Capital One Chief Scientist Prem Natarajan explains the financial institution's criteria for selecting and deploying open-source AI models.
Insight
Off-the-shelf AI fails because complex architectures require full-stack joint optimization
“We are in, in my mind, Past a system integration view of the world where you simply say, I bring this in here, I bring this in here, I tie them together and do it. I'll give you an example that's very old, ah, from speech recognition and machine translation, l…”
Insight
Enterprises must deeply customize AI models on proprietary data
“In order for you to truly bring all of that to life for your customers, you have to bring your own data to the models. In a way that you can do deep customization of those models so that you truly unlock the value in that data for the products and services tha…”
Insight
Baking static context directly into models outperforms relying on dynamic prompting
“One of the best ways to bring the right context to the models is actually to do a deep customization of the models. Not all context is dynamic. A lot of context is also, you know, changes at a much slower rate. That context being baked into the model makes the…”
Disclosure
Capital One's AI pilot success rate defies the 95% industry failure average
“That's about as far away from our experience as it could be. The, that study that, that would, that, that, that you're talking about.”
Assertion Not checkable as stated
Cloud GPUs lack elasticity and availability remains a persistent challenge
“GPUs, as you know, are still not elastic. In fact, they're often, you know, availability itself is, can be a challenge.”
Insight
Agentic AI combines reasoning with model specialization for complex workflows
“Agentic AI, at least the way we look at it, is the is the bringing together of two of the most powerful forces in generative AI today. One is the power of reasoning, and the other is the power of specialization, and so agentic AI uses reasoning to break comple…”