There’s been a bunch of exciting research-focused AI labs popping up in recent months, and Flapping Airplanes is one of the most interesting. Propelled by its young and curious founders, Flapping Airplanes is focused on finding less data-hungry ways to train AI. It’s a potential game-changer for the economics and capabilities of AI models — and with $180 million in seed funding, they’ll have plenty of runway to figure it out.
Last week, I spoke with the lab’s three co-founders — brothers Ben and Asher Spector, and Aidan Smith — about why this is an exciting moment to start a new AI lab and why they keep coming back to ideas about the human brain.
I want to start by asking, why now? Labs like OpenAI and DeepMind have spent so much on scaling their models. I’m sure the competition seems daunting. Why did this feel like a good moment to launch a foundation model company?
Ben: There’s just so much to do. So, the advances that we’ve gotten over the last five to ten years have been spectacular. We love the tools. We use them every day. But the question is, is this the whole universe of things that needs to happen? And we thought about it very carefully and our answer was no, there’s a lot more to do. In our case, we thought that the data efficiency problem was sort of really the key thing to go look at. The current frontier models are trained on the sum totality of human knowledge, and humans can obviously make do with an awful lot less. So there’s a big gap there, and it’s worth understanding.
What we’re doing is really a concentrated bet on three things. It’s a bet that this data efficiency problem is the important thing to be doing. Like, this is really a direction that is new and different and you can make progress on it. It’s a bet that this will be very commercially valuable and that will make the world a better place if we can do it. And it’s also a bet that’s sort of the right kind of team to do it is a creative and even in some ways inexperienced team that can go look at these problems again from the ground up.
Aidan: Yeah, absolutely. We don’t really see ourselves as competing with the other labs, because we think that we’re looking at just a very different set of problems. If you look at the human mind, it learns in an incredibly different way from transformers. And that’s not to say better, just very different. So we see these different trade offs. LLMs have an incredible ability to memorize, and draw on this great breadth of knowledge, but they can’t really pick up new skills very fast. It takes just rivers and rivers of data to adapt. And when you look inside the brain, you see that the algorithms that it uses are just fundamentally so different from gradient descent and some of the techniques that people use to train AI today. So that’s why we’re building a new guard of researchers to kind of address these problems and really think differently about the AI space.
Asher: This question is just so scientifically interesting: why are the systems that we have built that are intelligent also so different from what humans do? Where does this difference come from? How can we use knowledge of that difference to make better systems? But at the same time, I also think it’s actually very commercially viable and very good for the world. Lots of regimes that are really important are also highly data constrained, like robotics or scientific discovery. Even in enterprise applications, a model that’s a million times more data efficient is probably a million times easier to put into the economy. So for us, it was very exciting to take a fresh perspective on these approaches, and think, if we really had a model that’s vastly more data efficient, what could we do with it?
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