Making foundation models accessible: The battle between closed and open source AI

The massive explosion of generative AI models for text and image has been unavoidable lately. As these models become increasingly capable, “foundation model” is a relatively new term being tossed around. So what is a foundation model?

The term remains somewhat vague. Some define it by the number of parameters, and therefore, how large a neural network is, and others by the number of unique and hard tasks that the model can perform. Is making AI models larger and larger and the model’s ability to tackle multiple tasks really that exciting? If you take away all the hype and marketing language, what is truly exciting about these new generations of AI models is this: They fundamentally changed the way we interface with computers and data. Think about companies like Cohere, Covariant, Hebbia and You.com.

We’ve now entered a critical phase of AI where who gets to build and serve these powerful models has become an important discussion point, particularly as ethical issues begin to swirl, like who has a right to what data, whether models violate reasonable assumptions of privacy, whether consent for data usage is a factor, what constitutes “inappropriate behavior” and much more. With questions such as these on the table, it is reasonable to assume that those in control of AI models will perhaps be the most important decision-makers of our time.