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Category: robotics/AI – Page 48

PCI-SIG Announces PCI-Express Gen 8 Specification: 256 Gbps Per Lane Per Direction, Arrives 2028

PCI-SIG today announced the PCI Express (PCIe) 8.0 specification will double the data of the PCIe 7.0 specification to 256.0 GT/s and is planned for release to members by 2028. “Following this year’s release of the PCIe 7.0 specification, PCI-SIG is excited to announce that the PCIe 8.0 specification will double the data rate to 256 GT/s, maintaining our tradition of doubling bandwidth every three years to support next-generation applications,” said Al Yanes, PCI-SIG President and Chairperson. “With the increasing data throughput required in AI and other applications, there remains a strong demand for high performance. PCIe technology will continue to deliver a cost-effective, high-bandwidth, and low-latency I/O interconnect to meet industry needs.”

AI-Powered Discovery of High-Performance Polymers for Heat Dissipation

In a groundbreaking step forward for polymer science and electronics cooling technology, researchers from Japan have leveraged artificial intelligence to identify a new class of liquid crystalline polyimides with remarkably high thermal conductivity. Their work, recently published in npj Computational Materials, combines data science, chemistry, and machine learning to accelerate the search for next-generation materials capable of efficiently dissipating heat in compact, high-performance electronics.

🔗 Original article on Phys.org

CRISPR-GPT Turns Novice Scientists into Gene Editing Experts

CRISPR technology has revolutionized biology, largely because of its simplicity compared to previous gene editing techniques. However, it still takes weeks to learn, design, perform, and analyze CRISPR experiments; first-time CRISPR users often end up with low editing efficiencies and even experts can make costly mistakes.

In a new study, researchers from Stanford University, Princeton University, and the University of California, Berkeley, teamed up with Google DeepMind to create CRISPR-GPT, an artificial intelligence (AI) tool that can guide researchers through every aspect of CRISPR editing from start to finish in as little as one day.1 The results, published in Nature Biomedical Engineering, demonstrate that researchers with no previous CRISPR experience could achieve up to 90 percent efficiency in their first shot at gene editing using the tool.

CRISPR-GPT is a large language model (LLM), a type of AI model that uses text-based input data. Led by Le Cong of Stanford University and Mengdi Wang of Princeton University, the team trained the model on over a decade of expert discussions, as well as established protocols and peer-reviewed literature. They designed it to cover gene knockout, base editing, prime editing, and epigenetic editing systems, and benchmarked the tool against almost 300 test questions and answers.

High-level visual representations in the human brain are aligned with large language models

Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain activity to caption-based embeddings from large language models, the study reveals a way to quantify complex visual understanding.

Anthropic says they’ve found a new way to stop AI from turning evil

AI is a relatively new tool, and despite its rapid deployment in nearly every aspect of our lives, researchers are still trying to figure out how its “personality traits” arise and how to control them. Large learning models (LLMs) use chatbots or “assistants” to interface with users, and some of these assistants have exhibited troubling behaviors recently, like praising evil dictators, using blackmail or displaying sycophantic behaviors with users. Considering how much these LLMs have already been integrated into our society, it is no surprise that researchers are trying to find ways to weed out undesirable behaviors.

Anthropic, the AI company and creator of the LLM Claude, recently released a paper on the arXiv preprint server discussing their new approach to reining in these undesirable traits in LLMs. In their method, they identify patterns of activity within an AI model’s neural network—referred to as “persona vectors”—that control its character traits. Anthropic says these persona vectors are somewhat analogous to parts of the brain that “light up” when a person experiences a certain feeling or does a particular activity.

Anthropic’s researchers used two open-source LLMs, Qwen 2.5-7B-Instruct and Llama-3.1-8B-Instruct, to test whether they could remove or manipulate these persona vectors to control the behaviors of the LLMs. Their study focuses on three traits: evil, sycophancy and hallucination (the LLM’s propensity to make up information). Traits must be given a name and an explicit description for the vectors to be properly identified.

Computers reconstruct 3D environments from 2D photos in a fraction of the time

Imagine trying to make an accurate three-dimensional model of a building using only pictures taken from different angles—but you’re not sure where or how far away all the cameras were. Our big human brains can fill in a lot of those details, but computers have a much harder time doing so.

This scenario is a well-known problem in and robot navigation systems. Robots, for instance, must take in lots of 2D information and make 3D —collections of data points in 3D space—in order to interpret a scene. But the mathematics involved in this process is challenging and error-prone, with many ways for the computer to incorrectly estimate distances. It’s also slow, because it forces the computer to create its 3D point cloud bit by bit.

Computer scientists at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) think they have a better method: A breakthrough algorithm that lets computers reconstruct high-quality 3D scenes from 2D images much more quickly than existing methods.

Friction that cools: Threshold effects enable self-stopping robot swarms

How can a horde of active robots be automatically brought to a standstill? By arresting their dynamics in a self-sustained way. This phenomenon was discovered by physicists at Heinrich Heine University Dusseldorf (HHU) and La Sapienza University in Rome. The threshold principle of static friction with the ground plays a decisive role here: it removes the kinetic energy of two robots after a mutual collision so efficiently that they can no longer set themselves in motion.

The researchers describe in the journal Nature Communications that this fundamental effect can also be used to construct controllable moving systems.

Friction creates heat, as anyone knows who has rubbed their hands together in winter weather. And costs energy. Road friction on vehicle tires, for example, will cause a moving car to steadily slow down unless the accelerator is used.

Researchers create shape-shifting robot that liquifies to escape cage

Researchers from China and us create shape shifting robot:

In a scene straight out of science fiction, researchers from China and the U.S. have developed a shape-shifting robot made from magnetically responsive liquid metal that can melt, flow, escape confinement, and reassemble itself—all on command.

Inspired by sea cucumbers and powered by gallium, a metal with a melting point just above room temperature, the robot can switch between solid and liquid states using magnetic fields. During tests, it was able to melt, escape from a prison-like cage, and then re-solidify into its original form—without losing function.

Unlike traditional rigid robots, this breakthrough allows machines to:

* Navigate tight or complex spaces * Heal themselves or split apart to avoid damage * Perform surgical tasks inside the human body without invasive procedures * Transition between tool-like solidity and liquid flexibility.

The magnetic fields not only induce the phase change but also control movement, making the robot swim, climb walls, and even jump. Researchers envision future uses in minimally invasive medicine, like removing foreign objects from internal organs, or in electronic assembly, where the robot could flow into hard-to-reach places and form circuits.

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