3 posts tagged with "large language models"

In the last one year since OpenAI popularised generative AI, a lot of the excitement and intrigue have been centred on the generative power of large language models (LLMs) or foundational models. Understandably so. However, the generative power of this models only cover a segment of business needs. The underlying promise of these frontier models is in its ability to "understand" natural language, and that to me, is where we will find more utility.

For the last few years at Insum, we've had an almost biannual tradition of reviewing what's been going on in the AI/ML space, in particular, around what Oracle has been doing to provide innovative solutions and tools. If you missed those, I have provided the links below. In the absence of an updated review, I am hoping that this final 2023 blog post would serve somewhat as a stopgap.

In my previous post, I wrote about how a scikit-learn machine learning (ML) could be trained and deployed on the Oracle Cloud Infrastructure (OCI) Data Science service. The model is deployed on the service's managed infrastructure, allowing developers to simply call a HTTP endpoint to perform ML model inference on the submitted data. In my latest adventure, I built an Oracle APEX application that takes product reviews and then automatically ranks the review using a fine-tuned large language model (LLM) available through Hugging Face.

One of the key highlights at Oracle Cloudworld 2023 is general availability of Select AI feature that allows anyone to query the Oracle Database using natural language. This is likely made possible with a template wrapping the database metadata with the prompt, and then calling either the OpenAI or Cohere APIs to generate the intended text.