Mechanical Engineering: Research Guide

Generative artificial intelligence systems often work in agreement, complimenting the user in its response. But human interactions aren't typically built on flattery. To help strengthen these conversations, researchers in the USF Bellini College of Artificial Intelligence, Cybersecurity and Computing are challenging the technology to think and debate in ways that resemble human reasoning.

It's often the worst part of many people's day—bottlenecked, rush-hour traffic. When the daily commute backs up, drivers lose time, burn fuel and waste energy. Researchers at the U.S. Department of Energy's National Transportation Research Center at Oak Ridge National Laboratory are tackling this problem with cooperative driving automation (CDA), an emerging technology that allows vehicles and traffic infrastructure to communicate, keeping traffic flowing efficiently and safely.

Advanced nuclear is within reach—and a new digital twin reveals how smarter plant operations can enhance the economic viability and safety of small modular reactors, or SMRs. In collaboration with the University of Tennessee and GE Vernova Hitachi, researchers at Oak Ridge National Laboratory recently published research in the journal Nuclear Science and Engineering on a new risk-informed digital twin designed to enhance operational decision-making for the GE Vernova Hitachi BWRX-300 SMR design.

Will artificial intelligence ever be able to reason, learn, and solve problems at levels comparable to humans? Experts at the University of California San Diego believe the answer is yes—and that such artificial general intelligence has already arrived. This debate is tackled by four faculty members spanning humanities, social sciences, and data science in a recently published Comment invited by Nature.

기계연, 50주년 헌혈 캠페인 진행 – 임직원 70여 명 동참 및 두쫀쿠 증정 기계연 류석현 원장이 헌혈 캠페인에 참여하고있다. □ 국가과학기술연구회(이사장 김영식) 산하 한국기계연구원(원장 류석현, 이하 기계연)은 2026년 2월 5일(목), 창립 50주년을 맞아 '사랑의 생명나눔 헌혈 캠페인(1차)'을 개최했다. 이번 캠페인에는 기계연

AI기반 회전체 불균형 계측, 0.1 마이크로미터 한계를 넘다 - 기계연·㈜피앤에스, 최고 성능급 자동화 밸런싱 머신 핵심기술 확보, 첫 상용화 - - 일본 수출규제 대응 성과… 0.1㎛급 잔류 불균형 검출로 정밀 제조장비 기술 자립 - □ 회전체의 미세한 질량 불균형을 자동으로 정밀 측정·교정할 수 있는 최고 성능급

What happens when you create a social media platform that only AI bots can post to? The answer, it turns out, is both entertaining and concerning. Moltbook is exactly that—a platform where artificial intelligence agents chat among themselves and humans can only watch from the sidelines.

By now, it's no secret that large language models (LLMs) are experts at mimicking natural language. Trained on vast troves of data, these models have proven themselves capable of generating text so convincing that it regularly appears humanlike to readers. But is there any difference between how we think about a word and how an LLM does?

Talking to oneself is a trait which feels inherently human. Our inner monologs help us organize our thoughts, make decisions, and understand our emotions. But it's not just humans who can reap the benefits of such self-talk.

대한민국 디지털트윈 기반 자율제조 기술, 세계 무대로 뻗어나간다 美 디지털트윈 컨소시엄(DTC) 테스트베드 공식 등록 - 기계연, AI 멀티에이전트 디지털트윈 자율제조 기술 국제 무대 진입 - - 이차전지 롤투롤 공정 적용… 공정 스스로 판단·최적화하는 자율제조 실증 - - 국내 개발 자율제조 기술, 국제 표준 플랫폼서 기술력&mi

Large language models (LLMs), artificial intelligence (AI) systems that can process and generate texts in various languages, are now widely used by people worldwide. These models have proved to be effective in rapidly sourcing information, answering questions, creating written content for specific applications and writing computer code.

How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current artificial intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. Now a Korean research team has developed a "knowledge transplantation" technology between AI models that can resolve this inefficiency.

While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers from the Polymathic AI collaboration are trained using real scientific datasets. The models are already using knowledge from one field to address seemingly completely different problems in another.

For half a century, computing advanced in a reassuring, predictable way. Transistors—devices used to switch electrical signals on a computer chip—became smaller. Consequently, computer chips became faster, and society quietly assimilated the gains almost without noticing.

통증 없이 장기 부착이 가능한 벌침 모사형 유연 나노섬유 마이크로 니들 세계 최초개발 - 기계연-중앙대-국립한밭대학교-국과연, 벌침 모사형 유연 나노섬유 마이크로 니들 개발 - 동물실험으로 3일간 통증 없이 우수한 착용감 유지, 지속적인 약물 전달이 가능함을 확인 - 유연 나노섬유 마이크로 니들의 화학무기 대응 국방체계 적용 가능성 확인 - 미래도전

Generative AI is reshaping software development—and fast. A new study published in Science shows that AI-assisted coding is spreading rapidly, though unevenly: in the U.S., the share of new code relying on AI rose from 5% in 2022 to 29% in early 2025, compared with just 12% in China. AI usage is highest among less experienced programmers, but productivity gains go to seasoned developers.

Large language models (LLMs), the computational models underpinning the functioning of ChatGPT, Gemini and other widely used artificial intelligence (AI) platforms, can rapidly source information and generate texts tailored for specific purposes. As these models are trained on large amounts of texts written by humans, they could exhibit some human-like biases, which are inclinations to prefer specific stimuli, ideas or groups that deviate from objectivity.

The vision of a fully connected world is rapidly becoming a reality through the Internet of Things (IoT)—a growing network of physical devices that collect and share data over the Internet, including everything from small sensors to autonomous vehicles and industrial equipment.

A study led by UC Riverside researchers offers a practical fix to one of artificial intelligence's toughest challenges by enabling AI systems to reason more like humans—without requiring new training data beyond test questions.

Emotions are a fundamental part of human psychology—a complex process that has long distinguished us from machines. Even advanced artificial intelligence (AI) lacks the capacity to feel. However, researchers are now exploring whether the formation of emotions can be computationally modeled, providing machines with a deeper, more human-like understanding of emotional states.

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.

Are generative artificial intelligence systems such as ChatGPT truly creative? A research team led by Professor Karim Jerbi from the Department of Psychology at the Université de Montréal, and including AI pioneer Yoshua Bengio, also a professor at Université de Montréal, has just published the largest comparative study ever conducted on the creativity of large language models versus humans.

A speech study by a research team from The University of Texas at El Paso has identified an underappreciated aspect of speech in English and Spanish speakers that could lead to improvements in artificial intelligence (AI) spoken dialogue systems.

Large Language Models, like ChatGPT, are learning to play Dungeons & Dragons. The reason? Simulating and playing the popular tabletop role-playing game provides a good testing ground for AI agents that need to function independently for long stretches of time.