Size Matters

For the past year, we’ve lived in a world overwhelmed by news of large AI, especially large language models like GPT, the model behind OpenAI’s ChatGPT. The general genius of large language models, however, comes at a cost—and that cost may not be worth it in plenty of use cases.

An abstract image of a small person on top of a tall mountain

For the past year, we’ve lived in a world overwhelmed by news of large AI, especially large language models like GPT, the model behind OpenAI’s ChatGPT. The narrative has been that generative AI models become more capable with increasing size and, eventually, these models will get big enough to exceed human intelligence. The general genius of large language models, however, comes at a cost—and that cost may not be worth it in plenty of use cases.

Think of all the times you might want help from a digital partner:

  • Understanding a specific concept or idea,
  • Breaking down a particular problem,
  • Planning for a unique project, or
  • Weaving together concepts in a novel way,

If you were seeking human help with these tasks, you’d likely seek someone who is an expert in that particular workflow. You would seek a physics expert to explain quantum mechanics or a go-to-market specialist to help prioritize channels for a product launch. Might you reach out to a hypothetical expert at everything? Sure. But, would you pay more for that ‘everything expert’ if the value of their input would be the same as the ‘specific expert’ for the task at hand?

This is one of the key challenges with large AI—will the value of large models make up for the increased cost to build and deploy? Or will people prefer small models that are cheaper and excel in specific workflows?

In contrast to generalized 'everything experts,' small AI presents three key advantages:

  • Expertise: Training models on specific datasets can create topic experts. For instance, Yale and Harvard’s CS50 chatbot was trained on course materials to make it an expert in computer science specifically for that level course.
  • Efficiency: Smaller models require less compute to train and deploy. While exact cost comparisons are difficult to come by, some research has shown that small models can cost 30 times than large models when providing responses.
  • Excellence: Smaller models may allow for increased value-add within workflows. For instance, a small model may excel in drafting content as part of a social media scheduling workflow. Or a small model may excel in providing financial evaluations within a bookkeeping workflow. Or a small model may excel in coding within a development platform.

This week, we’ve seen interesting advances in small models, including Microsoft’s Phi-2, Mistral’s Mixtral, and Google Nano. For instance, Phi-2 was trained on “textbook-quality” data including science, theory of mind, etc. (expertise) which resulted in a model with superior commonsense reasoning, math, and coding (excellence)—all while outperforming some models that are 25x larger (efficiency).

In these early stages of generative AI discovery and development, it’s important to remember that general purpose technologies are not deployed generically. Just as there is no one way to create and deploy a website, there will not be one way to create and deploy generative AI. We are particularly focused on the expertise, efficiency, and excellence advantages of small models because they may be the best way to weave AI into our daily lives—especially in use cases that we want to access on-device in our mobile-first world.

Note: Model cost and AI workflows are part of our ongoing research agenda for Artificiality Pro. Check out our latest update here and please contact us with any questions about Artificiality Pro individual or organization-wide subscriptions.

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