The University of East Anglia has conducted a study that reveals a crucial difference between essays written by students and those generated by AI tools like ChatGPT. While AI-generated essays are impressive in their coherence and grammatical soundness, they fall short when it comes to injecting a personal touch into their content. The researchers analyzed 145 essays written by real university students and another 145 generated by ChatGPT, comparing the two in terms of engagement markers – techniques that enhance clarity, connection, and persuade readers.

The study found that student-written essays consistently featured a rich array of engagement strategies, making them more interactive and persuasive. These included rhetorical questions, personal asides, and direct appeals to the reader. In contrast, ChatGPT-generated essays tended to be impersonal, mimicking academic writing conventions but lacking the conversational nuance and personal touch that human writers bring to their work.

The researchers attribute this difference to the nature of AI training data and statistical learning methods, which prioritize coherence over conversational nuance. This reflects a broader concern that relying too heavily on AI tools could lead to a decline in critical literacy and thinking skills among students.

Despite these findings, the study does not dismiss the potential role of AI in education. Instead, it suggests that tools like ChatGPT should be used as teaching aids rather than shortcuts. By harnessing the power of AI while preserving human engagement and creativity, educators can create a more balanced learning environment that benefits both students and teachers.

This research has significant implications for educators worldwide, particularly in spotting cheating and promoting critical literacy and ethical awareness in the digital age. The study’s findings highlight the importance of fostering personal touch and critical thinking skills in students, rather than relying solely on AI-generated content.

The faster planning system developed by MIT researchers uses machine learning to reduce solve time by up to 50 percent and produce a solution that better meets a user’s objective, such as on-time train departures. The new method could also be applied to other complex logistical problems like scheduling hospital staff or assigning airline crews.

Engineers often break down these kinds of problems into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. However, the overlaps cause many decisions to be needlessly recomputed, making it take much longer to reach an optimal solution.

The researchers’ new approach learns which parts of each subproblem should remain unchanged and freeze those variables to avoid redundant computations. A traditional algorithmic solver then tackles the remaining variables.

“This is a very complex combinatorial scheduling problem,” says Cathy Wu, a member of the Laboratory for Information and Decision Systems at MIT. “Our approach can be applied without modification to all these different variants.”

The researchers’ technique, which they call learning-guided rolling horizon optimization (L-RHO), teaches a machine-learning model to predict which operations should be recomputed when the planning horizon rolls forward.

To test their approach, the researchers compared L-RHO to several base algorithmic solvers and specialized solvers. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

Their method continued to outperform all baselines even when tested on more complex variants of the problem, such as factory machines breaking down or extra train congestion.

“Our approach can be applied without modification to all these different variants,” says Wu. “It even outperformed additional baselines we created to challenge our solver.”

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem – all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported by the National Science Foundation and MIT’s Research Support Committee, among others.

The world of professional baseball has long been aware of the importance of grip in pitching. However, a recent study by researchers from Japan has shed new light on the impact of slip between fingertips and the ball on pitching performance. Prior to June 3, 2021, Major League Baseball (MLB) pitchers had taken advantage of unapproved substances, like pine resin, to create a sticky situation that helped them maintain a precise grip. But what happens when the stickiness is removed?

A team of researchers from Tohoku University’s Graduate School of Engineering set out to understand this phenomenon. Using high-speed cameras, they captured six experienced pitchers throwing fastballs at approximately 130 kilometers per hour and analyzed how different baseball treatments impacted the finger-ball slip distance. The slip distance refers to the distance the fingers slip on the surface of the ball as it is wound and released.

The researchers found that the stickier the surface of the ball, the less the fingers slipped. This resulted in faster pitches with more revolutions per minute (RPM) and more directional control. In fact, when coated with rosin powder or pine resin, the slip distance was reduced by more than half to approximately 8 millimeters on average.

However, the study also revealed an unexpected result: when pitching water-treated balls, the velocity of the pitches dropped significantly compared to other conditions. This is thought to be due to the pitcher’s perception of fingertip slippage and subsequent adjustments in their pitching action.

The researchers’ findings are expected to enhance our understanding of the ball release mechanism under varying friction conditions, contributing to improved pitching performance, injury prevention for pitchers, and the development of better equipment.

In the future, the team plans to investigate changes in pitching movement resulting from different conditions through analysis of whole-body movements and muscle activity. They also aim to identify pitching techniques that maintain performance with slippery balls while reducing the risk of injury.

This study has significant implications for professional baseball and highlights the importance of understanding the physics behind the game. By optimizing grip and minimizing slip, pitchers can potentially improve their performance and reduce the risk of injury.