Large language models (LLMs) are increasingly deployed in decision-support systems for high-stakes domains such as hiring and university admissions, where choices often involve selecting among competing alternatives. While prior work has noted position order biases in LLM-driven comparisons, these biases have not been systematically analyzed or linked to underlying preference structures. We present the first comprehensive study of position biases across multiple LLMs and two distinct domains: resume comparisons, representing a realistic high-stakes context, and color selection, which isolates position effects by removing confounding factors. We find strong and consistent order effects, including a quality-dependent shift: when all options are high quality, models favor the first option, but when quality is lower, they favor later options. We also identify two previously undocumented biases in both human and machine decision-making: a centrality bias (favoring the middle position in triplewise comparisons) and a name bias, where certain names are favored despite controlling for demographic signals. To separate superficial tie-breaking from genuine distortions of judgment, we extend the rational choice framework to classify pairwise preferences as robust, fragile, or indifferent. Using this framework, we show that order effects can lead models to select strictly inferior options, and that position biases are typically stronger than gender biases. These results indicate that LLMs exhibit distinct failure modes not documented in human decision-making. We also propose targeted mitigation strategies, including a novel use of the temperature parameter, to recover underlying preferences when order effects distort model behavior.

"I observed assistive technologies -- developed by scientists and engineers my friends and I never met -- which liberated us. My dream has always been to be one of those engineers." Before James Hermus started elementary school, he was a happy, curious kid who loved to learn. By the end of first grade, however, all that started to change, he says. As his schoolbooks became more advanced, Hermus could no longer memorize the words on each page, and pretend to be reading.

Arachnophobes look away now; engineers have found a way to turn dead spiders into mechanical gripping robots straight out of your nightmares. Researchers from Rice University in Texas pumped wolf spider cadavers with air to get their legs to unfurl and clasp around objects. They discovered that the arachnids were able to lift 130 per cent of their own body weight, and could manipulate a circuit board. It is hoped the delicate gripper could be used in microelectronics, or that its natural camouflage could be helpful if capturing small insects for study. Daniel Preston, assistant professor in mechanical engineering, said: 'It happens to be the case that the spider, after it's deceased, is the perfect architecture for small scale, naturally derived grippers.

Mechanical Engineer at Hawk Research Remote › Worldwide, 100% remote position (Posted Mar 18 2022) About the company Hawk Research is a freelance educational company. We are one of the most popular companies in the field of knowledge exchange. It offers educational services in various spheres and subjects to customers around the world since 2014. The main goal is to share educational experience, knowledge and ideas with clients and give a personal educational support to those who have lack of it. The service allows our customers to get a better understanding of subject questions in specific spheres.

There are some mechanical engineering fields in which AI is about to give a paradigm shift. AI used in Computer-Aided Design (CAD) generally works on knowledge-based systems. Design artefacts, rules, and problems in CAD are stored which later assist CAD designers. Merging of AI and CAD is done through Model-Based Reasoning (MBR). Many new releases of software packages are using knowledge-based systems.

The best teams for robotics are not all computer scientists -- they have electrical & mechanical engineers, computer scientists, robots, and more to fill the cracks. This post is an exploration of how different ways of thinking contribute in robotics -- and by extension to many software engineering projects. How would you summarize the overarching conceptual theme of your undergraduate major? This was originally posted on my free newsletter on robotics & automation, Democratizing Automation. I don't characterize EE primarily by circuit design nor nano-fabrication.

Wired Magazine recently called for us to, post pandemic, "ditch our tech enabled tools of social distancing". But are our telepresence robots creating emotional distancing or are they actually improving our emotional lives. This week in our weekly "COVID-19, robots and us" discussion with experts, we're looking at the topic of virtual presence and emotional contact as well as many other practical ways that robotics can make a difference in pandemic times. Robin Murphy, Raytheon Professor at Texas A&M University and founder of the field of Rescue Robotics, was involved in the very first use of robots in a disaster scenario in 9/11. Since then she's been involved in multiple disaster responses worldwide, including the Ebola outbreak in 2014-2016.

I have a Mechanical Engineering (ME) background as all of my degrees are in ME. After my Bachelor, I was doing my higher education in the field of Robotics when the Data revolution took shape. People were more familiar with the word "Big Data" at the time rather than Data Science. Then, I got hooked up with Machine Learning and started steering my career path towards Data Science since. I had a bit of good start with a Robotics background, especially in programming, so I didn't have to start from scratch.

The front end of recycling is familiar to the point of invisibility: Blue bins, clear bags, and barely comprehensible signs designating which material goes where. Once the right plastic or paper is put in the right place, most people forget all about it. For the actual recycled material, though, that's not the end of the journey but rather the beginning. Most of it gets trucked to a special recycling facility, where it is unceremoniously dumped on a concrete floor. Front-end loaders scoop bottles, papers, and myriad other materials onto conveyors, which zoom off in various directions, often climbing to different levels like staircases.

We are looking for a Mechanical Engineer to be a part of the Mechanical and Thermal Engineering Group (MTEG), which is the design center of excellence for all mechanical (sockets, chip debug hardware, chassis, lab robotics, automation equipment etc..) and thermal (passive heatsinks, active heatsinks, thermal margining tools, thermal controllers, system thermal solutions) for all Validation of nearly every Intel's silicon product (Products that power: laptops, notebooks, tablets, phones, work stations, servers, super computers, wearables, drones etc..). The candidate will be exposed to a variety of design and analysis opportunities in both Thermal and Mechanical engineering across multiple business segments. Intel AI, leveraging Intel's world leading position in silicon innovation and proven history in creating the compute standards that power our world, is transforming Artificial Intelligence (AI) with the Intel AI products portfolio. Harnessing silicon designed specifically for AI, end to end solutions that broadly span from the data center to the edge, and tools that enable customers to quickly deploy and scale up, Intel AI is inside AI and leading the next evolution of compute. All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance….