Oracle, the provider of enterprise level database tools, has announced the release of Oracle Cloud Infrastructure (OCI) Generative AI services to help its users add generative AI to enterprise workflows. Specifically, the service manages and integrates large language models (LLMs) from Cohere and Meta Llama 2 into all of Oracle’s technologies to improve enterprise decision making. The service supports over 100 languages, improves GPU cluster management and can be fine-tuned to an organization’s needs. The OCI Generative AI service can even be used on-premises thanks to OCI Dedicated Region.
“Oracle’s AI focus is on solving real-world business use cases to enable widespread adoption in the enterprise,” said Greg Pavlik, senior vice president of AI and Data Management at Oracle Cloud Infrastructure, in a release. “To do this, we are embedding AI across all layers of the technology stack by integrating generative AI into our applications and converged database, and offering new LLMs and managed services—all supported by a fast and cost-effective AI infrastructure. Instead of providing a toolkit that requires assembling, we are offering a powerful suite of pre-built generative AI services and features that work together to help customers solve business problems smarter and faster.”
So, what does Pavlik mean when he says that AI will be embedded across all layers of Oracle’s technology? It means that via API calls organizations can securely generate text, automate tasks and embed generative AI into other tasks. Though the initial beta release will support OCI OpenSearch, future releases will support wider data search and aggregation tools with access to Oracle Database 23c, with AI Vector Search and MySQL HeatWave with Vector Store.
Perhaps the most exciting news is that Oracle will have prebuilt AI features across its suite of software-as-a-service (SaaS) offerings, including Oracle Fusion Cloud Application suite, Oracle NetSuite and industry applications like Oracle Health. Since Oracle Fusion Cloud applications is on that list, AI can be embedded into a vast number of engineering tools such as PLM, ERP, supply chain management and more.
Organizations can also refine the AI models using retrieval augmented generation tools. This enables the AI models to learn internal operations from an organization’s database.
Another interesting feature is that AI results will reference original data sources so that engineers can verify the findings. This can limit the phenomena known as AI hallucinations where similar tools convincingly lie to the user while obfuscating where the erroneous information is coming from.
“With today’s news, Oracle is bringing generative AI to customer workloads and their data—not asking customers to move their data to a separate vector database,” said Ritu Jyoti, group vice president of Worldwide Artificial Intelligence and Automation Research Practice and Global AI Research Lead at IDC. “With a common architecture for generative AI that is being integrated across the Oracle ecosystem from its Autonomous Database to Fusion SaaS applications, Oracle is bringing generative AI to where exabytes of customer data already reside, both in cloud data centers and on-premises environments. This greatly simplifies the process for organizations to deploy generative AI with their existing business operations.”