There seems to be a recent surge in the "HTML is/isn't a programming language" discussion.

While there are a lot of honest misconceptions and also outright bullshit, I still think if we allow for some nuance there is a meaningful discussion to have about it.

My two cents 👇
First, to be bluntly clear, if a person is using this argument to make a judgment of character, to imply that someone is lesser because of their knowledge (or lack of) about HTML or other skills of any nature, then that person is an asshole.

With that out the way...
Why is this discussion meaningful at all?

If you are newcomer to the dev world and you have some misconceptions about it, you can find yourself getting into compromises you're not yet ready for, or letting go options you could take.
I will argue that HTML is not a general-purpose programming language, for a very concise definition that I think most devs can agree with.

On the other hand, programming languages are but one part of the whole tech stack, and not necessarily the most relevant one at all times.
One argument that is often held is that HTML is not Turing-complete. That's true, but it's also unrelated.

LaTeX is also Turing-complete and I will argue that is not a programming language in the same sense as HTML.

So, why is HTML not a general-purpose programming language?
It has to do with the intended semantics of the language's building blocks.

A programming language describes *behaviour*, understood as the process of mutating data to compute a desired output from a given input.

Under this definition, HTML is not a programming language.
It's a matter of semantics.

HTML is not intended to describe behaviour, it's intended to describe content.

There is nuance, because the frontier between data and behaviour is fuzzy, and you can interpret data as behaviour, but again this requires agreeing upon the semantics.
On the other hand, there is *some* behaviour that is implied in HTML semantics.

Take for example the <form/> tag. It describes a behaviour involving an HTTP request to a given endpoint with a data payload.

But this behaviour is domain-specific, and for a quite narrow domain.
For these reasons I argue that HTML cannot be objectively considered a general-purpose programming language in any sensible definition of programming language.

That doesn't make it any less relevant, or the ones who master it any less skilled.
HTML is a fundamental component of the web, which is arguably one the most important inventions of the last century.

And HTML is tremendously complex to do right, as anyone who's ever tried can witness.

Anyone skilled in it deserves as much appreciation as with any other skill.

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More from @AlejandroPiad

2 Feb
This is a Twitter series on #FoundationsOfML. Today, I want to talk about another fundamental question:

❓ What makes a metric useful for Machine Learning?

Let's take a look at some common evaluation metrics and their most important caveats... 👇🧵
Remember our purpose is to find some optimal program P for solving a task T, by maximizing a performance metric M using some experience E.

We've already discussed different modeling paradigms and different types of experiences.

👉 But arguably, the most difficult design decision in any ML process is which evaluation metric(s) to use.
Read 25 tweets
31 Jan
One of the very interesting questions that really got me thinking yesterday (they all did to an important degree) was from @Jeande_d regarding how to balance between learning foundational/transferable skills vs focusing on specific tools.
@Jeande_d My reasoning was that one should try hard not to learn too much of a tool, because any tool will eventually disappear. But tools are crucial to be productive, so one should still learn enough to really take advantage of the unique features of that tool.
@Jeande_d One way I think you can try to hit that sweet spot is practice some sort of dropout regularization on your common tool set.

In every new project, substitute one of your usual tools for some convenient alternative. It will make you a bit less productive, to be sure...
Read 5 tweets
13 Jan
This is a Twitter series on #FoundationsOfML.

❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric M.

According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
Read 18 tweets
12 Jan
A big problem with social and political sciences is that they *look* so intuitive and approachable that literally everyone has an opinion.

If I say "this is how quantum entanglement works" almost no one will dare to reply.

But if I say "this is how content moderation works"...
And the thing is, there is huge amount of actual, solid science on almost any socially relevant topic, and most of us are as uninformed in that as we are on any dark corner of particle physics.

We just believe we can have an opinion, because the topic seems less objective.
So we are paying a huge disrespect to social scientists, who have to deal every day with the false notion that what they have been researching for years is something that anyone, thinking for maybe five minutes, can weigh in. This is of course nonsense.
Read 5 tweets
12 Jan
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.

❓ What is Machine Learning?

This is my preferred way of explaining it... 👇🧵
Machine Learning is a computational approach to problem-solving with four key ingredients:

1️⃣ A task to solve T
2️⃣ A performance metric M
3️⃣ A computer program P
4️⃣ A source of experience E
You have a Machine Learning solution when:

🔑 The performance of program P at task T, as measured by M, improves with access to the experience E.

That's it.

Now let's unpack it with a simple example 👇
Read 23 tweets
7 Jan
Today is #TheoryThursday 🧐!

The most important question in all of Computer Science is probably whether P equals NP. That is, are all problems easy to validate also easy to solve?

How can we even start to ponder this question? 🧵👇
🔖 In case you're just hearing about this, here's a short refresher on what we are talking about:



Let's think now about what would it mean to properly answer this question. 👇
Say we want to answer "yes". How can we do that? Do we have to check that *all* NP problems are actually in P?

👉 To begin, there are infinitely many problems in Computer Science.

There must a better way...
Read 20 tweets

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