Building Generative AI We Can Trust
2022 will be remembered as an inflection point for generative AI — AI that doesn’t just classify or predict, but creates content of its own, be it text, imagery, video, or even executable code. And, it does so with a human-like command of language.
It was the year that we saw large foundation models (a deep learning algorithm that has been pre-trained with large data sets with a wide variety of data, which can transfer knowledge from one task to another) like ChatGPT, StableDiffusion, and Midjourney drive widespread attention by delivering capabilities that would have seemed like science fiction only years ago.
But despite their flashy output, these models’ most striking feature might be their sheer flexibility. Unlike even the most advanced machine learning models of the last decade, foundation models appear capable of producing truly incredible content and solving a staggering range of problems — from writing poetry and explaining physics, to solving riddles and painting pictures. And with additional training and human guidance, their applicability can be extended even further.
As a technologist and researcher, I’ve long believed that AI won’t just enhance the way we live, but transform it fundamentally. In particular, the more I explore Conversational AI, the more convinced I am that it will increasingly dissolve the correlation between a technology’s power and the expertise required to harness it. AI is placing tools of unprecedented power, flexibility, and even personalization into everyone’s hands, requiring little more than natural language to operate. They’ll assist us in many parts of our lives, taking on the role of superpowered collaborators.
For engineers, marketers, sales reps, and customer support specialists, the role of AI in day-to-day work will only grow in the coming years. At Salesforce, we’ve spent years embedding state-of-the-art AI within business applications spanning sales, service, marketing, and commerce, and today, our Customer 360 platform is generating more than 200 billion AI-powered predictions per day.
My role as Executive Vice President and Chief Scientist of Salesforce Research has given me a unique perspective on this. We operate within the world’s biggest companies, reaching billions of people in one form or another, and serve industries that touch every facet of society. That means everything we put in the hands of our customers has to offer mission-critical reliability as well — the kind that engenders lasting trust.
And while no one denies the power of generative AI as a whole, our ability to trust it is another matter entirely.
Generative AI promises an entirely new way to interact with machine intelligence, but it introduces what might be an entirely new kind of failure as well — confident failure. The poised, often professional tone these models exude when answering questions and fulfilling prompts make their hits all the more impressive, but it makes their misses downright dangerous. Even experts are routinely caught off guard by their powers of persuasion.
For example, in December 2022, researchers at the University of Chicago and Northwestern University used ChatGPT to generate abstracts based on titles taken from real articles in five medical journals. Then, when given a mix of original and fictitious abstracts in a blinded review, expert reviewers misidentified 32% of those generated by ChatGPT as originals and incorrectly identified 14% of the originals as being generated by ChatGPT.
This is a complex challenge, but it’s an easy one to understand. In a forthcoming paper, a team of cognitive scientists from MIT, UCLA, and UT Austin analyzed the models that power generative AI in terms analogous to the human brain.
