Oracle is integrating generative AI capabilities into the entire OCI’s tech stack. In an exclusive interview with AIM, Vinod Mamtani, vice president and general manager of Generative AI Services at Oracle Cloud Infrastructure, revealed Oracle’s plans which include the general availability of generative AI services, the launch of generative AI agents, and the introduction of Data Science AI Quick Actions.
“With a common architecture for generative AI that is being integrated across the Oracle ecosystem, the company is bringing generative AI to where exabytes of customer data already reside, both in cloud data centres and on-premises environments,” said Ritu Jyoti, Group VP, IDC.
Notably the enterprise-friendly cloud service provider last year added generative AI services to several applications in the Oracle application suite which included HCM, SCM, CX, ERP, NetSuite, and a bunch of other industry vertical models. This time it is generative AI madness!
Integrates Llama 2
The recent surge in the popularity of Llama 2 hasn’t escaped OCI’s attention and it will now host Llama 2 with 70 billion parameters. Interestingly, this is the first time OCI is hosting models other than Cohere.
When asked what made OCI turn to Llama 2, Mamtani said, “Regarding Llama 2, we noticed the interest and uptake from developers, which is why we thought it could be quite useful for our customers.” He further added, “We want to add Llama 2 7B within six weeks, and then we have Cohere’s Command, Summarise, and Embed models”.
Moreover, in addition to hosting Llama 2, OCI will now host Cohere’s 52 billion embedded models. “If you look at the leaderboard, Cohere’s embedded models are ranked very high. It supports both English and multilingual language embeddings,” said Mamatani, explaining that embeddings generated will be in the same space, irrespective of the language used.
The OCI Generative AI service also offers flexible fine-tuning, available for Cohere’s Command 52/6B models through vanilla and TPU fine-tuning. Moreover, in order to make it easier for customers to build their AI applications, Oracle has integrated LangChain.
Bets on AI Agents
Enterprises have a vast corpus of documents. Since generic LLMs (like GPT-4 and Gemini) are trained on public datasets, they are unaware of all the information in these documents. To address this, OCI has introduced Retrieval Augmented Generation Agents to its OpenSearch. Users can now simply attach their documents to it and start chatting with it in natural language.
“It’s going to provide responses that are grounded, reducing hallucination. We are plugging in support for OpenSearch. Users can now transparently access diverse enterprise data sets through natural language without the need for specialist skills or to know the data’s format or storage location,” explained Mamtani.
Apart from the RAG agent, Oracle plans to introduce new AI agents. Upcoming releases will support a broader range of data search and aggregation tools and provide access to Oracle Database 23c with AI Vector Search and MySQL HeatWave with Vector Store.
Oracle will also deliver prebuilt agent actions across its suite of SaaS applications, including Oracle Fusion Cloud Applications Suite, Oracle NetSuite, and industry applications such as Oracle Health.
“There are these complex workflows that exist in our SaaS suite of apps. So, we want to automate and simplify that. And we will think of building agents that are very specialised for those applications.” said Mamtani.
He further said that Oracle plans to introduce a Code Gen Agent for Java applications. “Oracle acquired Sun Microsystems, so we would be best suited to have an agent for the Java language,” he added.
AI Quick Actions
Oracle also unveiled OCI Data Science AI Quick Actions, a no-code feature of the OCI Data Science service, which will enable access to a wide range of open-source LLMs, including options from Meta, Mistral AI, and more, simplifying the customisation of AI models for business-specific needs, reducing the need for specialist skills.
“We want to cater to a whole spectrum of data scientists and machine learning practitioners. There is a class of developers who want to try out and play with every other open-source model that’s out there,” concluded Mamtani, saying this feature would be available in the coming months.
