PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

The rise of robots and the implications for military organizations

This thesis explores the reasons for the inevitability of the extensive use of robots in military organizations, projects the adoption timeframe for robots in military organizations, proposes how robots might evolve, assesses the impact of robots on military organizations and suggests the way forward for military organizations to facilitate the adoption of robots. Macro environmental trends suggest that the use of robots is the way forward for military organizations. The thesis projects that the adoption rate of robots will pick up from this point forward and will reach market saturation in a matter of decades. The use of robots has physical, functional, and behavioral implications for military organizations, and their increasing numbers will affect how militaries are organized and alter the existing organizational processes in the long term. Military organizations will benefit from a better understanding of the impact of robots and the resulting challenges. Taking the necessary steps to mitigate the challenges and facilitate the evolutionary transition for the military organizations will allow these organizations to reap the benefits of robots and to operate effectively in the changing macro environment.http://archive.org/details/theriseofrobotsn1094537662Captain, Singapore Armed ForcesApproved for public release; distribution is unlimited

Binder Jetting Additive Manufacturing of Hydroxyapatite Powders: Effects of Adhesives on Geometrical Accuracy and Green Compressive Strength

Binder jetting additive manufacturing (AM) is a promising process to print hydroxyapatite (HA) powder into bone tissue implants. However, one challenge remaining is the poor reactivity between HA powder with standard water-based ink. This study investigated different water-soluble adhesives to increase the 3D printability of HA powder. Maltodextrin and polyvinyl alcohol (PVOH) with low and high molecular weight (MW) were blended with HA from 10 to 30 wt.%. Powder characterisation and evaluation of the compressive properties and geometrical accuracy of the 3D printed scaffolds were performed to identify the optimal adhesive powder. This study adopted an image registration technique to quantify the geometrical accuracy of the final 3D printed scaffold in a more comprehensive and representative way than conventionally dimensional measurement. With these approaches, a highly promising binder jetting formulation has been developed via mixing HA powder with 30 wt.% PVOH (high MW). Samples manufactured from this formulation successfully achieved a geometrical accuracy greater than 85% and an excellent green compressive strength of 5.63 ± 0.27 MPa, which was 500% higher than the commercial binder jetting powder. This is the first study to demonstrate a high level of printability when using a formulation containing ≥70 wt.% HA powder and a water-based binder in the binder jetting AM process. Using the optimal powder composition developed in this study could potentially improve the structural, mechanical, and biological performances of HA-based 3D scaffolds manufactured using the binder jetting AM process for bone tissue engineering applications

Viable short-term directed energy weapon naval solutions: a systems analysis of current prototypes

With conventional weapons nearing their peak capability, the need to identify alternative war fighting solutions suggests a look at Directed Energy Weapons (DEWs). The goal is to change the means by which warfare is conducted to improve operational efficiencies and overall effectiveness. The Naval Postgraduate School Systems Engineering and Analysis (SEA-19B) Capstone project team examined how existing directed energy technologies can provide performance across multiple warfare area domains and mission subsets for the U.S. Navy. The aim was to identify and characterize the capability gaps with conventional weapons systems, produce a coherent vision of naval missions that incorporate DEWs, and generate a roadmap for a DEW fleet. By conducting a thorough Analysis of Alternatives based on system performance, integration, schedule, and cost, the project team identified that the Tactical Laser System (with a laser beam power of 10 kW) provided the best overall capability to defend surface combatants, although none of the analyzed DEWs have the capability to replace a current conventional weapon. The Active Denial System (microwave) provided a niche capability in the Anti-Terrorism/Force Protection mission set.http://archive.org/details/viableshorttermd1094534734Approved for public release; distribution is unlimited