“News Items is the most valuable newsletter out there. — Peggy Noonan.
1. Quantamagazine:
(A)fter over a century of studying introspection in humans and animals, researchers are beginning to explore the phenomenon in unlikely new subjects — large language models. These systems, which are based on mathematical structures called neural networks, excel at cognitive tasks that until recently seemed exclusive to humans. Can they introspect as well?
In 2025, Jack Lindsey, a researcher at Anthropic, devised a way to test introspection in language models by injecting “thoughts” into their neural networks — in a kind of targeted digital neurosurgery. Lindsey’s experiment demonstrated that models could at least sometimes detect and describe these injections. It looked a lot like introspection.
Lindsey’s paper was deliberately limited in scope — it didn’t make broader claims about consciousness. (In particular, it did not imply that language models have subjective first-person experiences). Even so, it caused a stir online. Researchers rushed to replicate Lindsey’s study with other models.
In the year since, a clearer picture has emerged. Some aspects of the phenomenon Lindsey observed have simpler explanations unrelated to introspection. Others have proved harder to explain away, but whether they should count as introspection remains hotly contested. Read the rest. (Source: quantamagazine.org et alia)
2. Google DeepMind:
Today, we are announcing AlphaGenome 2, our next-generation model that predicts how DNA sequences and their variations impact specific human cell types, giving scientists a powerful new tool to uncover the cellular drivers of disease.
Cells are the building blocks of life. Every cell in our body carries the same DNA blueprint, yet each cell type and state—like a heart muscle cell or an immune cell fighting an infection—switches different genes on or off to do its job. Global efforts like the Human Cell Atlas are mapping this gene expression for millions of cells, but it is still an unsolved challenge to predict how subtle DNA variations affect this expression in cells, which is a key driver of human traits and disease.
Last year, we launched AlphaGenome to predict how DNA variants affect molecular activity across broad tissues. We expanded its reach with AlphaGenome Atlas, a platform containing predictions for all 9 billion possible single-letter changes in the human genome. These tools help researchers prioritize and interpret variant effects, driving discoveries in rare diseases, complex traits, and fundamental biology.
By zooming in from broad tissue averages to individual cell types and cell states, and redesigning the model’s architecture, AlphaGenome 2 represents a step-change improvement in variant predictive performance. Importantly, it allows researchers to predict variant effects in disease-relevant cells that would be missed in broad-tissue models, as well as predictions for cell and disease states that the model hasn’t seen before.
AlphaGenome 2 identifies nearly twice as many variants that change gene expression in cells, compared to our previous state-of-the-art model. Trained on more than 100 million individual cell profiles across 68 tissues, the model turns static cell atlases into a predictive engine for human genetics. AlphaGenome 2 is available today for non-commercial research via the AlphaGenome API, and in private preview for commercial research. (Sources: deepmind.google, storage.google)
3. OpenAI’s progress on another million-dollar problem is finally public. Experts appointed by the company to vet the work say they’ve now verified its artificial-intelligence-generated proof of the “existence” of the quantum Yang-Mills theory—a vital foundation for the Standard Model of particle physics. “We update the progress of our committee in shepherding the LLM [large language model] produced result,” the group said in a statement on November 9 previewing its findings. The team’s work confirms that OpenAI’s result “proves the existence of pure Yang-Mills theory according to the problem statements which are standard in physics and mathematics.” (Sources: scientificamerican.com, kitp.ucsb.edu)
4. Increasingly, researchers are embracing what was once considered an intriguing but relatively fringe idea: that speech and how it changes over time can help predict and track cognitive decline. Technological advances are making it possible to collect and analyze many aspects of speech, including how fast people talk, how often they pause, the complexity and variety of their vocabulary, their pitch and cadence and the coordination of their vocal and facial muscles. “It’s really a window into brain health,” said Dr. Marilu Gorno Tempini, director of the language neurobiology laboratory at the University of California, San Francisco. “The way we can analyze speech and language now,” she said, allows insight into “detailed brain function and cognition that was unheard of before.” (Source: nytimes.com. One of the leaders in this field is Carnegie Mellon’s Rita Singh. Joe Klein and I did a podcast with her five years ago. She is amazing.)
