Tech Byte by Julia

How cloud computing technology contributes to sustainable computing practices ? Cloud computing has provided organizations in the tech industry with many benefits. Whether by helping to create a remote work environment or saving companies substantial costs, the cloud has transformed the ways businesses operate in the modern world. One of the major benefits of cloud computing technology is how it contributes to sustainability. The cloud reduces onsite activity associated with hardware and computing power consumption. Companies on the cloud do not need to maintain physical hardware or worry about disposing or recycling it. Cloud computing also eliminates the need to house and power an infrastructure. Not investing in physical IT equipment and consuming it as a service has environmental benefits because it helps to reduce the carbon footprint of major corporations. Below are some of the ways the cloud has helped contribute to sustainability efforts.    Reduces Energy Consumption  The Nati

        Introducing new generative AI capabilities for Google Cloud To help cloud users of all skill levels solve their everyday work challenges, we’re excited to announce  , a new generative AI-powered collaborator. Duet AI serves as your expert pair programmer and assists cloud users with contextual code completion, offering suggestions tuned to your code base, generating entire functions in real-time, and assisting you with code reviews and inspections. It can fundamentally transform the way cloud users of all skill sets build new experiences and is embedded across Google Cloud interfaces—within the integrated development environment (IDE), Google Cloud Console, and even chat.  For developers looking to create generative AI applications more simply and efficiently, we are also introducing  new foundation models and capabilities  across our Google Cloud AI products. And to continue to enable and inspire more customers and partners, we are opening up generative AI support in Vertex A

  FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance  Abstract There is a rapidly growing number of large language models (LLMs) that users can query for a fee.  We review the cost associated with querying popular LLM APIs—e.g. GPT-4, ChatGPT, J1-Jumbo—and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In particular, using LLMs on large collections of queries and text can be expensive. Motivated by this, we outline and discuss three types of strategies that users can exploit to reduce the inference cost associated with using LLMs: 1) prompt adaptation, 2) LLM approximation, and 3) LLM cascade. As an example, we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy. Our experiments show that FrugalGPT can match the performance of the best individual LLM

  Introducing PaLM 2   next generation language model. PaLM 2 is a state-of-the-art language model with improved multilingual, reasoning and coding capabilities. Multilinguality:  PaLM 2 is more heavily trained on multilingual text, spanning more than 100 languages. This has significantly improved its ability to understand, generate and translate nuanced text — including idioms, poems and riddles — across a wide variety of languages, a hard problem to solve. PaLM 2 also passes advanced language proficiency exams at the “mastery” level. Reasoning:  PaLM 2’s wide-ranging dataset includes scientific papers and web pages that contain mathematical expressions. As a result, it demonstrates improved capabilities in logic, common sense reasoning, and mathematics. Coding:  PaLM 2 was pre-trained on a large quantity of publicly available source code datasets. This means that it excels at popular programming languages like Python and JavaScript, but can also generate specialized code in languages