Bryan Bischof: recommendation

Transcript context

…That's great. You obviously wrote a book on RecSys. What are some of the key concepts that maybe people that don't have a data science background, ML background should keep in mind as they work in this area? It's easy to first think these models are stochastic. They're unpredictable. Oh, well, what are we going to do? I think of this almost like gaseous type question of like, if you've got this entropy, where can you put the entropy? Where can you let it be entropic and where can you constrain it? And so what I want to say here is think about the cases where you need it to be really tightly constrained. So why are people so excited about function calling? Because function calling feels like a way to constrict it. Where can you let it be more gaseous? Well, maybe in the way that it talks about what it wants to do. Maybe for planning, if you're building agents and you want to do sort of something chain of thoughty. Well, that's a place where the entropy can happily live. When you're building applications of these models, I think it's really important as part of the problem framing to be super clear upfront. These are the things that can be entropic. These are the things that cannot be. These are the things that need to be super rigid and really, really aligned to a particular schema. We've had a lot of success in making specific the parts that need to be precise and tightly schemified, and that has really paid dividends. And so other analogies from data science that I think are very valuable is there's the sort of like human in the loop analogy, which has been around for quite a while. And I have gone on record a couple of times saying that like, I don't really love human in the loop. One of the things that I think we can learn from human in the loop is that the user is the best judge of what is good. And the user is pretty motivated to sort of like interact and give you kind of like additional nudges in the direction that you want. I think what I'd like to flip though, is instead of human in the loop, I'd like it to be AI in the loop. I'd rather center the user. I'd rather keep the user as the like core item at the center of this universe. And the AI is a tool. By switching that analogy a little bit, what it allows you to do is think about where are the places in which the user can reach for this as a tool, execute some task with this tool, and then go back to doing their workflow. It still gets this back and forth between things that computers are good at and things that humans are good at, which has been valuable in the human loop paradigm. But it allows us to be a little bit more, I would say, like the designers talk about like user-centered. And I think that's really powerful for AI applications. And it's one of the things that I've been trying really hard with Magic to make that feel like the workflow as the AI is right there. It's right where you're doing your work. It's ready for you anytime you need it. But ultimately you're in charge at all times and your workflow is what we care the most about. Awesome. Let's jump into lightning round. What's something that is not on your LinkedIn that you're passionate about or, you know, what's something you would give a TED talk on that is not work related?…

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