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Random insight of the night: every couple years, someone stands up and bemoans the fact that programming is still primarily done through the medium of text. And surely with all the power of modern graphical systems there must be a better way. But consider:

* the most powerful tool we have as humans for handling abstract concepts is language
* our brains have several hundred millenia of optimizations for processing language
* we have about 5 millenia of experimenting with ways to represent language outside our heads, using media (paper, parchment, clay, cave walls) that don't prejudice any particular form of representation at least in two dimensions
* the most wildly successful and enduring scheme we have stuck with over all that time is linear strings of symbols. Which is text.

So it is no great surprise that text is well adapted to our latest adventure in encoding and manipulating abstract concepts.

@rafial Both accurate and also misses the fact that Excel is REGULARLY misused for scientific calculations and near-programming level things since its GUI is so intuitive for doing math on things.

Like, GUI programming is HERE, we just don't want to admit it due to how embarrassing it is.

@Canageek very good point. Excel is actually the most widely used programming environment by far.

@rafial Now what we need to do is make a cheap, easy to use version of it that is designed for what scientists are using it for it. Column labels, semantic labels, faster calculations, better dealing with mid-sized data (tens of thousands of data point range), etc

@Canageek I'm wondering, given your professional leanings if you can comment on the use of "notebook" style programming systems such as Jupyter and of course Mathematica. Do you have experience with those? And if so how do they address those needs?

@urusan @rafial Whomever wrote this is an idiot that has never worked in actual physical sciences where you have actual physical experiments, just FYI. Like 10% of my labs works involves computers at all. But sure, science now moves at the speed of software.

@Canageek @rafial The attention grabbing headline is pretty stupid, but there's a lot of interesting content in the article. It's especially relevant in the fields I've worked in.

@urusan @rafial It totally ignores the advantages of PDF though, like the fact there are a stack of independent implementations that can view it, which means we will still be able to read these files in 50 years, unlike whatever format they are using, and we can even print them out on paper to edit them (for example, how my boss and I do it, as he doesn't know LaTeX, which is what I write in).

Or the fact that like, 90% of scientists don't know how to program and are unlikely to learn.

@Canageek @urusan @rafial > 90% of scientists don't know how to program and are unlikely to learn

Do you think that's stable, or does it really mean "90% of old scientists"?

I probably listen to biased sources, but my impression is that basically any field doing any amount of statistics these days does it in Jupyter.

If that counts as "know how to program", of course.

@clacke @urusan @rafial Well yeah, but most scientists don't do statistics. Most chemists, most biologists, geologists, etc.

Like, there is a reason computational is a subfeild of every discipline.

I think it is going up, due to more stats and computations being used, but I also think we are way to reliant on stats these days and use them instead of getting good data.

@Canageek @clacke @rafial Getting good data is a noble goal, but you'll always have to cope with statistical uncertainty in science.

Even in computer science, where we theoretically control the underlying systems we're studying perfectly, there's often still statistical uncertainty to deal with.

I don't see how that would be any better in the real world where there's uncertainty in measurement.

That said, you're right that you want to get good enough data that your statistics are simple.

@urusan @clacke @rafial Right, I've been frustrated with this in science for decades. We should do half as many studies and put twice or more as much funding into each one so we have actually decent stats.

For example, lately you have to justify the minimum number of rats for ethics committees for any experiment. Fuck that, use 4 times as many so we can be confident in our work instead of justifying it to heck and back.

@urusan @clacke @rafial (This is part of why I picked a field where all the stats were baked into software and validated before my Dad was born, and the sample sizes for everything else is around 10^26 (ie the number of atoms in solution)

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