Jason Droege: prediction

Transcript context

…And another example you shared is a short story where it's like, here's one short story, here's another I imagine generated by a model. And then it's like, which is better, and then how would you make it better? The other example you just shared is a Salesforce agent where it's like, Hey, book a meeting with a prospect and then teach it how that happens. I love just how concrete these are because it's like, okay, I get it. This is the stuff that these companies do. Is there another maybe one or two examples just to give people a sense of what this data looks like? Absolutely. I can actually give you an example from, so we have two sides of our business. One, we supply data to model builders. We sell the data, and then the other is we actually do solutions. We sell applications and services to healthcare systems, insurance systems, et cetera. I actually think it would paint a more colorful picture if I gave you an example of one of those because it involves data, but it involves the use of data, the manipulation of data for a very, very specific goal. And so, one example there is we work with a healthcare system and health systems have lots of problems. This particular healthcare system has experts that see very rare cases on a regular basis. So you go there only if no one else can figure out your problem, and there's a huge backlog. So there's a productivity element to this implementation tier. So there's a huge backlog. They want to be able to see more patients, they want to be able to provide better care, and they want to prevent the number of revisits because they want to give the accurate diagnosis day one and what the treatment should be. Well, to do this today without the help of AI, the doctor really needs to read 200 to 300 pages of documentation and it's rolled into one document, but in different formats. And so, if you're a doctor, how are you going to read 200 or 300 pages of everything? So what they do is they do the best they can. They scan it, they ask a nurse to look at it, they ask maybe a more junior doctor to take a look at this case. They want to treat the patient well, obviously this is why they became a doctor. And then, they go into the room and they talk to the person and then they make a diagnosis. Well, we basically built a tool that will read that document for them and point out the top 5 to 10 things that they should take into consideration, either allergies that might not be obvious is one example where we actually, we picked up on an allergy that a patient had that would not have been obvious from reading the document and that allergy actually would've had a conflict with the medication that they were going to be prescribed. And so, the AI tool basically pulled out this correlation that would've even been hard for a human being to do. To make this tool better and better, you get to a certain limit with the models off the shelf, and actually the people inside of this healthcare system have to do their own labeling. So we talk about labeling for model builders, but we are starting to see the labeling move into enterprises and into governments because you can only get so far with off the shelf plus rag plus some fine-tuning based on recorded data. One thing people often miss about these systems is we assume because you hear these numbers of like, "Oh, this bank in just 200 petabytes of data a year or whatever fantastical number." What we miss is is that the right data? Which of that data is useful to the models? And most of it is not useful. , "Oh, this bank in just 200 petabytes of data a year or whatever fantastical number." What we miss is is that the right data? Which of that data is useful to the models? And most of it is not useful. Some of it is, but a lot of what we do when we're talking about knowledge work, when we're talking about making judgment is human judgment based on synthesizing how would this doctor in this case or how would this banker in this case make this decision and how would they make decision in the context of their overall enterprise? And that might be different bank to bank, healthcare system to healthcare system, because of the culture, the objectives, the incentives, et cetera. And so, we're getting to the point now where we see that digitizing judgment, human judgment, true subject matter, deep expertise is becoming a bottleneck that we're unblocking for our customers.…

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