🧬 Life Uses 4 DNA Letters. Scientists Just Made 8 Work.

Every known organism on Earth writes its genetic instructions using the same four DNA letters: A, T, C and G.

Scientists have now shown that one of biology’s most fundamental molecular machines can read an alphabet containing eight.

Researchers tested E. coli RNA polymerase — the enzyme that reads DNA and turns its information into RNA — with synthetic DNA containing four additional chemical letters known as P, Z, B and S. Remarkably, the enzyme successfully recognized and transcribed the artificial base pairs using much of the same molecular machinery it employs for natural DNA.

Using cryo-electron microscopy at resolutions down to about 2.4 ångströms, the team could watch how the synthetic letters fit inside the polymerase. The artificial pairs adopted almost the same geometry as ordinary Watson–Crick DNA pairs, allowing the enzyme’s catalytic machinery to close around them and continue transcription. Researchers also engineered a modified version of one synthetic letter to reduce copying errors.

The implications are potentially enormous. A larger genetic alphabet could eventually produce RNA molecules with chemical capabilities unavailable to natural biology and might help scientists design new diagnostics, drugs and engineered biological systems. Expanded genetic alphabets have already been used experimentally to create molecules that recognize cancer cells.

But there is an important boundary: scientists have not created an eight-letter living organism here. The experiment demonstrates transcription by bacterial RNA polymerase; reliably replicating a full eight-letter genome and translating that expanded information into proteins inside living cells remain much harder problems.

For four billion years, life on Earth has been writing with four letters.

Apparently, biology’s machinery can read a bigger alphabet than evolution ever gave it.

#Genetics #DNA #SyntheticBiology #Biotechnology #RNA #Science

Primary paper — Nature Communications⁠

☀️ AI Just Learned to Control Fusion Plasma Faster Than Humans Can React

Inside a fusion reactor, plasma can become unstable in just a few milliseconds.

That is a problem when even a highly focused human operator reacts on the scale of seconds.

Researchers from Princeton University and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory have now tested an AI control framework called PACMAN on the real DIII-D tokamak in California. The system continuously reads temperatures, densities and magnetic signals, runs multiple machine-learning models, resolves their commands and sends instructions back to the machine — with a complete control cycle typically taking about 20 milliseconds.

In one of five live experiments, PACMAN predicted a dangerous tearing-mode instability about 200 milliseconds before it appeared. Instead of trying to suppress the instability after it had already formed, the controller changed the plasma early enough to prevent it. In other tests, the system controlled plasma heating, density and rotation, detected energetic-particle waves, and simultaneously optimized all six of DIII-D’s microwave heating systems.

This does not mean AI has solved fusion. DIII-D is an experimental tokamak, not a commercial power plant, and researchers still set the goals and safety limits. The important step is that machine-learning models are now fast enough to participate directly in the millisecond-by-millisecond control of a real fusion plasma rather than merely analyzing experiments afterward.

Fusion has always had a control problem: the plasma changes faster than humans can think.

Apparently, that is exactly the sort of problem AI likes.

#Fusion #AI #Physics #Tokamak #Energy #MachineLearning #Science

https://www.pppl.gov/news/2026/pacman-ai-framework-controlling-fusion-systems-safely-makes-key-decisions-milliseconds

🥊 This Is the Beginning of the End: Unitree Taught Robots to Fight Autonomously

The company unveiled UnifoLM-X2-1.0 — a world model that lets a robot decide in real time how to move, dodge and attack. No operator, no pre-scripted motions — it does it all on its own.

⚡️ The breakthrough in a nutshell:
Unitree calls this the first fully autonomous humanoid fight driven by a world model. The robot doesn't follow scripted punches — it builds a model of what's happening and makes decisions on the fly.

🔬 Key findings:
• The UnifoLM-X2-1.0 world model predicts the consequences of movements and plans actions in real time.
• The footage shows both actual recording and predictive modeling — the system "plays out" possible futures before acting.
• The robot dodges, attacks and keeps distance with no human in the loop.

💼 Why it matters:
This is a step from programmed motions to autonomous decision-making in a dynamic environment. The technology that teaches a robot to fight will tomorrow help in rescue, logistics and work in hazardous conditions.

It won't be funny for long 🪖

#Unitree #Robots #AI #Humanoids #Science

@science

🤖 Robots Are Now Building Robots

China’s XPeng has switched on a new production line for its humanoid robot IRON — and more than 80% of the line’s core manufacturing processes are automated.

The first production-line IRON completed assembly and then walked off the line by itself.

XPeng describes the facility as the world’s first automated production line for advanced general-purpose humanoid robots. The important nuance: this is not yet a completely human-free “self-replicating robot factory.” But it is a serious step from handcrafted prototypes toward industrial-scale humanoid production.

And IRON is not exactly a conventional industrial robot.

Its body has human-like proportions, flexible skin, highly articulated hands and movements realistic enough that, during XPeng’s 2025 AI Day, some viewers suspected there might actually be a person inside. CEO He Xiaopeng responded in the most convincing possible way: he cut open the robot’s leg on stage to reveal the machinery underneath.

XPeng plans to begin mass production before the end of 2026, with commercial deliveries expected in China and overseas in 2027.

For decades, factories used robots to manufacture cars.

Now a car company has built a factory where robots manufacture humanoid robots.

The recursion has officially begun.

#Robotics #AI #XPeng #HumanoidRobots #China #PhysicalAI #Technology

https://www.xpeng.com/news/01a080371029a057bc8e8a02a2c6012b

🧬 Google DeepMind Just Precomputed 9 Billion Possible Human DNA Mutations

This may be one of DeepMind’s most ambitious biology releases since AlphaFold.

AlphaGenome Atlas contains AI predictions for the molecular effects of essentially every possible single-letter substitution in the human genome — around 9 billion variants.

The resulting dataset is about 1 petabyte, more than 30 times larger than the AlphaFold Database.

Why does this matter?

Only around 2% of our genome directly encodes proteins. Much of the remaining 98% regulates when, where and how strongly genes are switched on — and contains huge numbers of variants associated with human traits and disease.

AlphaGenome predicts how mutations may alter processes including gene expression, RNA splicing, chromatin accessibility and regulatory activity. DeepMind then combines these predictions with AlphaMissense into a single AlphaGenome Variant Impact — AVI — score, allowing researchers to rapidly rank variants across both coding and non-coding DNA.

In an analysis of whole-genome data from more than 54,000 UK Biobank participants, the approach uncovered 22% more associations involving rare non-coding variants that had previously been buried in statistical noise.

And there is another important shift happening alongside it.

DeepMind has released Science Skills — an open collection of agent tools connecting AI workflows to resources including AlphaGenome, AlphaFold DB, UniProt, ClinVar and dozens of other scientific databases.

This does not turn an AI agent into a doctor or make consumer DNA tests clinically diagnostic.

But it does move genomics toward something fundamentally new:

A human genome is becoming a dataset an AI agent can systematically interrogate, prioritize and explain.

We sequenced the human genome 25 years ago.

Now we are starting to make it searchable.

#AlphaGenome #DeepMind #Genetics #AI #Bioinformatics #Biotechnology #Science

Atlas:
https://alphagenome.google/atlas