turingspiritfly

Why not The Princeton Companion to Mathematics?

Not really sure what the author thinks he got right about Zuckerberg

> “traditional masculinity — marked by stoicism, competitiveness, dominance and aggression — is, on the whole, harmful.

This really sounds like an attack on masculinity. One must never forget, the age of information with all it's flaws and good was brought upon the back of a structure who's main building block atleast in terms of values were masculine values which I think is independent of gender/sex

I think there hardly is any field out there where once the roadmap is mapped, the roadmap doesn't look long and convoluted. Overcoming being overwhelmed is something your relative should get a grip of from the get go, or atleast be aware of the expectation that one will likely be overwhelmed at every step. That and mapping out of minimal reqs to get started. Of course I understand the person is a beginner but the advice applies to every field

"the theory that would not die"

I didn't expect much, and don't think I was a fan of the first chapter because the story is pretty well known atleast in my circles. But it grows exponentially interesting as the chapters roll

https://yalebooks.yale.edu/book/9780300188226/theory-would-n...

Is it bad to have expected to the article to be all about how his alleged virtue help become the famed figure he is today? This was just another portrait of his work.

so in summary:

1. get something started

2. surround yourself with a good community

3. pace yourself

4. be realistic

The things I have learnt from Haskell

1. Functors, Monads, Applicative Functors, Monoids are extremely good design patterns, and to me seems like at some point you invent approximations of them if you're not using them

2. Raw recursion should be eschewed in favour of recursive schemes or atleast do folds

3. If Math is the language of clear thought, those closest to it like Haskell ought to be used where clarity is required which is pretty much everywhere in software

4. Composable software has to be based on something more concrete but one-time feel good look good pieces that fit together but a handful of cases, and if a language doesn't back it up with some sort of math structure, then the language is no good. That is why I think Category Theory's place in Haskell as something akin to a first class citizen is extremely important

Where do I use Haskell?

1. When I am not allowed to code in Haskell which is pretty much 99 percent of my projects, I re-do critical pieces in Haskell but Haskell code acts as a clear documentation and my go-to when trouble brews for trouble-shooting

2. The monadic abstractions are the best tools in my tool-set and I use them abundantly and it has paid off quite a bit

Haskell and the Future of software

1. Haskell is extremely difficult and since my first contact with Haskell almost 10 years ago, I've seen people turn away from it because of it seemed too complicated.

2. For those you thinking I am a nut for thinking that this sort of mentality spells doom for the future of software devs remember this, what is the general attitude of the public towards our best tool at modelling reality aka Math? Is it a good one? Heck half of the programming community is still at odds with Math. While you may argue that not everybody needs math, I argue the fact that everybody has this we-don't-need math mentality is the reason a many a things that should have been automated haven't been automated , and loads of people have these bs jobs where all they do is stitch together apis and then call it a day and go home and think they've accomplished something

3. My point: The current average relationship that devs have towards a language like Haskell and it's future descendents is/will be a metric of the state of software now and in the future.

> Recursion is a bad idea

Raw recursion or recursion using recursive schemes?

Those massive gains have yet to considered reliable enough to be considered trusthworthy. Would you consider them trusthworthy in court, where lives are at stake? Gains are nice but we are still so far from the essence of AI systems and considering how much resources we are pouring into learning, at this point all of them appear as nothing more than massive fat expensive toys

So this is the part I don't get, re: "What's pushing us to go to Mars?", some time ago someone said the same of going to the Moon. There was no good main reason for going to the moon, other than the US and the USSR having who's got a bigger dk war. The reality I feel, is this time we do have a good reason, to actually kick start off-planet habitable zones that we'll reap the benefits years and years into the future

https://link.springer.com/chapter/10.1007%2F11494645_21 seemingly states ODEs can simulate turing machines. Is the simulation enough proof ofit's turing completeness, and in what sense can PDEs be called a language?

Sometimes I feel, when papers that have grand speculations contained in them, should be made available to the public without the author(s) name tacked on it, and only be released after the public comes to a consensus. Why? science is about science not the people involved, high credential folks low credential folks don't matter. Just because someone is a high-cred scientist doesn't make his speculation any more worthy than a jackass's just because his credentials somehow factor in, it leads to more often than not, utter bs. Case in point: Linus Pauling and Vitamin C

Doesn't Simon Marlow explain the difference very well? Concurrency is implementation detail and parallelism is an execution detail?

Pretty cool. Out of curiosity, who stashes these books (the hardcopy version) on their bookshelves and why? Am very interested to know

Bayesian updates to beliefs