Google Cloud's development platform, Vertex AI, is designed to help enterprises leverage Google's machine learning and large language models to build services. New features are being introduced to prevent the spread of inaccurate information by applications and services.
In addition to the Grounding with Google Search feature launched in May, Google has announced that customers can opt to use specialized third-party datasets to enhance the AI results of their services. These third-party datasets are provided by data vendors such as Moody's, MSCI, Thomson Reuters, and ZoomInfo, and are expected to be available in the third quarter of this year. Google is developing these new features to encourage organizations to adopt its "enterprise-ready" generative AI experiences, reducing the frequency of misleading or inaccurate model outputs.
Furthermore, a "High Fidelity Mode" has been introduced, allowing organizations to obtain generated outputs from their own enterprise datasets rather than from Gemini's extensive knowledge base. High Fidelity Mode is powered by a specialized version of Gemini1.5Flash and is currently available for preview through Vertex AI's Experiments tool. Organizations can also allow Google's AI models to draw information from their own company datasets.
Concurrently, Google has expanded the Vector Search feature to support hybrid searches, enabling users to find images by referencing similar graphics. This update is currently available for public preview and allows vector-based searches to be paired with keyword-based text searches to enhance accuracy. Grounding with Google Search will soon offer a "Dynamic Retrieval" feature, automatically selecting whether to fetch information from Gemini's established dataset or Google Search to address prompts that may require frequently updated resources.
Key Points:
⭐ Google introduces third-party dataset functionality to help improve AI results
⭐ Launches "High Fidelity Mode," enabling organizations to obtain generated outputs from their own datasets
⭐ Expands Vector Search feature to support hybrid searches, improving accuracy