In this Article, Professor Lee traces the expansion of prosecutorial power relative to judicial power under the federal sentencing guidelines by examining the substantial assistance departure. Under the Guidelines, a federal judge may grant sentencing leniency to a defendant who cooperates or provides "substantial assistance" to the government if, and only if, the prosecutor files a motion permitting such departure. The one exception to this government motion requirement allows the judge to depart sua sponte if- (1) the prosecutor's refusal to file the motion was based upon an impermissible motive such as race, religion, national origin, or the exercise of a constitutional right, or (2) the prosecutor's refusal to file is not rationally related to a legitimate governmental objective. Professor Lee points out that originally, the relationship between the prosecutor and the sentencing judge under the Guidelines was akin to that of a gatekeeper and the head of a household. The prosecutor functioned as a gatekeeper by determining which defendants would be eligible for a substantial departure. The judge, however, was still the head of the household. The judge retained final say over which defendants, if any, would be allowed into the house of substantial assistance departures, and which rooms they could enter. The judge could grant or deny the prosecutor's motion and if the judge granted the motion, the judge had the final say over the extent of the departure. The prosecutor's power over substantial assistance appears to be greater than originally imagined. Pursuant to Melendez v. United States, the prosecutor has power not only over which defendants are eligible for a substantial assistance departure, but also over the extent of the departure in cases involving a mandatory sentence. In other words, the prosecutor is more than merely a gatekeeper. The prosecutor now is more of a concierge, directing which defendants may enter the household and which rooms they may visit. To reign in the federal prosecutor's virtually unreviewable power over substantial assistance departures, Professor Lee suggests that the Department of Justice, or a body of United States Attorneys, should establish nationwide guidelines on substantial assistance. Prosecutorial guidelines would then offer consistency and visibility. To ensure compliance with new guidelines, deviation from the guidelines would be reviewable by the sentencing judge.
This discussion paper argues that brain–computer interface (BCI) technology provides empirical evidence for substrate-independent consciousness. Beginning from physicalist premises, I draw on neuroscientist Jon Lieff’s research showing consciousness operates at molecular levels where biological and mechanical distinctions dissolve. Examining ordinary speech reveals consciousness already traverses non-conscious media (air) while preserving embodied experience, suggesting substrate transfer is fundamental to consciousness rather than exceptional. BCI systems like Synchron’s stentrode demonstrate that neural signals transfer through multiple substrates—Bluetooth, TCP/IP protocols, electromagnetic transmission—while preserving functional content and phenomenological continuity. Using information technology examples, particularly networking protocols and PowerLAN technology, I show consciousness exhibits “code-switching”: transferring between physical media while maintaining information patterns. Language functions as a protocol stack rather than consciousness itself, with pre-linguistic consciousness operating through pattern processing at cellular and molecular levels. This raises a provocative question: when consciousness travels through wires, is the conduit itself conscious? I conclude by connecting this to panpsychism, understood as physical reality constituting a universal mathematical information network. An operational framework and practical examples demonstrate that biological–mechanical consciousness integration is empirically verifiable and currently functional.
With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and temporal scales, ranging from controlled laboratory settings to intricate field. Furthermore, AI facilitates the combination of satellite observations, unmanned aerial vehicle (UAV) imaging, soil and climate data, and spatiotemporal information to enhance the precision of trait monitoring and yield prediction. These advances enhance the ability to evaluate and predict crop performance under variable environmental conditions. This paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
Understanding the structure and dynamics of ring or cyclic polymers is a long-standing challenge in polymer science, with important implications for emerging biological phenomena such as chromosome territories. This Perspective article provides a comprehensive overview of the current state of ring polymer physics and rheology, highlighting emerging challenges and opportunities for future research. Key scientific questions are considered regarding the properties of synthetic and biological ring polymer systems using theory, simulations, and experiments. This article was inspired by stimulating discussions at a CECAM Flagship workshop on Ring Polymer Dynamics in Prato, Italy, in June 2023. Several of the concepts and results discussed here are also presented in the Journal of Rheology virtual issue on ring polymers (https://pubs.aip.org/jor/collection/1392/Ring-Polymers). Broadly, this article aims to spark conceptual advances in polymer physics and rheology by exploring new phenomena and open scientific questions that are unique to ring polymer systems.
Large Language Models (LLMs) such as ChatGPT are transforming how scientists conduct and validate research, offering promise as tools to improve scientific reproducibility. However, computational reproducibility and error detection remain expensive and labor-intensive. We experimentally test how collaboration between researchers and LLM assistants influences the reproduction of quantitative social science findings across different levels of AI autonomy. We randomly assigned 288 researchers to 103 teams working under three conditions: human-only, AI-assisted (using ChatGPT as a collaborative tool), or AI-led (ChatGPT operating with minimal human oversight). Teams reproduced published results from leading social science journals, detected coding errors, and proposed robustness checks. Human-only and AI-assisted teams achieved comparable reproduction rates (94% vs. 91%) and performed similarly on most outcomes, except human-only teams identified significantly more major coding errors. Both substantially outperformed AI-led teams, which achieved only a 37% reproduction rate, detected fewer errors across all categories, proposed weaker robustness checks, and required more time. This autonomous approach, however, likely represents only a lower bound of AI capabilities. Despite rapid model advances, expert human judgment currently remains indispensable for reliable empirical verification. While AI assistance did not degrade most outcomes, it provided no measurable advantages and was associated with reduced detection of major errors. However, the 37% autonomous reproduction rate indicates that AI could provide value in settings where scale or cost constraints preclude human review of papers, even though general-purpose LLMs offer no immediate advantages for human-supervised verification.
