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Brain-computer interface enables independent, accurate communication for man living with ALS

A new study demonstrates that a person with severe paralysis caused by amyotrophic lateral sclerosis (ALS) can use a brain-computer interface (BCI) at home to communicate, work and interact with the digital world—without the need for researcher support. Published in Nature Medicine, the results mark a significant step toward delivering practical assistive technology for people with severe speech and motor impairments.

The BCI system was developed at UC Davis, in collaboration with colleagues at Brown University and Mass General Brigham Neuroscience Institute. It is equipped with advanced decoding algorithms that translate neural signals into text (speech BCI) and enable cursor control (movement BCI). It allows full interaction with a personal computer.

The brain-computer interface is designed to restore communication and computer control by decoding neural activity linked to attempted speech and movement. Although recent advances have achieved high accuracy in research settings, real-world adoption has been limited by two key challenges: independent at-home use and reliable long-term performance.

Alonzo Church

His revolutionary idea? Before “computer science” was even a field, Church invented the lambda calculus (λ-calculus)—an elegant, abstract system for expressing computation through pure mathematical functions. In 1936, he used it to prove that no universal algorithm could ever decide the truth of all mathematical statements, solving Hilbert’s famous Entscheidungsproblem in the negative. This became known as Church’s Theorem, and it revealed something profound: there are hard limits to what any machine can compute.

That same year, Church articulated what we now call the Church–Turing thesis: any problem that can be “effectively calculated” can be computed by a Turing machine—or equivalently, expressed in lambda calculus. When Alan Turing learned of Church’s work, he traveled to Princeton to study under him. Together, they proved their two seemingly different models of computation were fundamentally equivalent, laying the bedrock for all future computer science.

Alonzo Church was born on June 14, 1903, in Washington, D.C., where his father, Samuel Robbins Church, was a justice of the peace [ 5 ] and the judge of the Municipal Court for the District of Columbia. He was the grandson of Alonzo Webster Church (1829−1909), United States Senate Librarian from 1881 to 1901, and great-grandson of Alonzo Church, a professor of Mathematics and Astronomy and 6th President of the University of Georgia. [ 6 ] As a young boy, Church was partially blinded by an air gun accident. [ 7 ] The family later moved to Virginia after his father lost his position at the university because of failing eyesight. With help from his uncle, also named Alonzo Church, the son attended the private Ridgefield School for Boys in Ridgefield, Connecticut. [ 8 ] After graduating from Ridgefield in 1920, Church attended Princeton University, where he was an exceptional student. He published his first paper on Lorentz transformations [ 9 ] in 1924 and graduated the same year with a degree in mathematics. He stayed at Princeton for graduate work, earning a Ph. D. in mathematics in three years under Oswald Veblen.

He married Mary Julia Kuczinski in 1925. The couple had three children: Alonzo Jr. (1929), Mary Ann (1933), and Mildred (1938).

After receiving his Ph.D., he taught briefly as an instructor at the University of Chicago. [ 10 ] He received a two-year National Research Fellowship that enabled him to attend Harvard University in 1927–1928, and the University of Göttingen and University of Amsterdam the following year.

Simplifying complex ideas in sketches

What would you see if you tried to travel alongside a light wave at the speed of light? And suppose you held a mirror in front of you as you zipped along. What would you see in the mirror? This and similar thought experiments were posed by the young Albert Einstein to himself in his teens. It’s come to be known as Einstein’s Mirror and is also the title of a popular book on relativity. It would at first seem that light, reflected off your face, could never reach the mirror to, in turn, reflect back into your eyes to see it. So what would you see? It was only years later that Einstein developed a theory that answered this puzzle. And it required some fundamental adjustments to how we understood the world, which still bend my mind to think about them. These include: You can’t travel at the speed of light. Time is not fixed; it is relative. The speed of light is a universal constant—it is the same, independent of the motion of the source. Einstein wrote: “After ten years of reflection, such a principle resulted from a paradox upon which I had already hit at the age of sixteen: If I pursue a beam of light with the velocity c [the velocity of light in a vacuum], I should observe such a beam of light as a spatially oscillatory electromagnetic field at rest. However, there seems to be no such thing…” — Autobiographical notes, 1949 I’ll try to explain a little as I understand it. Our usual experience is that velocities are additive. Suppose I am on a moving train carriage and I throw a ball from the back of the carriage to the front. For an observer outside the train, that ball moves at the speed of the train plus the speed of the ball relative to me. But light behaves differently. As you approach the speed of light, the energy required to keep accelerating approaches infinity. In effect, you can’t reach the speed of light. So an observer of a flying Einstein wouldn’t see light travelling from him to the mirror at twice the speed of light. What changes is time. For the high-speed Einstein, the light would appear to travel away from him to the mirror and back at its usual immense speed. However, for an observer, what would only seem a moment for the high-speed Einstein might take years for the rest of us—the experience of time changes with velocity. It’s a remarkable turn for a simple and fascinating question. It’s amazing to me that the young Einstein would both pose this question, continue work on it, and then think to question some of the most self-evident facts of our world as we experience it: that time is not fixed, that a speed cannot be reached, and of course, ultimately, that energy is matter. The book Einstein’s Mirror is co-authored by my Dad (respect!). It’s full of photographs, fascinating stories, and the characters that moved physics forward. It includes the people, events and science central to another of Christopher Nolan’s films, Oppenheimer. Perhaps Christopher read it 🤔 Related Ideas to Einstein’s Mirror Also see: Laplace’s Demon Redshift Looking back in time The Doppler Effect Sonic Boom The most beautiful equation — Earlier this year, we attended a showing of Christopher Nolan’s Interstellar at the Royal Albert Hall in London with Hans Zimmer’s soundtrack played by a live orchestra. It was a fantastic way to experience a remarkable film—a film that manages to make black holes, wormholes, and time slippage both understandable (largely) and part of the plot. It strikes me as an astonishing achievement for a mainstream film.

The AI tools shaping patient care may be operating outside regulatory oversight. MIT researchers say it’s time to change that

Every day, across thousands of American hospitals, artificial intelligence quietly shapes decisions that determine patient outcomes. An algorithm flags a patient as high risk for sepsis; a risk score informs whether a woman receives additional cancer screening; a deterioration model triggers an alert that sends a care team to a bedside. These tools are embedded in the workflows of nearly two-thirds of US hospitals, integrated into the electronic health record systems clinicians rely on daily. But many have never been reviewed by the FDA.

A new viewpoint in The Lancet Digital Health, co-authored by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic, traces how this problem took root, why it carries serious consequences, and what genuine transparency would require to fix it.

The argument, the scientists say, is not that AI has no place in clinical decision-making. It is that a $4 billion market of clinical decision support tools operates largely beyond public accountability, leaving patients and providers often unable to know whether the tools influencing their care have been validated, by whom, or for which populations they work as intended.

Recursive Self Improvement

Computer, load up celery man.
Can AI build AI? Yes, and it already is. Sort of. I showcase the ability of AI agents like claude code to perform AI research, to build and optimize machine learning algorithms. I put various state-of-the-art LLMs like claude Mythos/Fable into an endless recursive research loop and have them build a neural network that learns the shape of the mandelbrot set. It is inspired by Andrej Karpathy’s autoresearch. While we watch this loop, I express my thoughts on the concept of recursive self improvement, arguing that it is possible, hard, and dangerous.
Sorry for the bitrate issues.

Fractalsearch repo: coming soon!

~SUPPORT ME~
Learn to code faster with Scrimba! (saves you 20% and support me): https://scrimba.com/?via=EmergentGarden.
Patreon: / emergentgarden.
Twitter: / max_romana.
Bluesky: https://bsky.app/profile/emergentgard… Autoreasearch: https://github.com/karpathy/autoresearch Mandelbrot Zoom: • Mandelbrot World Record Attempt — Part 1 (… Celery Man: • Tim and Eric — Celery Man Karpathy’s Youtube: / @andrejkarpathy Self-building Cranes: • How Tower Cranes Build Themselves Darwin-Godel Machine: https://arxiv.org/abs/2505.22954 Hashgrid Paper: https://arxiv.org/abs/2201.05989 Anthropic’s RSI Article: https://www.anthropic.com/institute/r… Fable System Card: https://www-cdn.anthropic.com/d00db56… My Music Guy: / @acolyte-compositions “Equatorial Complex” Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/b… ~TIMESTAMPS~ (0:00) Recursive Self Improvement (3:14) fractalsearch (9:56) RSI is Possible (15:03) RSI is Hard (21:52) RSI is Dangerous (26:03) Results (28:28) Cost (29:29) Takeoff.

~SOURCES~
Autoreasearch: https://github.com/karpathy/autoresearch.
Mandelbrot Zoom: • Mandelbrot World Record Attempt — Part 1 (…
Celery Man: • Tim and Eric — Celery Man.
Karpathy’s Youtube: / @andrejkarpathy.
Self-building Cranes: • How Tower Cranes Build Themselves.
Darwin-Godel Machine: https://arxiv.org/abs/2505.22954
Hashgrid Paper: https://arxiv.org/abs/2201.05989
Anthropic’s RSI Article: https://www.anthropic.com/institute/r…
Fable System Card: https://www-cdn.anthropic.com/d00db56…

My Music Guy: / @acolyte-compositions.

E= mc^2

Einstein’s famous equation has grown into one of the great symbols of the 20th century. It is the one equation in science that people recognize, if any is. It has a kind of iconic status and dual connotations: the brilliance and insight of Einstein and the darkness of atomic bombs. Images.

The basic idea behind the formula E=mc2 is easy to state. Mass and energy are really just the same thing. At first that seems impossible.

• Mass is a measure of the quantity of stuff and manifests as a resistance to acceleration. A body with little mass, like a pebble, is easy to set in motion.

Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice.

Scientist creates ‘mini‑universe’ to measure time without a clock

A University of Birmingham scientist has built a “mini-universe” that takes a step toward answering one of science’s biggest questions: “What is time?” Publishing his findings in Physical Review Research, Professor Giovanni Barontini shows how it is possible to measure the flow of time without using a clock at all. The new findings provide a scientific model in which a version of time emerges from the experiment itself.

Some theories of physics, such as the Wheeler–DeWitt equation, suggest that, at its deepest level, the universe has no built-in time but exists as a single, unchanging quantum state in which particles exhibit both wave-like and particle-like properties. It treats the universe as a whole with no external clock, and any sense of time must emerge from internal relationships between parts.

What can a neuron compute

They weren’t just tuning the strength of the incoming signals (the synapses); they were actually training the neuron on *where* those signals should land on its branchy “tree” to get the best results.

Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA-and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.

The authors have declared no competing interest.

ONR, N00014-24–1-2055, N00014-23–1-2051

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