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Category: robotics/AI – Page 24

Self-driving system makes key plastic ingredient using in-house generated H₂O₂

An eco-friendly system capable of producing propylene oxide (PO) without external electricity or sunlight has been developed. PO is a vital raw material used in manufacturing household items such as polyurethane for sofas and mattresses, as well as polyester for textiles and water bottles.

A research team led by Professors Ja Hun Kwak and Ji-Wook Jang from the School of Energy and Chemical Engineering at UNIST, in collaboration with Professor Sung June Cho of Chonnam National University, has successfully created a self-driven PO production system utilizing in-situ generated hydrogen peroxide (H₂O₂).

The research is published in Nature Communications.

The Minds of Modern AI: Jensen Huang, Yann LeCun, Fei-Fei Li & the AI Vision of the Future | FT Live

Six of the most influential minds in artificial intelligence joined FT Live for an exclusive conversation on how their breakthroughs and the current state of AI are shaping our world.

On 6 November, Jensen Huang, Yoshua Bengio, Geoffrey Hinton, Fei-Fei Li, Yann LeCun, and Bill Dally spoke with the FT’s AI editor, Madhumita Murgia at the FT Future of AI Summit in London. Together, they reflected on decades of pioneering work — from neural networks to generative AI and discuss the ethical, social, and economic implications of the technology they helped to create.

All six, along with Professor John Hopfield, are recipients of the 2025 Queen Elizabeth Prize for Engineering for their foundational contributions to machine learning and AI.

👉 For more exclusive interviews and agenda-setting conversations with global AI leaders, visit our website: https://ai.live.ft.com/

#ArtificialIntelligence #JensenHuang #GeoffreyHinton #AI #MachineLearning #FTLive #FutureofAI

AI tech can compress LLM chatbot conversation memory by 3–4 times

Seoul National University College of Engineering announced that a research team led by Professor Hyun Oh Song from the Department of Computer Science and Engineering has developed a new AI technology called KVzip that intelligently compresses the conversation memory of large language model (LLM)-based chatbots used in long-context tasks such as extended dialog and document summarization. The study is published on the arXiv preprint server.

The term conversation memory refers to the temporary storage of sentences, questions, and responses that a chatbot maintains during interaction, which it uses to generate contextually coherent replies. Using KVzip, a chatbot can compress this memory by eliminating redundant or unnecessary information that is not essential for reconstructing context. The technique allows the chatbot to retain accuracy while reducing memory size and speeding up response generation—a major step forward in efficient, scalable AI dialog systems.

Modern LLM chatbots perform tasks such as dialog, coding, and question answering using enormous contexts that can span hundreds or even thousands of pages. As conversations grow longer, however, the accumulated conversation memory increases computational cost and slows down response time.

How tiny drones inspired by bats could save lives in dark and stormy conditions

Don’t be fooled by the fog machine, spooky lights and fake bats: the robotics lab at Worcester Polytechnic Institute lab isn’t hosting a Halloween party.

Instead, it’s a testing ground for tiny drones that can be deployed in search and rescue missions even in dark, smoky or stormy conditions.

“We all know that when there’s an earthquake or a tsunami, the first thing that goes down is power lines. A lot of times, it’s at night, and you’re not going to wait until the next morning to go and rescue survivors,” said Nitin Sanket, assistant professor of robotics engineering. “So we started looking at nature. Is there a creature in the world which can actually do this?”

New brain atlas offers unprecedented detail in MRI scans

The human brain comprises hundreds of interconnected regions that drive our thoughts, emotions, and behaviours. Existing brain atlases can identify major structures in MRI scans – such as the hippocampus, which supports memory and learning – but their finer sub-regions remain hard to detect. These distinctions matter because sub-regions of areas like the hippocampus, for example, are affected differently during Alzheimer’s disease progression.

Examining the brain at the cellular level is achievable using microscopy (histology), but cannot be done in living individuals, limiting its potential for understanding how the human brain changes during development, ageing and disease.

Published in Nature, the new study introduces NextBrain, an atlas of the entire adult human brain that can be used to analyse MRI scans of living patients in a matter of minutes and at a level of detail not possible until now.

The creators of the atlas, which is freely available, hope it will ultimately help to accelerate discovery in brain science and its translation into better diagnosis and treatment of conditions such as Alzheimer’s.

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Coordinating health AI to prevent defensive escalation

Artificial intelligence (AI) systems that can analyse medical images, records, and claims are becoming accessible to everyone. Although these systems outperform physicians at specific tasks, such as detecting cancer on CT scans, they are still imperfect. But as AI performance progresses from occasionally correct to reliably superior, there will be increasing pressure to conform to algorithmic outputs.

AI-designed antibodies created from scratch

Research led by the University of Washington reports on an AI-guided method that designs epitope-specific antibodies and confirms atomically precise binding using high-resolution molecular imaging, then strengthens those designs so the antibodies latch on much more tightly.

Antibodies dominate modern therapeutics, with more than 160 products on the market and a projected value of US$445 billion in 5 years. Antibodies protect the body by locking onto a precise spot—an epitope—on a virus or toxin.

That pinpoint connection determines whether an antibody blocks infection, marks a pathogen for removal, or neutralizes a harmful protein. When a drug antibody misses its intended epitope, treatment can lose power or trigger side effects by binding the wrong target.

Brain-computer interface decodes Mandarin from neural signals in real time

Researchers in Shanghai have reported in a study, recently published in Science Advances, that they’ve successfully decoded Mandarin Chinese language in real time with the help of a brain-computer interface (BCI) framework, a first for BCIs working with tonal languages. The participant involved in the study was also capable of controlling a robotic arm and digital avatar and interacting with a large language model using this new system.

While most people may not want a computer reading their mind, those who are unable to speak due to neurological conditions, like strokes or amyotrophic lateral sclerosis (ALS), need to find alternative ways to communicate. Speech BCIs, capable of decoding neural signals, offer a promising way to restore communication in such individuals. In addition to communication, BCIs also offer ways to control devices directly through thought. This is particularly helpful for neurological conditions in which disabilities extend beyond loss.

These types of devices are not exactly a novel technology, but most BCI speech decoding research has focused on English, a non-tonal language.

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