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Category: robotics/AI

New AI-based technology offers real-time electric vehicle state estimation for safer driving

A research team led by Professor Kanghyun Nam from the Department of Robotics and Mechanical Engineering at DGIST has developed a physical AI-based vehicle state estimation technology that accurately estimates the driving state of electric vehicles in real time.

This technology is viewed as a key advancement that can improve the core control performance of electric vehicles and greatly enhance the safety of autonomous vehicles. The work was conducted through international joint research with Shanghai Jiao Tong University in China and the University of Tokyo in Japan.

The work is published in the journal IEEE Transactions on Industrial Electronics.

New memristor-based converter boosts energy efficiency in AI hardware

A cross-institutional team led by researchers from the Department of Electrical and Electronic Engineering (EEE), under the Faculty of Engineering at The University of Hong Kong (HKU), have achieved a major breakthrough in the field of artificial intelligence (AI) hardware by developing a new type of analog-to-digital converter (ADC) that uses innovative memristor technology. The work is published in Nature Communications.

Challenges with conventional AI hardware Conventional AI accelerators face challenges because the essential components that convert analog signals into digital form are often bulky and power-consuming. Led by Professor Ngai Wong, Professor Can Li and Dr. Zhengwu Liu of HKU EEE, in collaboration with researchers from Xidian University and the Hong Kong University of Science and Technology, the cross-disciplinary research team developed a new type of ADC that uses innovative memristor technology. This new converter can process signals more efficiently and accurately, paving the way for faster, more energy-efficient AI chips.

Adaptive system and efficiency gains The research team created an adaptive system that automatically adjusts its settings based on the data it receives, i.e., dynamically fine-tuning how signals are converted. This results in a 15.1× improvement in energy efficiency and a 12.9× reduction in circuit area compared with state-of-the-art solutions.

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Toward high entropy material discovery for energy applications using computational and machine learning methods

npj Computational Materials, Article number: (2025) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

ARK Robotics Research

Automation and robotics, particularly with the integration of AI, are transforming industries and poised to significantly impact the workforce, but are likely to lead to a reduction in work hours and increased productivity rather than total job destruction.

## Questions to inspire discussion.

Investment & Market Opportunity.

🤖 Q: What is the revenue potential for robotics by 2025? A: ARK Invest projects a $26 trillion global revenue opportunity across household and manufacturing robotics by 2025, driven by convergence of humanoid robots, AI, and computer vision technologies.

💰 Q: How should companies evaluate robot ROI for deployment? A: Robots are worth paying for based on task-specific capabilities delivering 2–10% productivity gains, unlike autonomous vehicles requiring full job performance—Roomba succeeded despite early limitations by being novel and time-saving for specific tasks.

Implementation Strategy.

Continue reading “ARK Robotics Research” | >

AI model uses social media posts to predict unemployment rates ahead of official data

Social media posts about unemployment can predict official jobless claims up to two weeks before government data is released, according to a study. Unemployment can be tough, and people often post about it online.

Researcher Sam Fraiberger and colleagues recently developed an artificial intelligence model that identifies unemployment disclosures on social media. The work is published in the journal PNAS Nexus.

Data from 31.5 million Twitter users posting between 2020 and 2022 was used to train a transformer-based classifier called JoblessBERT to detect unemployment-related posts, even those that featured slang or misspellings, such as “I needa job!” The authors used demographic adjustments to account for Twitter’s non-representative user base, then forecast US unemployment insurance claims at national, state, and city levels.

Too much screen time too soon? Study links infant screen exposure to brain changes and teen anxiety

Children exposed to high levels of screen time before age 2 showed changes in brain development that were linked to slower decision-making and increased anxiety by their teenage years, according to new research by Asst. Prof. Tan Ai Peng and her team from A*STAR Institute for Human Development and Potential (A*STAR IHDP) and National University of Singapore (NUS) Yong Loo Lin School of Medicine, using data from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) cohort.

New robotic skin lets humanoid robots sense pain and react instantly

If you accidentally put your hand on a hot object, you’ll naturally pull it away fast, before you have to think about it. This happens thanks to sensory nerves in your skin that send a lightning-fast signal to your spinal cord, which immediately activates your muscles. The speed at which this happens helps prevent serious burns. Your brain is only informed once the movement has already started.

If something similar happens to a humanoid robot, it typically has to send sensor data to a central processing unit (CPU), wait for the system to process it, and then send a command to the arm’s actuators to move. Even a brief delay can increase the risk of serious damage.

But as humanoid robots move out of labs and factories and into our homes, hospitals and workplaces, they will need to be more than just pre-programmed machines if they are to live up to their potential. Ideally, they should be able to interact with the environment instinctively. To help make that happen, scientists in China have developed a neuromorphic robotic e-skin (NRE-skin) that gives robots a sense of touch and even an ability to feel pain.

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