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“Breakthrough” robot with lab-grown “human brain” promises advancement in brain-computer interfacing

Chinese researchers from Tianjin University and the Southern University of Science and Technology have created a groundbreaking robot powered by a tiny organoid derived from human stem cells grafted to a neural interface. This breakthrough system allows the robot to learn tasks like obstacle avoidance and object manipulation.

Described as the “world’s first open-source brain-on-chip intelligent complex information interaction system,” the technology marks a significant advancement in brain-computer interfaces (BCIs) – devices that translate between neural and computational signals.

The South China Morning Post notes that the scientists grew the organoids from human pluripotent stem cells, which can develop into various cell types, including neural tissue. These synthetic-organic (pardon the oxymoron) brain cells are linked to the robot’s neural interface, enabling communication between the neural tissue and the robot’s systems. Although the presented images of pink brain matter are merely mockups (below), the actual organoids are much smaller.

Chinese researchers create ‘human-on-chip’ system using brain matter to create ‘organoid’ robot

Destroy them.


Researchers at Tianjin University and the Southern University of Science and Technology in China have created a “human-on-chip” system that combines human brain matter with a neural interface chip and have used the technology to create a hybrid “organoid” robot.

The technology is reported to be an emerging branch of brain-computer interfaces, which aims to combine the brain’s electrical signals with external computing power. The idea behind the technology is to develop brain-like computing.

According to the Global Times, the system uses an artificial brain cultivated in vitro – such as a “brain-like organ” — that can interact with external information through encoding, decoding and stimulus feedback when coupled with electrode chips. In vitro, in this case, means that they’re growing the brain-like organ in a controlled laboratory environment using stem cell technology.

Futurist Brian David Johnson: Don’t Let The Future Happen To You!

“When it comes to the future, there are three kinds of people: those who let it happen, those who make it happen, and those who wonder what happened.”

I recorded this conversation with Brian David Johnson 14 years ago, back when he was Intel’s futurist with 25 patents to his name and a mandate to build an actionable vision of computing for 2020.

Read that again. 2020 was the far horizon he was paid to imagine. We are now well past it.

So here is the uncomfortable question worth sitting with: how much of the future he described did we make happen on purpose, and how much simply happened to us while we wondered what was going on?

Brian’s whole method was a refusal to be passive about it. He used ethnographic fieldwork, trend data, and even science-fiction prototyping as a #design tool because he believed the future is not a forecast you wait for; it is an object you construct. His line still lands harder every year: own the fact that you can build the future.

A few things he said in 2012 that read very differently in the age of generative #AI and ubiquitous #robotics:

Distributed Cognition: The New Science of Non-Biological Intelligence

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Hello and welcome! My name is Anton and in this video, we will talk about distributed intelligence and experiments on slime mold and ants.
Links:
https://journals.aps.org/prxlife/pdf/.
ANT Lab • The odorous house ant trail pheromone depo…
Audrey Dussutour • Blob crawling around.
#inteligence #artificialintelligence #biology.

0:00 Intelligence — what is it?
1:10 Mechanical intelligence in the slime mold.
3:30 How it seems to work.
5:55 Ants and swarm intelligence.
6:45 What is the queen for?
8:35 Other swarm animals.
9:45 Ants vs humans.
11:10 Collective intelligence.
12:00 Implications for AI
13:20 Implications for the existence of alien intelligence.

Enjoy and please subscribe.

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The hardware used to record these videos:

We are introducing and open sourcing LongCat-2.0, a large-scale MoE language model with 1.6 trillion total parameters and ~48 billion activated per token —

We are introducing and open sourcing, a large-scale MoE language model with 1.6 trillion total parameters and ~48 billion activated per token — a substantial step up from previous LongCat models, accompanied by several architectural improvements.

Both the full training run and the large-scale deployment are built entirely on AI ASIC superpods. Pretraining spans millions of accelerator-days across more than 35 trillion tokens, with no rollbacks or irrecoverable loss spikes — demonstrating that we have the capability to conduct frontier-scale training on alternative hardware platforms.

To strengthen the model on long-horizon tasks, we introduce LongCat Sparse Attention and train on hundreds of billions of tokens of 1M-context data. Together with dedicated post-training, this gives strong performance on coding and agentic tasks.

New driving model predicts split-second crash avoidance with humanlike accuracy

Scientists at Delft University of Technology, in collaboration with Waymo, have developed a new model that predicts with high accuracy how human drivers respond to dangerous traffic situations. For the first time, different types of collision avoidance behavior are combined into a single model. The results will be published on 10 June in Nature Communications. Waymo is already using the model to compare the performance of its autonomous vehicles with that of human drivers.

When a leading vehicle suddenly brakes or an oncoming car unexpectedly enters your lane, you have only fractions of a second to decide whether to brake, swerve or both. “Existing models typically describe only part of this process, such as reaction time or steering behavior,” says Arkady Zgonnikov, assistant professor at Delft University of Technology (The Netherlands). “Our new model brings all these components together.”

The model integrates perception, decision-making and execution into a single coherent framework. As a result, it can detect when a situation becomes dangerous, predict how the traffic situation is likely to evolve and simultaneously determine the most effective avoidance strategy.

New superconductors identified, unlocking process that could yield thousands more

An international team of quantum researchers has shown how machine learning can be used to filter a practically infinite number of possible material combinations to identify candidates for superconductivity. Thanks to the breakthrough, new superconductors can now be found much faster, says Aalto University Professor Päivi Törmä, who leads the SuperC consortium behind the research.

Superconductors carry electric current with zero resistance, thanks to a quantum effect appearing only at extremely low temperatures. They power not only quantum computers but many other things, from neuroimaging to fusion reactors and maglev trains.

However, these unicorn materials are prohibitively hard to identify. Any endlessly variable combination of elements could be a superconductor—yet few actually are. And the ones already discovered require expensive cooling equipment to bring them to the near-absolute-zero temperatures that give them their quantum properties.

Apple Patches 30+ iOS, macOS, Safari Flaws, Including AI-Discovered WebKit Bugs

The four vulnerabilities are part of nearly 30 vulnerabilities that have been patched in WebKit, an open-source web browser engine developed by Apple. Others include a use-after-free issue in WebKit Canvas (CVE-2026–43720) and a vulnerability that could be exploited by a malicious website to process restricted web content outside the sandbox (CVE-2026–43725).

Apple has also remediated three bugs that could be exploited by a malicious app to leak sensitive kernel state (CVE-2026–43722), cause unexpected system termination or write kernel memory (CVE-2026–43724), or corrupt kernel memory (CVE-2026–39868). Security researcher Hyunwoo Kim, who discovered Dirty Frag, has been credited with discovering and reporting CVE-2026–43724 and CVE-2026–43722.

The updates are available for iOS 26.5.2, iPadOS 26.5.2, macOS Tahoe 26.5.2, and Safari 26.5.2. None of the patched vulnerabilities has been disclosed as actively exploited in the wild.

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