4 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
A Taxonomy of Everyday Grasps in Action*
Abstract-Grasping has been well studied in the robotics and human subjects literature, and numerous taxonomies have been developed to capture the range of grasps employed in work settings or everyday life. But how completely do these taxonomies capture grasping actions that we see every day? We asked two subjects to monitor every action that they performed with their hands during a typical day, as well as to roleplay actions important for self-care, rehabilitation, and various careers and then to classify all grasping actions using existing taxonomies. While our subjects were able to classify many grasps, they also found a collection of grasps that could not be classified. In addition, our subjects observed that single entries in the taxonomy captured not one grasp, but many. When we investigated, we found that these grasps were distinguished by features related to the grasping action, such as intended motion, force, and stiffness -properties also needed for robot control. We suggest a format for augmenting grasp taxonomies that includes features of motion, force, and stiffness using a language that can be understood and expressed by subjects with light training, as would be needed, for example, for annotating examples or coaching a robot. This paper describes our study, the results, and documents our annotated database
Media 2: High-speed three-dimensional profilometry for multiple objects with complex shapes
Originally published in Optics Express on 13 August 2012 (oe-20-17-19493
Media 1: High-speed three-dimensional profilometry for multiple objects with complex shapes
Originally published in Optics Express on 13 August 2012 (oe-20-17-19493