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What_The_Hex OP t1_j3a8bz8 wrote

I've seen a lot of memes lately about Chat GPT. Believe it or not, I'm testing it for this specific task, and holy fuck, it is absolutely nailing it. I can even go micro-specific with my requests, and ask stuff like: "Summarize this novel in less than 10 words", and it absolutely fucking nails it. VERY fucking cool!

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Bart-o-Man t1_j3agboz wrote

Yea, it gets even better.

Ask it to: Summarize quantum mechanics in a short Shakespearian Sonnet.

Or ask it to: Write a 10 paragraph screenplay in which people argue over which programming language is better: C++ or Python. And make the dialog rhyme while using words and sentences that sound like they are from southern Texas.

It's mindblowing.

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What_The_Hex OP t1_j3ah4z9 wrote

Absolutely bonkers what's possible these days. I see shit like this and I wonder how every computer programmer is not a millionaire XD

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Bart-o-Man t1_j3kowba wrote

LOL. No kidding. Bit then sometimes I look at it and get a little bit nervous, since they also write code!

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What_The_Hex OP t1_j3l3fe5 wrote

Dude it is just such a valuable tool. Sky's the fucking limit, truly. Computer programming + AI = like The PROMISED Land of just, massive, massive leverage and automation. Like, click one button, run one program, and you can move fucking mountains with what those two working in tandem are capable of.

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Bart-o-Man t1_j40r189 wrote

Yea, that's no exaggeration. The hardest part is continually remind yourself to keep trying new things to push it further.

I asked GitHub Copilot to write a couple of Python functions, and I was pretty impressed. I dont mean, "write a function to add two numbers" or "parse some text". I defined an Nx3 and an Mx3 Numpy matrices (a & b), told it (in comments) that a & b were two arrays of 3D points. I asked it to write a very fast function to compute all distances between pts in a and b, and return it. It did it immediately and the results were correct, so it's a start.

I had already written my own function to do it with vectorized numpy math (no slow loops), and optimized. I wanted to know which was faster.
The result is always a symmetric MxN matrix and diagonals are zero, so I knew my outer-difference MxN matrix had to be wasteful.

I benchmarked CoPilot's code against my own: their code was 3X faster in large matrix tests.

The second example: I told it I have a laser with 700 nm wavelength. I gave it some specs, like the diameter of the laser, an aperture size, and told CoPilot to write a function to compute and plot the laser image projection on a plane that was X mm away. It did it first try. It looks something like this image:

Yea... amazing is just the start.

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WikiSummarizerBot t1_j40r2n0 wrote

Diffraction

>Diffraction is defined as the interference or bending of waves around the corners of an obstacle or through an aperture into the region of geometrical shadow of the obstacle/aperture. The diffracting object or aperture effectively becomes a secondary source of the propagating wave. Italian scientist Francesco Maria Grimaldi coined the word diffraction and was the first to record accurate observations of the phenomenon in 1660. In classical physics, the diffraction phenomenon is described by the Huygens–Fresnel principle that treats each point in a propagating wavefront as a collection of individual spherical wavelets.

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