In principle, once cameras everywhere capture enough high-resolution, overlapping views, ordinary photographs may start to feel obsolete.

Instead of saving a single flat image, those recordings could be used to reconstruct a navigable 3D representation of a scene — estimating the position, shape, depth, texture, and appearance of objects from multiple camera angles.

You could then return to a particular moment, move the virtual camera to almost any viewpoint, and generate a new image from that angle. Parts of the scene that were never directly visible to any camera wouldn’t be true recordings — AI would have to infer and reconstruct them from the surrounding visual information.

Author: joergkahlhoefer

#gaussian #splat #3D

🧬 An AI Just Made a Biological Discovery

Not summarized a paper.
Not predicted a protein structure.

Found something in nature that scientists apparently hadn’t noticed before.

Researchers gave Claude AI agents a relatively broad task: search enormous DNA databases for unusual reverse transcriptases — enzymes that copy RNA into DNA.

Then they mostly stepped aside.

About 950 AI agents spent 21 hours analyzing more than 200,000 enzymes, narrowing them to 3,500 candidate systems and eventually 20 especially interesting ones.

One agent noticed something strange.

Next to a reverse transcriptase gene in a bacteriophage — a virus that infects bacteria — it found a long, organized array of repeating DNA sequences.

The pattern looked oddly familiar.

It resembled the repeat architecture of CRISPR.

Claude investigated the sequence, measured the repeats, compared them with known systems and searched the scientific literature. It concluded that the combination appeared to represent a previously uncharacterized biological system.

Human scientists then took over in the laboratory.

Their initial experiments supported a key prediction: the mysterious DNA array is actually expressed into multiple short RNA molecules.

The researchers named the system ART — array-associated reverse transcriptases.

And this is where the story gets interesting.

CRISPR also contains arrays that generate short RNAs, which ultimately help make the system programmable. Several other recently discovered molecular systems with similar combinations of features can cut, copy or insert genetic material.

But an important warning: nobody yet knows what ART actually does.

This is an early preprint, not a peer-reviewed discovery, and there is currently no evidence that ART is a new gene-editing system. Experiments are still underway to determine its biological function.

The bigger story may therefore be Claude itself.

For decades, biological discovery depended partly on humans noticing something strange hidden inside enormous datasets.

Now we may have machines capable of doing the noticing.

What happens when thousands — or millions — of AI scientists start searching nature simultaneously?

#AI #Biology #CRISPR #Genetics #Biotechnology #Claude #Science

https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

🪐 Astronomers Just Watched a Planet Being Built

We know surprisingly well how planets should form.

The problem is that the crucial parts of the process happen hundreds of light-years away, on scales so tiny that astronomers have mostly had to reconstruct them from simulations and indirect clues.

Now they have caught the process in action.

Using Atacama Large Millimeter/submillimeter Array, astronomers imaged gas moving around WISPIT 2b, a newborn planet about 430 light-years from Earth.

It is a monster in the making: roughly five times the mass of Jupiter, orbiting inside the disk of gas and dust from which its planetary system is still emerging.

And around the planet, the gas is doing something remarkable.

On one side of WISPIT 2b it is moving toward us; on the other, away from us.

Together, those motions reveal a swirl of gas around the growing planet — exactly the kind of interaction predicted by simulations of planet formation, but never directly observed around a known protoplanet before.

The system is unusually valuable because astronomers can now see essentially every major piece of the process at once:

the enormous protoplanetary disk, the planet itself, hydrogen emission showing that WISPIT 2b is still accreting material, the gap it has carved through the disk — and now the surrounding gas responding directly to the planet.

A second young planet, WISPIT 2c, is also reshaping the system by carving out a larger cavity. And at the center, astronomers recently discovered that there isn’t even one star.

There are two.

The scale of the observation is extraordinary.

At WISPIT 2’s distance, resolving a structure the size of Earth’s orbit around the Sun is roughly equivalent to reading a normal book from five kilometers away.

For decades, simulations have shown us beautiful animations of planets growing inside swirling disks.

Now nature has finally provided the footage.

We are beginning to watch solar systems assemble in real time.

#Space #Astronomy #Exoplanets #PlanetFormation #ALMA #WISPIT2 #Science

https://www.mpg.de/26990768/astronomers-produce-the-first-complete-picture-of-gas-planet-formation-in-action

🩻 AI that reads a CT scan in 3D—and explains its findings

NVIDIA, the NIH’s National Cancer Institute, and the University of Zurich have released NV-Reason-CT, an open model that analyzes full 3D CT volumes and generates reports with explanations.

⚙️ How it works
The model pairs the Qwen3.5-4B language model with Primus, a 3D visual encoder. Each scan becomes 13,824 visual tokens, passed to the language model without further compression. Three-dimensional positional encoding preserves spatial information, helping it distinguish, for example, a finding in the right kidney from one in the left.

📚 How it was trained
Training used 550,000 examples from 70,111 CT volumes. Supervised fine-tuning on radiologists’ analyses was followed by reinforcement learning, with rewards for correctly identifying abnormalities and following the required report structure.

📊 What the results show
On CT-RATE, NV-Reason-CT achieved an average precision of 0.614 across 18 abnormality categories, compared with 0.581 for VoxelFM and **0.398 for CT-CLIP**—without a separate classification head.

In a pilot study with radiologists, scan review and reporting time fell from 26.25 to 13.13 minutes: roughly half.

🧩 Part of a broader medical AI toolkit
NVIDIA’s open medical model family also includes:
• NV-Generate-CTMR — generates synthetic 3D CT and MRI volumes.
• NV-Segment-CTMR — segments organs and lesions.
• NV-Reason-CXR — analyzes chest X-rays.
• NV-Reason-CT — analyzes full 3D CT scans.

🔓 Weights and code are available under OpenMDW-1.1, alongside fine-tuning and reinforcement-learning examples and a web demo.

Promising early results for AI-assisted radiology—with the time savings demonstrated so far in a pilot study.

@science

⚛️ Scientists Watched Matter “Appear” Inside a Quantum Computer

Pull two quarks apart and something deeply strange happens.

You never actually get two isolated quarks.

Instead, the energy binding them grows — almost as if an invisible string were being stretched between them. Eventually, storing more energy in that string becomes so expensive that nature takes another option:

it creates a new particle–antiparticle pair.

The string breaks.

This process, called string breaking, is fundamental to the strong nuclear force and probably played an important role in how matter evolved in the extremely hot early universe.

But calculating its real-time quantum dynamics is extraordinarily difficult.

So researchers from Duke University, the University of Maryland, Oxford, Caltech, Cornell and KU Leuven built a miniature analogue of the problem inside a quantum machine.

They programmed a chain of 13 trapped ytterbium ions to behave according to a simplified lattice gauge theory.

The ions were not literally turned into quarks.

Instead, their quantum states encoded the particles, fields and “string” connecting them — allowing researchers to watch the simulated system evolve with both spatial and temporal resolution.

And then the string broke.

New effective particle pairs appeared and propagated through the simulated system, reproducing the essential quantum dynamics physicists wanted to study.

But the experiment also produced a surprise.

The conventional expectation was that particle pairs would spontaneously appear throughout the string through a process related to the Schwinger mechanism.

Instead, the researchers observed pairs forming preferentially near the two ends of the string, then spreading inward.

Their calculations indicate this is a distinct, previously unobserved mechanism for dynamical string breaking.

This is not a simulation of the full Standard Model, and no real matter was created inside the computer. The experiment used a simplified 1+1-dimensional Z₂ gauge theory, and today’s classical computers can still reproduce a system this small.

The real prize comes later.

As quantum simulators grow, they could attack versions of these problems that conventional supercomputers cannot efficiently calculate — potentially letting physicists experimentally explore the quantum dynamics of particle collisions and conditions resembling the universe shortly after the Big Bang.

We built computers out of quantum mechanics.

Now we’re beginning to use them to ask quantum mechanics how the universe built matter.

#QuantumComputing #QuantumPhysics #ParticlePhysics #BigBang #Physics #Science

https://doi.org/10.1038/s41567-026-03422-0