We’re living in an exciting and transformational time for AI, but what most of us—even those in the machine learning and artificial intelligence industry—couldn’t have anticipated is how exponential AI’s progress has been. With the public introduction of generative artificial intelligence in text-to-image models like DALL-E 2 and Stable Diffusion, as well as the stunning linguistic versatility of large language model chatbots like OpenAI’s ChatGPT, we find ourselves not asking whether the AI revolution is just over the horizon, but rather if it hasn’t already arrived.

Reactions to these stunning new tools have run the gamut as well: from awe at the range of these models’ visual or textual outputs (and the speed at which they produce or refine them) to fear of an impending collapse of the creative industries and the potential peril they pose to the professionals working in them. Between it all, there’s also a fair bit of existential introspection: will creativity no longer be the sole province of humanity?

Incremental improvement with Exponential Tools

These are all vital questions, but they’re also too big to answer without time and extensive societal discourse—never mind that if AI’s exponential progress trend continues, the AI developments of today will be completely superseded by the AI developments of literally tomorrow.

With what we’ve already seen of generative AI and foundation large language models, we can at least extrapolate that these models will first be leveraged to improve upon the AI systems we’ve already deployed and are currently using—specifically, for language-specific tasks and services like chatbots—which should help keep disruption to a minimum. Regardless of whether future writers and artists are in trouble, all of us will still want to shop online, take e-learning courses, or even seek out the advice of professionals online like doctors, therapists, or lawyers, for example (though, there are some kinks that’ll need to be worked out as these pieces and OpenAI founder Sam Altman rightly point out). We’ll still need prompt and attentive customer service as a result. This is where language models that can produce accurate, understandable, and even empathetic text will be valuable.

Of course, this doesn’t mean that companies big and small will get into the business of developing and training their own proprietary large language models. There’s a reason why the OpenAIs (via Microsoft), Googles, and Metas of the world are the companies developing what has come to be known as “foundation models”: it’s because collecting the massive amounts of data and having the herculean compute to train these models from scratch is reserved for only the largest and best resourced of the tech giants. (There are also ethical concerns to the mass collection of data needed to train these models, which is a topic we’ll tackle in a future blog.)

The key lies in the “foundation” part of their name, as they will inevitably serve as the foundation for a plethora of tailored, individualized applications. Startups are already raising capital with their unique solutions powered by foundation models.

In much the same way that Defined.ai provides high-quality tailored data solutions for businesses looking to train or refine their own AI solutions—whether it’s for a specialized chatbot, sentiment analysis system, translation engine, or more—companies like OpenAI offer businesses access to foundation models that can be tailored to their specific needs. All that model licensees need to then capitalize on these powerful foundation models are specifically targeted datasets to tweak or retrain them for their own enterprises.

Example of different results of the same prompt in Stable Diffusion
For SD v.2, the model was given a much more diverse and wide-ranging dataset, which delivered a big jump in image quality when it came to architecture, interior design, wildlife, and landscape scenes.

Your business powered by Generative AI

Imagine your online storefront having a tireless customer service representative. It can address customers by name and give them all the answers they need while adopting a patient, understanding, and perhaps most importantly, emotionally responsive tone, guaranteeing customer satisfaction even when they’re understandably worked up over a serious issue. Or imagine your e-learning business catering to the learning level of each and every one of your clients. It will, explaining why literature like The Great Gatsby or The Grapes of Wrath are still relevant today to one student, while allowing another student to explore linked concepts in biology and chemistry at their 10th grade level. The possibilities for leveraging these foundation models to improve innovative enterprises are virtually endless.

As always, the value any business derives from AI lies in the data it uses to train and perfect them. At Defined.ai, we’re always expanding and adding new categories of data as quickly as the AI market dictates or as our clients demand them. The unique proposition that our AI Marketplace offers is not just high-quality, well-structured, and unbiased, balanced data, but data that’s been collected ethically—with the fully informed consent of contributors who are paid fairly for their data.

How can Defined.ai help your business leverage the awesome power unleashed by these new bleeding edge generative tools? Reach out today and let us know what you’d like to achieve—we’re confident we can source the expertise and the high-quality data needed to supercharge your business with generative and foundation AI models.