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TR: Peter Drucker says, don’t solve problems, seize opportunities.

MEAD: Right. If Impinj had looked around and said, “Hey, let’s do RFID,” they would have ended up with a nonrewritable tag. Just like a dozen other companies out there now.

TR: RFID tags for Wal-Mart are a long way from trying to reverse-engineer computers from biological models…

MEAD: When you’ve finally got a product, the fact that you were inspired to go that way by thinking about touch and vision and hearing or whatever doesn’t matter much. You’re on to making products, and everything that led up to that falls away.

TR: That’s a little sad, no?

MEAD: Of course it is, but it’s what happens when you start a company. The unlimited potential of your new technology – it’s a huge high just thinking about it. But once it’s manifest, once it becomes a product, it’s not a myriad of anything; it’s one thing. So inevitably, there is a huge postpartum – a sense of all the things you weren’t able to do.

TR: Is that when you pull up stakes?

MEAD: It’s happened with every company I’ve worked with. They get to the point where they’re successful, they’re on a track, and there’s less and less that someone like me can contribute. You actually become a distraction: they’re trying to focus, and you’re wandering around thinking about all these interesting new questions. That’s when it’s time to leave.

TR: Some people think young technologists need to spend more time learning how to market their ideas.

MEAD: Science is not just about self-expression; you have to be able to explain what you’re doing. Dick Feynman was one of the best marketers I have ever met. He never wanted to admit it – in his day, anything entrepreneurial was socially unacceptable for an academic – but he was able to position physics as something exciting, in a way that has survived to this day.

TR: You and Feynman were behind a big neuromorphic-computing project launched at Caltech in the ’80s. What happened?

MEAD: Part of the problem was the refusal of the CS [computer science] community to have a new thought – the fact that there might be inherently more powerful ways to do computing. People said, “Everything’s a Turing machine, and that’s that.” No matter that we already have a working example of a massively parallel machine – the animal brain. And meanwhile, now, the quantum computing guys have come along and showed yet another alternative model – one that in theory will solve problems that are exponentially unsolvable by a Turing machine. I’m making no statement about the realization of quantum computers – we still don’t know about that. I’m just talking about our understanding of computing in the abstract. You need a fundamentally new conception of that if you want to try to make a better machine.

TR: Another neurally inspired company you’ve been involved with makes advanced hearing aids, Sonic Innovations.

MEAD: The thought process there came from thinking about how human hearing works, but again the actual device is just a little digital signal processor. The same thing happened with the idea of neural networks, by the way. They became just another algorithm for existing computers.

TR: What about Foveon, the camera company you founded in 1997? Most people probably don’t realize that its roots are in studies of the eye.

MEAD: We started out making models of the retina, which by itself might make a big difference to a few people, but it’s not enough of a commercial opportunity to justify big investment. What we realized was that if you took what we were doing and strip out the retina part, that’s a really good image sensor – so let’s do that. Foveon technology captures light directly, consuming less power and requiring far less processing than the file captured by a conventional digital camera. But when we explain it today, we don’t have any reference to anything neural.

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