Contextually aware suggestions for online information resources

Novice users often find it challenging to realize the full potential of available information resources. One approach to help such users is to present them with a categorized directory of ranked recommendations for useful information resources, such as apps or websites. However, such a directory may not help discover new resources that can be particularly relevant at specific times based on the user’s current location. This disclosure describes techniques to provide a personalized catalog of suggestions for information resources relevant to a user’s current context, which is obtained with user permission. A personalized catalog of contextual suggestions can help novices broaden their awareness and understanding of available online resources and expose them to a range of tasks that can be accomplished online. In addition to standalone operation, the catalog functionality can be embedded in various commonly used systems and can serve as a mechanism for entities to provide relevant information to individuals in their vicinity

Personalized catalog of categorized online information resources

Novice users often find it challenging to realize the full potential of available information resources. One approach to help overcome these challenges is to present users with a categorized directory of ranked recommendations for useful information resources, such as apps or websites. However, a single directory and categorization is unlikely to be equally relevant and useful for all users. This disclosure describes techniques to provide a continuously updated catalog of categorized information resources personalized to the user’s needs, expertise, and context. The techniques are implemented with user permission. For each resource within the catalog, the user is presented with associated useful information. The resources are presented within a common interface, thus making it unnecessary for the user to be aware of distinctions between different types of resources

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