5 research outputs found
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
The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project
Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 µm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 µg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 µg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area