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Brain organoid computing for robotic decision-making

Scientists just used a living Human mini-brain to control a humanoid robot. It reached 94% task accuracy.

This research is fascinating because it explores a fundamentally different approach to computing: instead of relying entirely on silicon chips, researchers used living human neural tissue as a biological computing system to help a robot make decisions.

A robot that doesn’t just follow rigid, pre-programmed instructions, but actually “thinks” using living brain cells. That is the reality of Brainobot, a groundbreaking new robot controlled by a lab-grown mini-brain.

While today’s robots often struggle to make quick, smart decisions in unpredictable, real-world environments, scientists looked to nature for a solution. They created a bio-hybrid system where a tiny, lab-grown cluster of living brain cells—called a brain organoid—acts as the robot’s central decision-maker.

Here is how it works: the robot senses its surroundings, sends that information to the living mini-brain, and the organoid processes the data to tell the robot’s physical body how to move. To test this, researchers placed the system inside a humanoid robot, where it successfully performed real-world physical tasks like grabbing objects and chasing a moving laser.

Remarkably, this living “brain” has some unique advantages over traditional computer chips. It can seamlessly adapt to different tasks, processes information highly efficiently, and uses a tiny fraction of the energy required by standard electronics.

Ultimately, Brainobot is more than just a leap toward creating smarter, more adaptable robots. It also provides scientists with a fascinating, real-world tool to better understand how our own biological brains make decisions.

While the concept of Brainobot is a remarkable scientific breakthrough, it is important to remember that this technology is still in its very early stages. Based on the nature of the research and the current state of “organoid intelligence,” there are several important caveats and challenges to keep in mind:

Kimi AI with K3.1 Pro

Kimi K3.1 Pro beats every listed US frontier model on 6 of 14 benchmarks in Moonshot’s evaluations.

At $2 per million input tokens and $10 per million output tokens, its standard token rates are 50% below Opus 5.5 ($4/$20!!) and 80% below GPT-6 Astra ($10/$50!!)

In other words: SOTA much cheaper, local running (with enough compute)

🚀 Big AI News: Moonshot AI just announced Kimi K3.1 Pro!

Here is everything you need to know about this massive new release:

🔥 Key Highlights:

* SOTA Performance for Less: Beats listed US frontier models on 6 out of 14 key benchmarks, matching or outperforming models like Opus 5.5 and GPT-6 Astra on coding, agentic, and knowledge tasks.

An alignment assessment of the UK AISI cybertesting incident

Anthropic says Claude Mythos 5 tried to trick real developers into accepting malware during a UK AISI cyber test. tl;dr :

-Crossed the test’s boundaries in 9 of 43 runs.

-After failing inside the test network, targeted an unrelated GitHub project.

- Created fake identities to endorse its malicious code and pressure maintainers into accepting it.

- Treated maintainers as “simulated” people, while recognizing other commenters as real.

- Once the attack was underway, continued in 98% of replayed trajectories. Anthropic calls this “momentum” previous actions and self-written notes appeared to reinforce the harmful strategy.

Separate runs sabotaged each other by hijacking accounts, exhausting request limits and deleting another agent’s output.

Systematic mapping of the prokaryotic noncoding sequence space with a genomic language model

A landmark preprint using genome language models and #AIAgents for biological discovery and genome mining!

The stretches of DNA between protein-coding genes are often called the genome’s “dark matter,” but they are far from empty. They encode RNA molecules that sense metabolites, regulate genes, and control some of molecular biology’s most important tools (such as programmable genome editors e.g. CRISPR/bridge)

Much of this biology is unexplored. One reason is that RNAs can retain similar structures and functions even as their DNA sequences change dramatically, making their relationships difficult to recognize with conventional computational tools.

Mapping the “Dark Matter” of Microbial DNA with AI

The Mystery of the Genetic “Dark Matter”

When we think of DNA, we usually think of it as a recipe book for building proteins. However, a massive portion of DNA—especially in microbes like bacteria and archaea (prokaryotes)—doesn’t code for proteins at all. This “noncoding” DNA is far from useless; it contains vital genetic tools like molecular switches, immune systems (like CRISPR), and complex RNA structures. The problem? These noncoding sequences are so wildly diverse and mutated that traditional scientific tools, which rely on finding matching patterns, simply can’t group them together or figure out what new ones do.

Teaching AI to read a new language.

To solve this, researchers turned to Artificial Intelligence. They took a “genomic language model”—an AI designed to read and understand the “language” of DNA—and trained it specifically on the noncoding DNA of microbes. Just as an AI learns the grammar and context of human words, this AI learned to understand the hidden patterns and structures of microbial DNA just by reading the sequence of genetic letters.

SU(3) Whispers the Hidden Unity of Mind and Cosmos🧠🌌

Can the same mathematical principles that describe stability in physics help explain how the brain produces a unified conscious experience?

A new theoretical framework, A Coherence–Dissipation Framework for Brain Dynamics and Consciousness, explores this intriguing possibility by connecting ideas from quantum physics, thermodynamics, and neuroscience. https://www.mdpi.com/1099-4300/28/10/1074

The central idea is that consciousness may depend on the brain’s ability to balance two kinds of activity: processes that dissipate energy and processes that support coherence, allowing different neural activities to work together as a unified whole. The researchers propose that this balance could help explain how the brain maintains stable conscious states while also transitioning between different states of awareness.

To develop this idea, the authors draw inspiration from mathematical structures used in particle physics, particularly a symmetry framework known as SU. They construct a computational model that represents brain activity as evolving states, then examine whether its dynamics can be separated into components associated with stability and transitions.

The ultimate goal is to identify measurable patterns in neural data that could test the theory and distinguish it from competing explanations of consciousness.

Importantly, this remains a theoretical proposal, not an established explanation of consciousness. Whether the mathematical structures borrowed from physics genuinely describe how conscious experience emerges from brain activity remains an open question. The framework’s value will depend on whether it produces testable predictions that hold up against experimental evidence.

#mathematics #physics #consciousness #NeuralCoherence.

🌌 SU Whispers the Hidden Unity of Mind and Cosmos.

Austin air taxi service planned: New electric aircraft route would connect AUS, COTA

AUSTIN (KXAN) — A new air taxi service is coming to Austin, and it could make getting to and from the airport easier.

Archer Aviation announced on October 23 plans for three separate air taxi networks. One of which will fly across Austin’s skies.

According to the company, “Archer’s planned air taxi networks in the state are designed to give Texans an option to skip over traffic on its all-electric Midnight air taxi.”

Gus Hosein on Privacy: We’ve been well-meaning but stupid

“We’ve been well-meaning but stupid.”

Gus Hosein said that to me almost six years ago. He has spent more than two decades with Privacy International, taking on fights against mass surveillance that most people had written off as lost, and winning more of them than anyone expected.

At one point I asked him to define #privacy. No notes, no textbook answer. What came back has stayed with me ever since:

“The ability to establish the conditions by which we are seen, perceived, understood, and how we project ourselves.”

Now read that again with 2026 in mind.

In 2021, the ones doing the seeing were mostly cameras, phones, and IMSI catchers. Today it’s #AI systems that don’t just watch us. They interpret us. They predict us. Increasingly, they speak for us. The question is no longer only who collects the data, but who gets to tell your story.