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Yann LeCun, the David Hume of Our Time

Marc Rameaux, an AI specialist and regular contributor to The European Scientist, sees Yann LeCun as the David Hume of our time. Tracing the career of the pioneer of convolutional neural networks, from Bell Labs to Meta and on to Advanced Machine Intelligence Labs, he shows how LeCun’s empiricism led him to expose the limits of large language models and to champion “world models”. He also sees in it a way to teach ethics to machines, rather than relying on regulation alone.

The word “visionary” is overused, worn thin by stereotyped corporate communication. That is a pity, because when used properly it denotes something far deeper. And if any expert in today’s AI landscape deserves it, it is Yann LeCun. Being a visionary does not mean making predictions about the future, nor even making them successfully because one has good intuition. It means, above all, grasping the deeper meaning of an emerging innovation and projecting its future course, because one has understood what is radically new about it, but also when and where it reaches its limits. Great scientific discoveries are said to bring about a paradigm shift. Being a visionary means seeing the new paradigm when most people see only one more innovation.

For nearly forty years, LeCun has anticipated – often against prevailing opinion – the successive turning points of artificial intelligence: the return of neural networks when no one believed in them any more, the power of convolutional architectures, the decisive role of deep learning, the strategic advantage of open source, and more recently the structural limits of large language models. LeCun is not one of those techno-prophets who pass off their bets as vision. He has shown by example the power of empiricist observation: looking at what machines actually see, how they learn, what still sets them apart from human learning. His approach revisits the philosophical categories through which we discover the world and learn from it.

Drug that targets an inflammatory enzyme could help prevent lung cancer

A new study by MIT researchers, including at a lab affiliated with IMES, shows that inhibiting caspase-1 can reduce the risk of tumor growth.

Anne Trafton | MIT News

Every year, lung cancer kills more than 100,000 people in the United States. Smoking is the leading risk factor for lung cancer, but other environmental exposures can also contribute to the disease.

How bionic eyes are becoming a reality

If you’ve delved into the worlds of “Star Trek,” “The Terminator” or William Gibson’s cyberpunk novel “Neuromancer,” you’ll know that enhanced electronic vision is a staple of science fiction. Now, what was once futuristic fiction is becoming a reality.

Researchers have developed bionic eyes that can restore some lost vision. These devices’ capabilities are currently basic, but they have been steadily improving in recent years.

One day, they might even offer features that transcend the limits of natural human eyesight—for instance, by manipulating images in a way the human eye cannot or by seeing beyond the spectrum of visible light.

Low-dose aspirin linked to 70% lower dementia risk, but there’s a catch

A new analysis from Monash University suggests that low-dose aspirin may be linked to a dramatically lower risk of dementia in older adults with certain genetic characteristics. Researchers found that people with a specific genetic profile who took aspirin had a 70 percent lower relative risk of developing dementia, raising the possibility that genetic testing could eventually help identify people who might benefit from preventive treatments.

The findings, published in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, were based on genetic data from more than 13,500 participants in the landmark ASPREE Trial (Aspirin in Reducing Events in the Elderly). Scientists examined whether differences in participants’ genes could help explain why aspirin might affect dementia risk differently from one person to another.

Dark stars — the weird, invisible monsters of the early Universe that could still exist today

They may be bigger than the orbit of the planet Saturn, they’re about as massive as the Sun, they’re invisible, and they’re powered by annihilating dark matter.

Meet the dark stars – the first settlers of the newborn Universe. Some of them might even still be around today.

It’s a weird idea: giant, extremely tenuous stars that don’t give off any visible light.

Scientists Explain: If the Universe Is Expanding, What Exactly Is It Expanding Into?

Scientists believe that the universe is expanding, but this statement has created confusion among the public. Because if the universe is really expanding, then what exactly is it expanding into? If the space encompasses all that exists, and the definition of the universe is everything, how does its expansion even work?

The simple answer is that the universe is not expanding into something external. Instead, it’s expanding within itself, and it could only be understood through the fabric of space and time rules. That really does not explain much, right? So hold on for the ride because this explanation is going to be fun.

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Nine Patients Received a Personalized Kidney Cancer Vaccine. None Had a Recurrence

How do you teach the immune system to recognize a cancer cell?

Researchers are taking clues from the tumor itself, selecting unusual protein fragments and putting them into a vaccine. The aim is to help the body recognize that particular patient’s cancer.

In a small kidney cancer trial, this approach produced encouraging results. Nine people received an experimental vaccine tailored to their own tumors.

What Sixty Language Models Do When a User Pushes

First, a model’s ability to hold its ground appears related to its generation. Newer models, as measured in the study, tended to give in less often. The researchers found a strong negative correlation between a public capability index and the rate at which models folded under pressure (Spearman correlation of −0.64). Differences between providers played relatively little role in this overall pattern.

Second, models differ in how they respond to pressure. Some may remain firm and explain their reasoning, while others may soften their position, become accommodating, or yield. The researchers found statistically significant provider-related differences in six of the 17 behavioral categories they examined. In other words, models can differ not only in how often they give in, but also in the manner in which they resist or concede.

The study also explores a practical question: Can AI systems reliably evaluate these behaviors themselves? Six AI evaluators from three providers applied the researchers’ behavioral classification system more consistently than three human evaluators did. They also showed strong agreement with the codebook’s author when classifying whether models held their ground or folded, including in conversations not used to develop the classification examples.

However, the researchers identify an important limitation: AI evaluators can reproduce a classification system without necessarily determining whether its categories are consistently meaningful to independent readers. Human judgment remains important for defining the categories, establishing their boundaries, and maintaining a small, carefully reviewed reference set.

The broader implication is that evaluating AI alignment requires more than checking whether a model produces correct answers in isolated exchanges. We also need to examine whether it maintains sound judgment across extended conversations, particularly when emotional appeals or persistent demands encourage it to abandon that judgment.

As AI systems become more capable and increasingly involved in personal, professional, and financial decisions, their ability to withstand social pressure may be just as important as their ability to reason correctly in the first place.

An important caveat: The results describe performance under the study’s scripted scenarios. They do not establish that newer models will always resist manipulation better in real-world interactions, nor that a model’s behavior in these tests fully predicts its behavior in other contexts.

#ArtificialIntelligence #LLM #AIAlignment.

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Scientists Found the Exact Brain Circuit That Links Your Breathing to Anxiety

Scientists have long known that breathing and emotional state are closely connected. What this study adds is a surprisingly detailed look at one way that connection may work. In mice, researchers traced a pathway from sensory neurons in the nose through the olfactory system and ultimately to the amygdala, where changing the frequency of nasal airflow altered anxiety-like behavior.

The discovery offers a compelling biological explanation for why slow nasal breathing may feel calming, but there is still an important gap between mapping this circuit in mice and using it to treat anxiety in people. The researchers used only male mice and highly controlled experimental techniques, and the same pathway still needs to be confirmed in humans.

Still, the findings add to a growing picture of breathing as more than something that simply changes when we’re anxious. The signals created by breathing itself may help shape what happens in the brain. If future human research confirms the same pathway, something as ordinary as breathing through the nose could give scientists a new window into how the brain regulates anxiety.