Smaller Language Models Could Define Generative AIs Role in Enterprises

Ever since OpenAI’s ChatGPT first demonstrated its abilities to navigate and respond to conversations with humans, researchers have been exploring ways to apply similar technology wholesale to computer-generated dialogue. This week, they unveiled a newChatGPT prototype that features an impressive ability to generate plausible dialogue based on large language models trained on trillions of examples of text. The results are stunning — the bot can convincingly respond in real time to questions and comments from humans, even if it has never seen them before.

If this is the Future of AI, then it seems we will have to get used to machines that are specialized in understanding only a few specific topics. This would benefit both the companies who employ these models and the general public, who could instead rest assured knowing that the machines are not trying to answer questions about things they don’t know or understand.

Data is the lifeblood of any company. In the AI-driven future, each company’s own data could be its most valuable asset. If you’re an insurance company, you have a completely different lexicon than a hospital, automotive company or a law firm, and when you combine that with your customer data and the full body of content across the organization, you have a language model. While perhaps it’s not large, as in the truly large language model sense, it would be just the model you need, granting one an advantage over their competition.

In order to create a powerful and accurate corporate dataset, it is necessary to collect, aggregate and constantly update the data in a way that makes it ingestible for these smaller large language models (sLLMs). By using tools such as natural language processing (NLP) engines, big data management platforms and algorithms, businesses can make sure their data is properly structured and ready for use by sLLMs.

One challenge would be in developing the models themselves – they will likely tap into something like open source or a private company’s existing LLMs and then fine-tune it on the industry or company data to bring it more into focus, all in a more secure environment than the generic LLM variety.

Startups are often focused on innovation and finding new ways to do things, but most of these companies rely on third-party solutions to get their work done. With so many companies relying on others, it is important for startups to find a solution of their own. This is where blockchain comes in; by using blockchain technology, startups can facilitate transactions and contracts without the need for a third party. This could give startups an edge over competitors who cannot offer this level of service.

Avatar photo
Dylan Williams

Dylan Williams is a multimedia storyteller with a background in video production and graphic design. He has a knack for finding and sharing unique and visually striking stories from around the world.

Articles: 874

Leave a Reply

Your email address will not be published. Required fields are marked *