Examining the Potential and Pitfalls of ChatGPT in Science and Engineering Problem-Solving

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-world problems where not all necessary data were given. Our findings show that ChatGPT could successfully solve 62.5% of the well-specified problems, but its accuracy drops to 8.3% for under-specified problems. Analysis of the model's incorrect solutions revealed three distinct failure modes: 1) failure to construct accurate models of the physical world, 2) failure to make reasonable assumptions about missing data, and 3) calculation errors. The study offers implications for how to leverage LLM-augmented instructional materials to enhance STEM education. The insights also contribute to the broader discourse on AI's strengths and limitations, serving both educators aiming to leverage the technology and researchers investigating human-AI collaboration frameworks for problem-solving and decision-making.Comment: 12 pages, 2 figure

Examining the potential and pitfalls of ChatGPT in science and engineering problem-solving

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-world problems where not all necessary data were given. Our findings show that ChatGPT could successfully solve 62.5% of the well-specified problems, but its accuracy drops to 8.3% for under-specified problems. Analysis of the model's incorrect solutions revealed three distinct failure modes: (1) failure to construct accurate models of the physical world, (2) failure to make reasonable assumptions about missing data, and (3) calculation errors. The study offers implications for how to leverage LLM-augmented instructional materials to enhance STEM education. The insights also contribute to the broader discourse on AI's strengths and limitations, serving both educators aiming to leverage the technology and researchers investigating human-AI collaboration frameworks for problem-solving and decision-making

New Bounds on R-parity Violating Couplings

We use information from rare nonleptonic decays of heavy-quark mesons to put new bounds on the magnitudes of certain product combinations of baryon nonconserving R-parity violating couplings in supersymmetric models. Product combinations of lepton and baryon nonconserving R-parity violating couplings are also considered in the light of existing bounds on nucleon decay. Contrary to popular impression, a few such combinations are shown to remain essentially unconstrained.Comment: Latex file, 16 pages including 6 uuencoded Postscript files appended. Figures also available via anonymous ftp at ftp://physics.wm.edu/pub/ (get rparity*.ps). Revised version corrects a few sentences in introduction and adds some reference

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

The Persistence of (Subnational) Fortune

Abstract Using subnational historical data, this paper establishes the within country persistence of economic activity in the New World over the last half millennium, a period including the trauma of the European colonization, the decimation of the native populations, and the imposition of potentially growth inhibiting institutions. We construct a data set incorporating measures of pre-colonial population density, new measures of present regional per capita income and population, and a comprehensive set of locational fundamentals. These fundamentals are shown to have explanatory power: native populations throughout the hemisphere were found in more livable and productive places. We then show that high pre-colonial density areas tend to be dense today: population agglomerations persist. The data and historical evidence suggest this is due partly to locational fundamentals, but also to classic agglomeration effects: colonialists established settlements near existing native populations for reasons of labor, trade, knowledge and defense. The paper then shows that high density (historically prosperous) areas also tend to have higher incomes today, and largely due to agglomeration effects: fortune persists for the United States and most of Latin America. JEL: J1, N9, R1, O1, O4