GPT-3 cannot access non-public information

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Making Science , a digital acceleration company, launches Trust Generative AI, a generative artificial intelligence platform that solves the two main problems of systems like OpenAI: the inability to access non-public data from companies and the lack of updated information in generative models.

The launch comes at a time when generative artificial intelligence, based on large language models such as GPT and Bard, aims to transform the way people work , taking all companies to a higher level of automation and efficiency. A recent MIT study, which asked different professionals to use ChatGPT to write reports, press releases and other business documents, achieved savings of around 50% in writing reports and an improvement in their quality.

However, large language models such asfrom companies and also do not have recent information stored. In fact, GPT-4 is a language model that is trained with information up to September 2021 and does not have updated information after that date. “The availability of large language models and generative artificial intelligence opens up a world of possibilities that were unthinkable just two years ago. In this sense, Trust Generative AI is a solution that creates a bridge between private and recent company data and large models generated thailand number screening with public information. This will undoubtedly facilitate the adoption of generative AI by large companies,” explains José Antonio Martínez Aguilar , CEO of Making Science.

The platform, developed on Google Cloud Platform and OpenAI (ChatGPT-3.5 Turbo), optimizes both the data cycle and the training of artificial intelligence models , and validates the responses obtained through models developed on Google Cloud Platform on private customer data and more recent data that does not exist in models such as GPT-3 and GPT-4.

In this way, companies maintain full control over their data and are able to generate less generic content, more aligned with their policies, and style guides, all optimized with updated information.

An example of Generative AI’s efficiency is how Trust Generative AI generated 100,000 product descriptions for the Ventis.it website in minutes and at a cost more than 10,000 times lower than traditional generation. The generated descriptions were more than 95% aligned with other descriptions generated by the editorial department and were produced using “prompts” optimized by Trust Generative AI.

Trust Generative AI also has the ability to generate all types of content such as call center conversations, FAQs or emails, as well as images and other entities, connecting to services such as Google Cloud's Vertex Generative AI, DALL-E2 and Stable Diffusion.

In an environment of rapid technological change and uncertainty about the use of technology, it is very important to develop tools tha