Asked to define continual learning and why today's agents are only 'pseudo' continual learners, Malde uses his family's printed road-trip maps as the analogy. It frames the pitch for his company, Trajectory.
…and deployment in one place, making that just dead simple for every single person to do, and then you can close the loop and basically now upload that model back to production, gather new traces. It suddenly is this magical paradigm that I think every single company is going to adopt very soon.
…and where you think the future is going. Maybe to start, you guys spent a lot of time thinking about continual learning. Can you define it for us? I think you've also said this field is in a state of pseudo-continual learning. What is real, and how will we know when we get to the real thing?
That's a great question. So, when I think about continual learning, I actually think the human brain is a really good model for where we should aspire to get to. Like, in some sense, we are true continual learners. We learn online, we are constantly updating, iterating. And I think the world we're in right now with agents and AI is kind of like you said, pseudo-continual learning. When I was growing up, my family used to do a bunch of road trips, right? And I don't know how you guys did things, but we would go to AAA back in the day, and like they would give us a bunch of these maps, they would like highlight everything, and then we'd go on our road trip. And if there's any point where we wanted to change our route, we'd have to either call AAA or like go to a physical location. And I feel like that's the world we're in right now, where we have these amazing agents, they're very intelligent, we have these mapping algorithms, but you can only consult offline a couple of places. And anytime you want to update your course, you have to just do an entire mass batch update. What the first goal and what we're building at trajectory is first the ability to just democratize that access, right? To even just…
That's a really good analogy. So we had recently we had Rich Sutton and David Silver on the show, and both of them kind of made the comment that continual learning isn't really possible with today's architectures. I'm curious in your point of view, what's doable? What's not doable? What's the state…
Yeah, so it's funny because when you think about a transformer and LLMs, I feel like a lot of people have seen maybe Carpathy's tiny GPT video or something where you basically have the stack of attention blocks and transformers that are built on top of each other. And a lot of LLM scaling has just…
Yeah. So how do you connect the dots to the real world? And how much of that is product or productizable? And how much of that is just handholding and helping people through the process?