Google Vertex AI Search Add News GenAI Capabilities And Enterprise-Ready Features 

Generative AI (GenAI) has ushered in a new era of interactive and multimodal experience for developers, businesses, and governments. For GenAI to achieve its potential, it needs to be easily accessible and integrated into a range of applications so users with little machine learning expertise can develop and deploy intelligence apps. 

To achieve this goal, Google had earlier unveiled its vision for the Vertex AI Search and made it generally available in August. The company has now announced new GenAI updates to Google Cloud’s Vertex AI Search. The enhancements will empower customers to get their applications off the ground faster, increase productivity, uncover hidden insights across data, and drive greater user satisfaction. 

Google Vertex AI Search provides customers with a unified platform to manage end-to-end ML workflows, use and fine-tune pre-build models, and harness the power of Google Cloud’s infrastructure to scale resources. 

The recently announced beta launch of Adelaide by Forbes is an example of the value offered by Google Vertex AI. Adelaide is a GenAI search platform that combines Forbes trusted journalism with AI-driven personalized recommendations. The search and conversation capabilities offered by Google Vertex AI help make content discovery easier and more intuitive for Forbes audiences. 

“As we look to the future, we are enabling our audiences to better understand how AI can be a tool for good and enhance their lives," said Vadim Supitskiy, Chief Digital and Information Officer, Forbes. "Adelaide is poised to revolutionize how Forbes audiences engage with news and media content, offering a more personalized and insightful experience from start to finish.”

Customizable Answers and Search Tuning

The new GenAI capabilities include tailored search to fit business needs, especially for large enterprises that need highly customized AI-driven search. Developers now have more control over the prompts including the length, tone, style, and format of the information presented. In addition, they can let the users customize the output through different options presented through a drop-down menu. For example, users can choose to view a “standard” or “simple” prompt. 

Along with enhanced capabilities for building apps, Vertex AI Search now allows users to use their own data for search to boost the accuracy of results. Even small training sets can allow Google Vertex AI Search to refine its rankings and deliver better search experiences. There is even an option for users to build their own GenAI applications for more complex use cases using Vertex AI Embeddings. 

Vertex AI Embeddings 

Vertex AI now offers a set of embedding models to support use cases for semantic search, classification, outlier detection, and recommendations. The embeddings can be uploaded to Vector Search to pair with other Vertex AI foundation models and services to power predictive and generative AI applications. 

Vertex AI’s Text Embeddings and Multimodal Embeddings (supporting Text and image) models are both generally available, and new Multimodal Embeddings models that support text, image, and now video are being announced in preview.  

Vertex Search 

The Vertex Search, formerly known as Machine Machine Matching, can index data as vector embeddings to find the most relevant embeddings at scale. It uses a search algorithm called approximate nearest neighbor (ANN) that can handle high throughput while providing high recall at low latency. The UI has also been updated to minimize the need for any coding. In addition, the index time has been reduced and filter capabilities have been enhanced. 

Grounding Data 

A key challenge for enterprises that use GenAI is that foundation models can perceive objects or patterns that are imperceptible to human observers, and this creates inaccurate outputs. To help prevent this “AI hallucination”, Vertex AI Search offers multiple options to ground data. 

With its new capabilities, Vertex AI Search offers enterprises a method for grounding in their own enterprise data to help users verify and validate results across disparate data sources. They can also use Vertex AI Connectors to expand data sources to other enterprise applications. There is also a new option for users to leverage wide sources of information for discovery needs to minimize the time and effort required for searching multiple sources for the same data. 

Compliance-First Search 

Google Vertex AI supports a range of compliance and security standards including ISO 27000-series, HIPAA, and SOC-1/2/3. These standards help ensure the integrity, transparency, confidentiality, and accountability of your data. 

Google has now announced expanding support for access transparency to help enhance visibility and control over your cloud provider with admin access logs and approval controls. It will also provide customers with better awareness of Google administers access to their data.  The customer-managed encryption keys (CMEK) are now available in preview. This new capability allows customers to encrypt their core content with their own encryption keys. 

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