pacala

There are 4 mainstream programming styles:

* Data Oriented Programming. There is data, and there are functions operating on the data.

* Functional Programming. Variant of DOP, with a strong emphasis on purism and abstract algebra concepts.

* Procedural Programming. Variant of DOP, lacking language level mechanisms or best practices around polymorphic interfaces. Largely obsolete.

* Object Oriented Programming. Everything is an object.

Information hiding and polymorphism are readily available in all styles, except PP. The trouble with OOP is that insists on two big lies:

* Everything is an object, therefore data is an object, therefore data must be hidden. In fact, data simply 'is' and you can't hide it. Think JSON. There is nothing to hide.

* Everything is a polymorphic object, codified as the popular 'Open/Closed Principle'. In fact, most code is functions that just compute some data. Never needs N different polymorphic variants. If the business cases ever warrant such variants, simply refactor a polymorphic interface into the code base.

Make is a 44 year old implementation of the DAG model. It is not the only possible one, and it has plenty of flaws. At least in principle, there is room for a better implementation of the DAG model, and even different implementations tailored for different ecosystems.

The Mother of all Demos was amazing 5 decades ago. And yet we're not stuck using the exact same UI toolkit as Douglas Engelbart.

> we learned that covid may cause permament damages on patients

It stands to reason that a disease that outright kills 0.1-1% of the people infected also gets a fraction of people near death, which means it damages them hard enough that they almost die. This effect is entirely consistent with other diseases, for example non-covid19 pneumonia.

As of now we have not learned:

* What fraction of people develop long term effects.

* By age group.

* How do these effects compare with long term effects of other diseases, like influenza or influenza-triggered pneumonia.

* Do these effects heal over a period of time, say a few months?

The method itself, smashing ever more energetic particles into each other, makes it highly unlikely that any discovery will be applicable to human sized energy regimens.

If the countries you are referring to are South Korea, Taiwan and New Zealand, it may be that the rest of us have very little chance to follow suit. We are too interconnected and too diverse to counteract the exponential nature of the threat.

At some point we have to make peace with ourselves.

I've heard 'solar system size' and even 'galaxy size'. Maybe we should take a step back and realize that funding for high energy physics is rooted in the Cold War's nuclear arm's race. We already have nuclear devices more powerful than we'd ever need, not clear why we'd fund ever larger particle colliders.

The 'economy' is not some abstract entity. It is us, the people, going about our business every day. A hard lockdown is destroying all of the economy. No accounting tricks can mask that. We should not play accounting tricks to begin with. If we want to stoop at the article's level, note that the numbers are flawed, as they don't account for age distribution of the risk.

https://twitter.com/AndyBiotech/status/1241741127205572609/p...

Thanks for the thoughtful response. Wrt CT scans, the frustration is that we don't know the kind of selection biases led to the respective numbers. It's a couple of studies over non-random populations. 100 cases out of 3,711 passengers; 6 divers out of ??? divers out of 80M people in Germany. We got a couple numbers, how do we responsibly translate them into population-level risk assessments?

China is a big place. Imagine any possible negative health complications, we can safely assume that at least one person in China has developed it. The question is, what fraction of people have developed these complications? We don't even know what fraction of people developed the disease, or what fraction outright died...

I am worried about this disease. I am perhaps even more worried that I can't calibrate how worried I am supposed to be.

Case in point 'There will be hundreds of thousands of people worldwide, possibly more.' Taking 'hundreds of thousands' to be 500,000 and 'number of people worldwide' to be 8,000,000,000, we get 0.006% people with long term adverse side effects. This is much less than 1% we assume are going to outright die. Is this ballpark even remotely accurate? Do we have reliable clinical / epidemiological data to ballpark this number? Do models have any credibility left after being adapted on-the-fly by an order of magnitude or more?

As of the Grand Princess / 6 divers scenarios, something doesn't add up. Either 50% of people [in context, 4 billion] are going to end up with serious long term oxygen deficiency or not. If this is indeed the case, I don't understand why the medical community doesn't explain in more stern terms the severe [Black Death lite] long term implications. Given the implied severity, why don't we have a Manhattan project to replicate these studies, in every single developed country out there? Drop everything else you are doing, pick a 10,000 sized city with a sizeable outbreak and CT everyone in that city, repeatedly. Send the Army if you have to.

There is little hope that one day we could speak about the willful blindspots on both sides of such debates. Global warming is a major crisis with a hard tradeoff between two deeply negative outcomes: poverty now vs. climate disaster in the medium future. Judging by the covid19 response, humanity is unable to handle such tradeoffs with any degree of maturity. Society will polarize, each side never mentioning one half of the tradeoff and blaming the other side for all the negative impact of the other half of the tradeoff.

Of course there is some social aspect to CS. That being said, comparatively, the interaction loop is waaay more abstract. Just walk into a medical practice vs a software engineering office.

As a medical professional, you get to interact face to face with several people every day, and have a direct observable positive impact in their lives.

As a CS professional, you spend half your waking time dealing with or dishing out style guide feedback nitpicks via email / irc / github [FAANG experience], interacting remotely with funny little avatars. You very rarely directly experience the impact of your work, and the impact itself is quite a bit less clear cut positive.

That is an interesting question. How are law and medicine different than CS career-wise? A couple additional thoughts, not intended to be exhaustive:

* CS is notorious for its solitary aspect. Stare at a screen juggling abstractions for hours on end.

* CS is notorious for lack of career paths. Between ageism and outsourcing, once you hit 40, the perception is you are relegated to the bottom of the pile.

That is a fair criticism. The tone is off-putting. Partly because of the tone, I considered NKS a load of pointless drivel for 20 years. Yes, absolutely, he didn't invented graph automata. But he's the one willing to propose graph automata as a model of the Universe, and work out some technical details. Or, at least, he's building tooling to experiment with graph automata in this context such that someone else can work out technical details. Or fail to do so. Or fail to do so and prove this is a dead end.

While I'm not familiar with what kind of graph transforms work is done these days, I would have hoped that if someone re-created some version of Lorenz transforms on top of graph transforms, I would have heard of it. The discussion would be more constructive, or destructive as a manner of speaking, if someone would point out that either Stephen is wrong, or old news, on the handful of technical points he is making.

https://www.wolframphysics.org/technical-introduction/potent...

This is highly misleading for a disease that can exponentially explode from a handful of cases to a national emergency within a span of a few weeks.

If we could just freeze time, then we'd have perfect safety.

Stephen's work is intriguing. He wants a model to simulate space, time, matter and energy. Some desirable properties of such a system, from a layman perspective:

1. The model must be fundamentally simple.

2. The model must be able to produce 'interesting' behaviors somehow.

3. The model must exhibit very puzzling behaviors like 'speed of light is a constant', 'space dilation' or 'quantum superposition'.

He observes that extremely simple rewrite systems, like the infamous rule 30, satisfy 1 and 2, under a provable universality result: all 'interesting' behaviors are Turing equivalent. He then observes that the succession of states of the system could be observed as 'time', that having the fundamental simulation substrate create new nodes on the fly could be observed as 'space dilation', that the speed of generating new nodes is a limiting factor in the same way the speed of light is and that having alternate rules applicable in a given configurations could be observed as 'superposition'. This sounds promising. Let's work out the details. To my layman understanding, it's a fundamentally different approach than what physicists have been doing, extremely successfully, in the past 100+ years.

So far, he's empty handed, and very far from concrete results. He's pioneering the exploration of this space by building the first generation of programming languages, the machine code. Maybe 50 later, someone will build ZFS / explain the '3 but not 4 particle generations', using ML to express their thought process.

He's put some thought in attempting to bridge graph rewrite systems to the Universe's behavior. No measurable results, but no [layman's] obvious way to refute either. https://www.wolframphysics.org/technical-introduction/potent...

Everything you say is correct. Your acid test is great. And yet, we agree that the machine is stuck.

'Does your startup implement every single feature of Windows / Excel / Postgres / AWS and then some? No? Well, then go back to work and come back when you have something truly innovative.'

There is no charity. 'What you did is fairly incomplete, but the ground you covered so far is promising. Here's a list of challenges. Where would you start addressing them?' Maybe the answer will come 10 years down the road, in some University unaffiliated with the original author. In the current system, there is zero investigation of anything else but well trodden orthodoxy.

That's a very poor review. Given how long the incubation period is, plus the presence of asymptomatic carriers, it is impossible for the average person to accurately attribute the infection to a specific business.

Why not? Assuming NYT has free pages and I want to access one of them, what do they need a cookie for?

Exactly right. Building cowboy software with intense schedule pressure and best practices corner cutting it's likely to give you both late releases and bug backlog long enough to circle Earth three times.

There will be more bugs, and bugs will be more time-consuming to fix.

As other people mentioned, this is shortsighted. What you want to target is a balance between exploitation, 'bring value to the business', and process improvement, 'how do we simplify our processes to make it simpler to bring value to the business in the future'.

Both can be argued with concrete scenarios and data.

It doesn't need to have a perfect metaphor for all possible cases, just something you can picture in an instant: 'behaves like a concating variable length box sequences'. It's a mnemonic / learning aid, not a formal spec. But I can stretch it :)

+: concat marbles sequence

* : concat prime factors sequence

++: concat box sequence

For * , it can also be reduced to + via logarithms. I just don't have a good metaphor for '+ over reals' this morning.

concat?

I like the way you frame CT. Never got too deep down this rabbit hole, the terminology is just too atrocius. I wonder though how useful it is in practice. It is easier for me to take a formula and generalize it among one dimension at a time as the needs arise, see the catamorphism example above. As opposed to correctly divine 5-10-15 very specific concretizations of an uber-abstract scheme to arrive to the same result. Echoes of premature abstraction.

Perhaps the world lacks CT for dummies. Teach it in primary school. New Math was not aggressive enough ;)

Most of the jargon denotes straightforward definitions. Pick the top-most frequent three terms, e.g. monad, monoid, applicative, write down the definition + a small example on a card, lather rinse repeat a few times and you're golden.

Edit. For example, let's take 'catamorphism'. Read the Wikipedia entry [0] and it's a seemingly never ending jungle of more and more abstract terms. Homomorphism, initial algebra, F-algebra, endomporphism. This easy, let's see what an F-algebra is [1] and we'll be on our way soon. "If C is a category, and F: C → C is an endofunctor of C, then an F-algebra is a tuple (A, α), where A is an object of C and α is a C-morphism F(A) → A.". Oh my God, this is hopeless.

Or, find a good description, with examples [2]. A 'catamorphism' card, abbreviated from [2]:

Catamorphism

A function that “collapses” a recursive type into a new value based on its structure. Visitor pattern.

Define a sum type Gift:

    type Book = {title: string; price: decimal}
    
    type Gift =
        | Book of Book
        | Boxed of Gift

Define a 'description' function over Gift:

    let rec description gift =
        match gift with 
        | Book book -> 
            sprintf "'%s'" book.title 
        | Boxed innerGift -> 
            sprintf "%s in a box" (description innerGift) 
    

Parametrize the function, creating a 'catamorphism':

    let rec cataGift fBook fBox gift =
        match gift with 
        | Book book -> 
            fBook book
        | Boxed innerGift -> 
            let innerGiftResult = cataGift fBook fBox innerGift
            fBox innerGiftResult

Refactor your 'description' function to use a 'catamorphism':

    let descriptionUsingCata gift =
        let fBook (book:Book) = 
            sprintf "'%s'" book.title 
        let fBox innerText = 
            sprintf "%s in a box" innerText
        // call the catamorphism
        cataGift fBook fBox gift

[0] https://en.wikipedia.org/wiki/Catamorphism

[1] https://en.wikipedia.org/wiki/F-algebra

[2] https://fsharpforfunandprofit.com/posts/recursive-types-and-...

I'm on record in this very comment section of pointing out that the SARS-CoV-2 transmission paths is likely via a non-bat species traveling between Yunnan and Wuhan. That doesn't mean that WIV gets a cart blanche or that asking for transparency and independent verification of their activity is a 'rhetorical trick' or a 'conspiracy theory'. The impact is too large to leave stones unturned.

Your turn.

https://news.ycombinator.com/item?id=23072574

> As of your first point, there may be another natural virus, as of yet unidentified, possibly carried by a different species, that brought the disease to Wuhan. From the virologist chatter I hear, the genetic difference between RaTG13 is too large to have RaTG13 a recent direct precursor of SARS-CoV-2, either through genetic manipulation, or via culturing.

The bats from which RaTG13, the closest relative to SARS-CoV-2, was isolated, live in a cave in Yunnan, 1000 miles from Wuhan. To the best of my knowledge, they do not naturally occur anywhere near Wuhan. Pick your own North American animal that satisfies this constraint and answer the question.

Couple more points:

* Asking questions is a legitimate activity, even if the answer turns out to be negative. It's the fundamental mechanism for truth finding. I'm appalled, but not surprised, at the aggressive censorship strain that is spreading like wildfire in our society. Not only some people know all the correct answers, they know all the correct questions as well.

* Think hard about the wisdom of proliferating BSL4 centers in the middle of population centers of the size of NYC. That single fact is a scandal in itself.

Correct. The point is that the peasant mass center, so to speak, is today more distant that it's ever been.

Do you have a source for how many research labs in China were actively working on bat coronaviruses? From what I can garner in the Western press, WIV is their #1 such research lab.

If a new flu virus, quite more virulent that the usual flu, popped up in Atlanta 10 miles from the BSL4 facility and the closest known relative is endemic in polar bears, yeah, I'd be asking questions. Both about BSL4 and about the zoo. And maybe there are private citizens raising polar bears in Atlanta. I'd ask about them too. Especially if it were already known that polar bear viruses can be quite dangerous, see SARS episode 1.

But then again, these discussions are by now futile. It is obvious that the conversation has been politicized. One side will try its best to dismiss and discredit the slightest mention that there was ongoing bat coronavirus research in Wuhan, including WTF moments like creating chimera viruses for the specific purpose of infecting humans. The other will overhype all the scary innuendo that can possibly be created around WIV, whether it relates even remotely to SARS-CoV-2 or not.

I don't have the energy to elaborate the full decision tree in every post. Have fun!