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Various methods exist to mitigate bias in AI models, including "Constitutional AI," a technique which guides the AI to behave according to a list of rules and principles. Lo, Poosarla, Singhal, Li, Fu, and Mui investigate whether constitutional AI can reduce bias in AI outputs on political topics.

Middle school math forms the basis for advanced mathematical courses leading up to the university level. Large language models (LLMs) have the potential to power next-generation educational technologies, acting as digital tutors to students. The main objective of this study was to determine whether LLMs like ChatGPT, Bard, and Llama 2 can serve as reliable middle school math tutoring assistants on three tutoring tasks: hint generation, comprehensive solution, and exercise creation.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

Here, the authors sought to identify a method to optimize the lift generated by an airfoil based solely on its shape. By beginning with a Bernoullian model to predict an optimized wing shape, the authors then tested their model against other possible shapes by constructing them from Styrofoam and testing them in a small wind tunnel. Contrary to their hypothesis, they found their expected optimal airfoil shape did not result in the greatest lift generation. They attributed this to a variety of confounding variables and concluded that their results pointed to a correlation between airfoil shape and lift generation.

The authors test the feasibility of using thermoelectric modules as a power source and as an air conditioner to decrease reliance on fossil fuels. The results showed that, at its peak, their battery generated 27% more power – in watts per square inch – than a solar panel, and the thermoelectric air conditioner operated despite an unsteady input voltage. The battery has incredible potential, especially if its peak power output can be maintained.

In this study, the authors use high-throughput virtual screening to design and evaluate a set of non-nucleoside reverse transcriptase inhibitors for binding affinity to the protein reverse transcriptase. These studies have important applications toward HIV therapies.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

Greenspaces (urban and wetland areas that contain vegetation) are beneficial to reducing pollution, while airplanes are a highly-polluting method of transportation. The authors examine the intersection of these two environmental factors by processing satellite images to reveal what percentage of flight paths go over greenspaces at major US airports.

The authors examine the relationships between race, racist beliefs, and perceptions of beauty across cultures and generations.

Ant colony optimization algorithms simulate ants moving from point to point on a graph and coordinate their actions, similar to ants laying down pheromones to strengthen a path as it is used more frequently. These ACO algorithms can be applied to the classic traveling salesman problem, which aims to determine the lowest-cost path through a given set of points on a graph. In this study, a novel multiple-colony system was developed that uses multiple simulated ant colonies to generate improved solutions to the traveling salesman problem.