Richard Feynman Computer Science Lecture - Hardware, Software and Heuristics

Sorry I am pasting my old comment here, but the intention is same Before learning programming one should know what is computing in general? It sets good mental model, after that you can easily pickup and start writing program yourself . Data, data, data :))) Some basic notions to know: Input → Computation → Output Information is omnipresent (this is just an intuition, not a claim). It serves as both input and output. Computation—also known as a procedure, function, set of instructions, transformation, method, algorithm, or calculation. In my early days, I ignored the fundamental notion of data and procedures. But eventually, it clicked: Programs = Data + Instructions Watch Feynman on computing—he even starts with the same concept of data, introducing computers as information processing systems. And processing requires algorithms (i.e., instructions or procedures). Programming is simply writing instructions for the computer to perform computations. A computer is just a machine for computing. Computation is a general idea: a transformation of one form of information into another. Richard Feynman Computer Science Lecture: [this video] Old documentry on programming: [link] George Hotz video: what is programming? [link] [link] [link]

Sorry I am pasting my old comment here, but the intention is same Before learning programming one should know what is computing in general? It sets good mental model, after that you can easily pickup and start writing program yourself . Data, data, data :))) Some basic notions to know: Input → Computation → Output Information is omnipresent (this is just an intuition, not a claim). It serves as both input and output. Computation—also known as a procedure, function, set of instructions, transformation, method, algorithm, or calculation. In my early days, I ignored the fundamental notion of data and procedures. But eventually, it clicked: Programs = Data + Instructions Watch Feynman on computing—he even starts with the same concept of data, introducing computers as information processing systems. And processing requires algorithms (i.e., instructions or procedures). Programming is simply writing instructions for the computer to perform computations. A computer is just a machine for computing. Computation is a general idea: a transformation of one form of information into another. [link] Richard Feynman Computer Science Lecture: [this video] Old documentry on programming: [link] George Hotz video: what is programming? [link] [link] [link] [link]

Good catch re: 'late antiquity' -> I should have been a bit more rigorous in my research there. I like your point about "how little knowledge is a perilous thing?" Completely agree that programming is far from the only important skill. I just generally think that demystifying what is popularly considered to be "esoteric" knowledge is almost always good for a healthy society. The less we know about how the mysterious machines work, the more we fear them and the more trust and power we give to the people who can manipulate them. I think this is hogwash and completely unnecessary since a computer is really just a super-fast filing clerk [ [this video] ].

Sure. I don't have anything to link on the spot but this was/is/has been foreseeable for some time. Although it's all very cool and shiny - most business applications of machine learning remain squarely in the territory of classic algos like GLM & forests (random, boosted trees etc. etc.). As a fun note, advances like these highlight that data scientists etc. will not be beaten by more complex automated methods, but simply by speed. Much like the filing system that 'runs' whatever you're using to see these words ( [this video] ). Edit: to elaborate... single model training runs are possible to do quite fast now, but knowing how to tune hyper parameters remains the 'voodoo' of the field. But the best hyper params are also possible to discover through brute force: try every combination you can! Today, you can use various heuristics to improve this process, but either way, being able to train whatever X times faster just means we can search hyper parameter space that much faster. The robots are coming :)

What's fascinating to me is that index cards are a kind of spiritual predecessor to modern database systems and computers - perhaps even more closely related than counting devices like the abacus. Richard Feynman touches on this in one of his lectures [1] that's been linked many times on HN. The theory of information and computing seems pretty fundamental, and not necessarily tied to what we typically think of as a computer, with CPUs, RAM, SSDs, etc. In a way, a card catalog full of index cards and run by a bunch of people is a computer too. Maybe this isn't an incredible revelation, but it's still interesting to think about. [1] [this video]

"One of the miseries of life is that everyone names everything a litte bit wrong, and so it makes everything a little harder to understand in the world than it would be if it were named differently. A computer does not primarily compute in the sense of doing arithmetic. Strange. Although they call them computers, that's not what they primarily do. They primarily are filing systems. People in the computer business say they're not really computers, they are "data handlers". All right. That's nice. Data handlers would have been a better name because it gives a better idea of the idea of a filing system." Richard Feynman [this video]

Excellent work, glinscott. Thank you for sharing! I am about to send this to all my pals who are parents to schoolkids (um, and also pals who are still, like me, basically a schoolkid :p XD). Building things up from first principles, and ideally also tactile / visceral examples, is my absolute favourite genre of explanations. Alas, a rarefied genre, because it is so much harder to do well (without doing disservice to the learner's intellect and ability) than expert talking to (presumed) expert. So thanks for contributing to it. People like George Polya [0], Richard Feynman [1], Grace Hopper [2] are exemplars of this tradition. [0] [link] [1] [this video] [2] [link] (And I too try in my own meagre way through my blog and source code, albeit it's all plain text because visual explainers, animated ones that too are ridiculously difficult. So I have some idea of how much of a labour of love your piece is. <Claps hands, Whistles, Doffs hat>.)