TehPhysicalist
1 HN recommender · recommended in 5 months, 2015–2025
5:37 · 2.7M views · May 17, 2012
A short clip of Richard Feynman on the difference between merely knowing how to reason mathematically and understanding why physical theories work. He discusses holding multiple equivalent ideas and guessing new laws.
HN uses it as a reference in threads on physics theories, models and what real understanding means.
5 comments from Hacker News
You and the person's comment above you are missing the point. First watch : [LINK] (A.I and physics) are quite related and workable ^I reference the above video not because I watched it and thought (hey that makes sense). I reference it because I already felt the same in my work on A.I and felt (wow someone else gets it) Also : this video It's good to keep all sorts of working ideas/knowledge in your head when you're working towards solving a problem. All the physics experiments in the world aren't going to give you insight into problems unless you have workable ideas for how to integrate the findings into understanding/knowledge. What people often error in is that you can keep hacking away at and brute force your way to knowledge/understanding ... “A theory can be proved by experiment; but no path leads from experiment to the birth of a theory.” — Albert Einstein Ancient philosophers solved a lot of problems. More than you seem to be giving credit to. It's from their ideas that people knew where to look to 'prove' things. Watch the Feynman video and truly grasp what he is saying.
I think you're missing some key points - Good models are important While the traditional computer model is good at explaining behavior, it doesn't help us ask new questions. And no matter how well what we currently know brain fits the traditional computer model, if it can't be used to inspire new questions then we need to explore new models. Feynman explains this better [ this video ]. Even though our finite non-magic brains could fit in a computer, a computer isn't the best metaphor. - Computers don't act like brains by themselves. In any FUZZY LOGIC compiled and ran on a computer, all your code is translated into functions and data. LOSSY COMPRESSION is actually the application of experiments on perception, and not an inherent property of computers. A jpg is designed to look the same while using less space, but the information from it is not integrated in the same way as a human. Our MEMORY is not like a computer's. It is shockingly constructive, something that isn't expected with the computer model. - New programming paradigms might be better models. My beef with the metaphor is that is has very separate memory and computation. I don't think the brain "reads and writes from memory". I think it creates and updates a mesh of functions. Something like FUNCTIONAL PROGRAMMING could be used to model this. What if instead of looking for a neural hard drive, we treat each neuron as a function? What if generation of a dollar bill is actually OPTIMIZATION of our recognition function? W
There is always a tension in Physics between physical proof and observability on the one hand, and having the right idea, the right intuition on the other. Going back to my Einstein example, there were other Physicists who contributed to Relativity (Lorentz, Minkowski, Poincaré, Fitzgerald) but we give Einstein the most credit because he had the right idea, that it's not enough to treat the speed of light as a constant in your math, you actually have to believe the Universe works that way. One of the stories that gets told is the reason Einstein never got a Nobel prize for Relativity is because the Nobel committee just wasn't ready to give it up and credit Einstein with discovering a new, fundamental truth about the Universe. Another story that gets told is that when Einstein was an undergrad at the ETH in Zurich, Minkowski was one of his professors and he dismissed Einstein for being a "Lazy Dog" because the math never came easy to him. But he looked at how Physics was taught at the time and knew something had to change. And he was right about that. I'll just leave this here, Feynman explained it better than I ever will. this video
The pursuit of knowledge is not a linear path. The reason you benefit from deep learning now is because a few people in the past believed neural networks had a future despite not working as well as other techniques such as SVMs. Discovering knowledge and using the knowledge that works best are very different. Your argument remind me from this lecture from Feynman. Quoting him: "...and every theoretical physicist that is any good knows 6 or 7 different theoretical representations for exactly the same physics and knows that they are equivalent... but he keeps them in his head hoping that they'll give him different ideas for guessing." this video
This reminds me what Feynman talk to us: this video > If you can find any other view of the world which agrees over the entire range where things have already been observed, but disagrees somewhere else, you have made a great discovery. It is very nearly impossible, but not quite, to find any theory which agrees with experiments over the entire range in which all theories have been checked, and yet gives different consequences in some other range, even a theory whose different consequences do not turn out to agree with nature. A new idea is extremely difficult to think of. It takes a fantastic imagination.