Verdex_2

Hear hear. Bill Gates is trying to kill malaria. He doesn't care about having the biggest number next to his name when he dies. He wants to be the guy who's known for killing hunger in Africa.

For some reason, when I started my software engineering career I got it into my head that I needed to learn as much as I could about programming languages.

I learned ruby, perl5, python, lisp, forth, ml, ocaml, scheme, haskell, r, c#, java, lua, c++, factor, idris, asm, erlang, prolog, rust, d. But that wasn't quite enough because haskell and idris kept on talking about complicated type theory stuff. So I also learned lambda calculus, type theory, set theory, domain theory, topology, category theory, information theory, sub-structural logic.

What I'm trying to say is that I'm not afraid of learning new things. Even if they seem hard or esoteric.

When I heard that perl6 was ready, I took a look. I like the idea of a lot of what is present in the language (hey look, a grammar engine, that's neat). But ultimately, I decided that it was too much stuff that I would have to learn. Maybe that's just a perception problem on my part, but I have to think that they have some sort of problem if someone like me feels overwhelmed by all of the things that you have to grok in order to understand the language.

> So basically you're saying that it is a preferred solution to teach kids math poorly, let them develop insecurity and aversion towards it and then spent a lot of money to let them overcome it than just do it right in the first place? Yeah, I know that's not what you mean, but you're missing a point.

If you knew it's not what they meant then why did you start off saying that's what they meant? If you thought they were missing the point why didn't you just say that?

I really like type systems. I think that if you take the time to learn about type theory, then you are more likely to create better solutions (both with code and in life in general).

However, it isn't free. Type theory is a kind of math that most people have very little exposure to, so there's going to be a lot of work in order to start becoming proficient.

Additionally, there's more than one type of type theory. Are you using a system F like system? Are you going to have sub-typing? Is it going to be structural sub-typing? Maybe you want row polymorphism. Is there going to be a kind system? What about higher order poly kinds? Dependent typing? Intensional or extensional?

Additionally, there's more than one type of implementation of these type systems. Ocaml functors ... is it a generative or applicative functor? Haskell ... are you using gadts, functional dependencies, or type families?

In the end I think that type systems will eventually be able to get you a completely typed experience that feels exactly like a completely dynamic experience, but with compile and design time checks that always make you feel good about the experience. However, I don't think we are quite there yet and I don't think you can expect everyone to be able to take the time to get sufficiently familiar with an arbitrary type system in order to be productive with it.

I'm not sure, but I remember seeing some research into using it for Haskell stream fusion (can't find the video, sorry).

I believe that the basic idea was that not all rewrites end up being equally fast ( a * b * c can be fused into ab * c, but maybe a * bc is faster). Trying all the combinations is an option in theory, but you get a combinatorial explosion so you normally dont get far in practice. Enter machine learning. I'm not sure how successful they were, but I imagine that the same sort of thing could be applied to SIMD.

More or less. I'm suggesting a mechanism for why something like the Dunning Kruger effect might occur and where we are likely to see it occurring.

I spent a couple years figuring out a certain problem. When I was done, it took me six months to be able to write up a blog series about it. I don't think autism has anything to do with it. Some problems are really hard to communicate.

I'm 30% through writing a 10-20K word blog series about something that I've been working on for the last 6-8 years. I've probably written ~5K words already on blogs unrelated to my series where I'm kicking around the ideas that went into my project. Finally, the project probably isn't that interesting unless you've encountered a very specific type of problem before. Which means I probably should put in another 5-10K words to market it.

Sometimes it's not about confidence. Sometimes it's about complex social dynamics and how people react to suddenly being thrown into the deep end of a domain that's completely new to them.

"Tell me about a technical challenge that won't make me feel inadequate or be difficult to follow. Don't make it too simple though because then I'll think you're an idiot."

Ah yes, just be confident. That's the ticket.

Apparently washing your hands also causes you to be more lenient.

So AI probably shouldn't be used for sentencing, but that's more to do with all the stakeholders being clueless about the technology. The people asking for it, paying for it, using it, and building it (hey I can just throw some packages together in R right?) don't know what they're doing and are using it to significantly impact the lives of others.

On the other hand, pretending an arbitrary Judge / Jury / sentencing guidelines also don't form a dynamic system with not well understood effects that's equivalent to random AI in terms of output doesn't exactly help anyone either.

At the end of the day you need people who are honorable who you can trust to do the right thing (as much as it sounds like a saturday morning cartoon).

Cross pollination of disciplines also seems to bring out great ideas, which makes your (obviously first) proposal sound even better.

Unfortunately, I feel this is similar to that one dessert on the front page of the menu at an expensive restaurant. It looks delicious and tastes even better, but as soon as the bill comes you wondered what in the world you were thinking.

I would be really interested in someone elaborating on this idea "phd is an apprenticeship for researchers".

Several people have insisted that I should go into a phd program, but when I looked into it I saw a lot of: learn how to do grant applications, publish this non-reproducible and barely significant result just so you have something published, work on a phd adviser's bad idea so he can take the credit, etc.

If you're working on an idea no one has had before, what kinds of support can others give you and do modern phd programs give that support?