Schools in the US are installing vape-detection tech in bathrooms to thwart student nicotine and cannabis use. A new investigation reveals the impact of using spying to solve a problem. It was in physical education class when Laila Gutierrez swapped out self-harm for a new vice. The freshman from Phoenix had long struggled with depression and would cut her arms to feel something. The first drag from a friend's vape several years ago offered the shy teenager a new way to escape. She quit cutting but got hooked on nicotine. Her sadness got harder to carry after her uncle died, and she felt she couldn't turn to her grieving parents for comfort. Bumming fruity vapes at school became part of her routine. "I would ask my friends who had them, 'I'm going through a lot, can I use it?'" Gutierrez, now 18, told The 74. "Or'I failed my test and I feel like smoking would be better than cutting my wrists.'"

Abstract--Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. Our method samples multiple CAD objects, which are then optimized using an evolutionary approach with vision language and reasoning language models. We assess our method using GPT -4V and GPT -4o, evaluating it on the CAD-Prompt benchmark dataset and comparing it to prior methods. Additionally, we introduce two new metrics based on topological properties defined by the Euler characteristic, which capture a form of semantic similarity between 3D objects. Our results demonstrate that EvoCAD outperforms previous approaches on multiple metrics, particularly in generating topologically correct objects, which can be efficiently evaluated using our two novel metrics that complement existing spatial metrics. The use of generative AI tools powered by large language models (LLMs) has transformed the way humans work, create, and develop. However, while significant attention is directed towards textual knowledge tasks, comparatively little focus is devoted on working with symbolic representations, such as those utilized in computer-aided design (CAD). These code-like textual representations, in the following referred as CAD code, enable visual assets to be processed by LLMs [21].

Silicon Valley billionaires and anti-poverty activists don't have a lot in common, but in recent years they've joined forces around a shared enthusiasm: programs that guarantee a basic income. Tech entrepreneurs like Sam Altman, chief executive of OpenAI, have promoted direct cash transfers to low-income Americans as a way to cushion them from what the entrepreneurs anticipate could be widespread job losses caused by artificial intelligence. Some local politicians and community leaders, concerned about growing wealth inequality, have also put their faith in these stipends, known as unconditional cash or, in their most ambitious form, a universal basic income. Dozens of small pilot projects testing unconditional cash transfers have popped up in communities around the country, from Alaska to Stockton, Calif. Andrew Yang, an entrepreneur, put the idea of 1,000 monthly payments for all adults at the center of his 2020 presidential campaign.

Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relatively small proportion of BLV individuals work with dog guides due to their limited availability and associated maintenance responsibilities. There is considerable recent interest in addressing this challenge by developing legged guide dog robots. This study was designed to determine critical aspects of the handler-guide dog interaction and better understand handler needs to inform guide dog robot development. We conducted semi-structured interviews and observation sessions with 23 dog guide handlers and 5 trainers. Thematic analysis revealed critical limitations in guide dog work, desired personalization in handler-guide dog interaction, and important perspectives on future guide dog robots. Grounded on these findings, we discuss pivotal design insights for guide dog robots aimed for adoption within the BLV community.

Fixed point lattice actions are designed to have continuum classical properties unaffected by discretization effects and reduced lattice artifacts at the quantum level. They provide a possible way to extract continuum physics with coarser lattices, thereby allowing to circumvent problems with critical slowing down and topological freezing toward the continuum limit. A crucial ingredient for practical applications is to find an accurate and compact parametrization of a fixed point action, since many of its properties are only implicitly defined. Here we use machine learning methods to revisit the question of how to parametrize fixed point actions. In particular, we obtain a fixed point action for four-dimensional SU(3) gauge theory using convolutional neural networks with exact gauge invariance. The large operator space allows us to find superior parametrizations compared to previous studies, a necessary first step for future Monte Carlo simulations.

Lattice gauge-equivariant convolutional neural networks (L-CNNs) can be used to form arbitrarily shaped Wilson loops and can approximate any gauge-covariant or gauge-invariant function on the lattice. Here we use L-CNNs to describe fixed point (FP) actions which are based on renormalization group transformations. FP actions are classically perfect, i.e., they have no lattice artifacts on classical gauge-field configurations satisfying the equations of motion, and therefore possess scale invariant instanton solutions. FP actions are tree-level Symanzik-improved to all orders in the lattice spacing and can produce physical predictions with very small lattice artifacts even on coarse lattices. We find that L-CNNs are much more accurate at parametrizing the FP action compared to older approaches. They may therefore provide a way to circumvent critical slowing down and topological freezing towards the continuum limit.

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io

At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. George Mason University looks like any other big college campus with its tall buildings, student housing, and manicured green lawns – except for the robots. This Northern Virginia university recently set up several dozen meal delivery robots from Starship Technologies to make it easier for students to access food. Multiple colleges across the country have deployed delivery robots – including University of the Pacific in Stockton, Calif., and Northern Arizona University – but George Mason University is the first college in the United States to incorporate robots into its student dining plan. The school is partnering with food service provider Sodexo for the program.

At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. George Mason University looks like any other big college campus with its tall buildings, student housing, and manicured green lawns – except for the robots. This Northern Virginia university recently set up several dozen meal delivery robots from Starship Technologies to make it easier for students to access food. Multiple colleges across the country have deployed delivery robots – including University of the Pacific in Stockton, Calif., and Northern Arizona University – but George Mason University is the first college in the United States to incorporate robots into its student dining plan. The school is partnering with food service provider Sodexo for the program.

At most universities, meal plans allow college students to take advantage of on-campus cafeterias or chow down at local restaurants. Now, thousands of students at George Mason University will have another dining option at their disposal: on-demand food delivery via an autonomous robot on wheels. The school has received a fleet of 25 delivery robots that can haul up to 20 pounds each as they roll across campus at four miles per hour, according to Starship Technologies, the Estonia-based robotics company that created the delivery vehicles. The company -- which claims its robots can make deliveries in 15 minutes or less -- says the Fairfax, Va.-based school is the first campus in the country to incorporate robots into its student dining plan and has the largest fleet of delivery roots on any university campus. "Students and teachers have little free time as it is, so there is a convenience for them to have their food, groceries and packages delivered to them," said Ryan Tuohy, Starship Technology's senior vice president of business development.