![]() This kind of platform would enhance the ability of teams to generate innovative solutions, improving the products and services that Thomson Reuters could offer its clients. Here, internal teams could not only explore and test the various LLMs developed in-house and those from the open-source community such as with the AWS and Hugging Face partnership, but also discover unique use cases by merging the capabilities of LLMs with Thomson Reuters’s extensive company data. Thomson Reuters’s objective was clear: to build a safe, secure, user-friendly platform-an “open arena”-as an enterprise-wide playground. This is all facilitated by the modular serverless AWS architecture that underpins the solution. The capabilities of Open Arena continue to grow as the experiences from employees across Thomson Reuters spur new ideas and as new trends emerge in the field of generative AI. Open Arena has been developed to get quick answers from several sets of corpora, such as for customer support agents, solutions to get quick answers from websites, solutions to summarize and verify points in a document, and much more. This provides non-programmatic access for Thomson Reuters employees who don’t have a background in coding but want to explore the art of the possible with generative AI at TR. Open Arena has helped unlock company-wide experimentation with generative AI in a safe and controlled environment.ĭiving deeper, Open Arena is a web-based playground that allows users to experiment with a growing set of tools enabled with LLMs. AWS-managed services such as AWS Lambda, Amazon DynamoDB, and Amazon SageMaker, as well as the pre-built Hugging Face Deep Learning Containers (DLCs), contributed to the pace of innovation. The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. In this post, we discuss how Thomson Reuters Labs created Open Arena, Thomson Reuters’s enterprise-wide large language model (LLM) playground that was developed in collaboration with AWS. During these sessions, ideas on how AI could be used started to surface as colleagues considered how to use tools that helped them use AI for their day-to-day tasks as well as serve their customers. Starting from foundational principles of what is AI and how does ML work, it’s delivering a rolling program of company-wide AI awareness sessions, including webinars, training materials, and panel discussions. Thomson Reuters is highly focused on driving awareness and understanding of AI among colleagues in every team and every business area. ![]() ![]() While Thomson Reuters pushes the boundaries of what generative AI and other technologies could do for the modern professional, how is it using the power of this technology for its own teams? The introduction of generative AI provides another opportunity for Thomson Reuters to work with customers and once again advance how they do their work, helping professionals draw insights and automate workflows, enabling them to focus their time where it matters most. Fast forward to 2023, and Thomson Reuters continues to define the future of professionals through rapid innovation, creative solutions, and powerful technology. This technology was one of the first of its kind, using NLP for more efficient and natural legal research. A key milestone was the launch of Westlaw Is Natural (WIN) in 1992. Thomson Reuters Labs, the company’s dedicated innovation team, has been integral to its pioneering work in AI and natural language processing (NLP). Thomson Reuters (TR), a global content and technology-driven company, has been using artificial intelligence (AI) and machine learning (ML) in its professional information products for decades. This post is cowritten by Shirsha Ray Chaudhuri, Harpreet Singh Baath, Rashmi B Pawar, and Palvika Bansal from Thomson Reuters.
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