F.A.R.O.G. FORUM, Vol. 2 No. 1

https://digitalcommons.library.umaine.edu/francoamericain_forum/1004/thumbnail.jp

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Precision Machine Learning

We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks (NNs) can often outperform classical approximation methods on high-dimensional examples, by (we hypothesize) auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivates us to study the unique properties of neural network loss landscapes and the corresponding optimization challenges that arise in the high precision regime. To address the optimization issue in low dimensions, we develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision

Double Cropping Cotton After Small Grain at Safford

The 1985 and 1986 Cotton Reports have the same publication and P-Series numbers

Upland and Pima Cotton Planting Rates and Dates at Safford

The 1985 and 1986 Cotton Reports have the same publication and P-Series numbers

Yield of 8 Upland and 2 Pima Cotton Varieties Planted at 5 Dates at Safford in 1985

The 1985 and 1986 Cotton Reports have the same publication and P-Series numbers.Verticillium wilt tolerance and varietal response were factors in lint yield in this test. Highest yields were obtained from the first planting on 8 April. The 19 April planting had a lower average yield than the 7 May planting, probably because of poorer stands. Lint yields decreased an average of 5 pounds of lint/acre/day between 8 April and 7 May; 9 pounds between 7 May and 24 May; and 15 pounds between 24 May and 10 June. Deltapine (DP) 90 had the highest lint yield for the first two plantings and was among the highest for all plantings. DP 30 was highest in lint yield for the third and fourth plantings and high at other planting dates. Wilt tolerance was a factor in DP 30 performance. If the price premium for pima lint is considered, P-62 was superior to all upland cottons for the first four plantings and Pima S-6 was for the first three plantings. Wilt tolerance was undoubtedly a factor in the pima cotton performance

Multivalency effects in Pseudomonas aeruginosa biofilm inhibition and dispersal by glycopeptide dendrimers targeting lectin LecA

The galactose specific lectin LecA partly mediates the formation of antibiotic resistant biofilms by Pseudomonas aeruginosa, an opportunistic pathogen causing lethal airways infections in immunocompromised and cystic fibrosis patients, suggesting that preventing LecA binding to natural saccharides might provide new opportunities for treatment. Here 8-fold (G3) and 16-fold (G4) galactosylated analogs of GalAG2, a tetravalent G2 glycopeptide dendrimer LecA ligand and P. aeruginosa biofilm inhibitor, were obtained by convergent chloroacetyl thioether (ClAc) ligation between 4-fold or 8-fold chloroacetylated dendrimer cores and digalactosylated dendritic arms. Hemagglutination inhibition, isothermal titration calorimetry and biofilm inhibition assays showed that G3 dendrimers bind LecA slightly better than their parent G2 dendrimers and induce complete biofilm inhibition and dispersal of P. aeruginosa biofilms, while G4 dendrimers show reduced binding and no biofilm inhibition. A binding model accounting for the observed saturation of glycopeptide dendrimer galactosyl groups and LecA binding sites is proposed based on the crystal structure of a G3 dendrimer LecA complex