Continuing on the theme of artificial intelligence from the David Eagleman post…
Our team at Lizi, which is somewhat obsessed with these great notes on Peter Thiel’s class by Blake Masters, was talking about the class dedicated on AI recently. There are unquestionably great points raised in this talk:
It might still be too early for AI. There’s a reasonable case to be made there. We know that futures fail quite often. Supersonic airplanes of the ‘70s failed; they were too noisy and people complained. Handheld iPad-like devices from the ‘90s and smart phones from ’99 failed. Siri is probably still a bit too early today. So whether the timing is right for AI is very hard to know ex ante.
We can look at Siri today, and most of us would agree she’s a “parlor trick” at best. Siri can’t do most of the things that we want her to do, despite having one of the most powerful design and marketing muscles ever known in commercial history backing her up.
I am interested in the comparisons between searching for artificial intelligence and trying to get an airplane to fly in the early part of the 20th century. Again, here are some winning quotes:
Re: Airplanes in 1900… “People have been trying to build flying machines for hundreds of years and it’s never worked.” Even right before it did happen, many of the smartest people in the field were saying that heavier than air flying machines were physically impossible.
Scott Brown: Part of it is about about process. What enabled the Wright brothers to build the airplane wasn’t some secret formula that they come up with all of a sudden. It was rigorous adherence to doing carefully controlled experiments. They started small and built a kite. They figured out kite mechanics. Then they moved onto engineless gliders. And once they understood control mechanisms, they moved on. At the end of the process, they had a thing that flies. So the key is understanding why each piece is necessary at each stage, and then ultimately, how they fit together. Since the quality comes from process behind the outcome, the outcome will be hard to duplicate. Copying the Wright brothers’ kite or our vision system doesn’t tell you what experiments to run next to turn it into an airplane or thinking computer.
This approach, we’ve come to learn from the Lean Startup movement, is vital to discovering a business model that connects to real users who are willing to pay for the service (with money or attention). I think we’re all big believers in this movement of incrementally finding a business model. We think that the key to making AI commercially acceptable relies in getting people “ready” for it.
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