November 2023 Summaries

The text discusses a multi-expert problem involving green energy companies, where data is fetched from Wiki API, calendar, and a database. It then computes the largest increase in share prices using a calculator and formats the answer with a language model. The process involves challenges such as training discrete experts, interfacing them with neural networks, routing among modules, etc. Further discussion includes advantages of Jurassic-X, like reading and updating databases in free language, enabling joining multiple databases, and updating databases using natural language commands.

The text discusses a multi-expert problem involving green energy companies, where data is fetched from Wiki API, calendar, and a database. It then computes the largest increase in share prices using a calculator and formats the answer with a language model. The process involves challenges such as training discrete experts, interfacing them with neural networks, routing among modules, etc. The text also mentions advantages of Jurassic-X, including reading and updating databases in free language, enabling joining multiple databases, and updating databases using natural language commands.

The text discusses a multi-expert problem that involves routing information from various sources such as Wiki API, calendar, and database. It then computes the largest increase in share prices by using a calculator and formats the answer using a language model. The process also includes challenges like training discrete experts, smoothing interfaces, and managing routing among modules. Further discussion highlights the advantages of Jurassic-X, such as reading and updating databases in free language, enabling joining multiple databases, and updating them with natural language commands.

AI21 Labs' Contextual Answers system, based on question-answering technology and large language models (LLMs), allows customer support teams to search their company's extensive knowledge bases for accurate, personalized solutions. This improves agent productivity, reduces handling times, and increases first-call resolution rates while enhancing customer satisfaction. The Contextual Answers system can be used internally or externally, with the option of building it into a company's website as a dynamic chatbot or refined search bar to directly answer common customer questions instantly and accurately.