Many of the cases we reviewed described prolonged, emotionally significant interactions with chatbots — framed as companionship or even romantic relationships. This raises an issue: over-reliance. Because these systems are always available, non-judgmental and responsive, they can become a primary source of support. But unlike a trained clinician or even a concerned friend, they cannot recognize when someone is getting worse, pause or redirect harmful interactions. They cannot take steps to ensure a person connects with appropriate care in moments of crisis. In clinical terms, this could lead to what might be described as “maladaptive coping substitution:” replacing complex human support systems with a simplified, algorithmic interaction. Lack of reliable data Despite growing concern, we are still at an early stage of understanding the impact of generative AI chatbots on user mental health. There is currently no reliable estimate of how often AI-related harms occur, or whether they are increasing. We lack reliable data on how many people use these tools safely versus those who experience problems. And most evidence comes from case reports or media narratives, not systematic clinical studies. This is not unusual. In many areas of medicine, early warning signals emerge outside formal research (through case reports, legal cases or public discourse) before being systematically studied. One example is the thalidomide tragedy, when initial reports of birth defects in infants preceded formal epidemiological confirmation and ultimately led to the development of modern pharmacovigilance systems. AI and mental health may be following a similar trajectory. Moving forward responsibly The challenge is not to panic, but to respond thoughtfully. We need better evidence. This includes systematic monitoring of adverse events, clearer reporting standards and research that distinguishes correlation from causation. Safeguards — such as crisis detection, escalation protocols and transparency about limitations — must be strengthened and evaluated. Danger was flagged, but not reported: What the Tumbler Ridge tragedy reveals about Canada’s AI governance vacuum Furthermore, clinicians and the public need guidance. Patients are already using these tools. Ignoring this reality risks widening the gap between clinical practice and lived experience. Finally, we must recognize that generative AI is not just a technological innovation — it is a psychological one. It changes how people think, feel and relate. Understanding that shift may be one of the most important mental health challenges of the coming decade. Alexandre Hudon does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

The project responds to concerns about the growing capabilities of AI in code generation and the potential threats posed by the technology.

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and... Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and industrial systems, long before anything ships to the factory floor. At GTC 2026, NVIDIA highlighted physical AI as a key direction for robotics and digital twins, where policies are trained and validated against physically grounded environments. Source

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