Explanation: “serverless” hosting platforms like Vercel and Netlify offer generous free tiers, with extremely expensive overage charges for bandwidth and processor time. When a small project suddenly goes viral, bills of tens of thousands dollars per day rack up.

Terminate all running children

Oof. Meme hits hard.

Bolive

Some people guide their behaviour by a sense of morality.

Others are guided by the threat of authority.

Trans people are literally just trying to get on with their lives while bigots obsess about them.

The same type of people said the same things about women getting the vote, interracial couples, and homosexuality.

I hope history continues to move in the right direction and leave these nosey fucks as nothing more than shameful memories.

When native English speakers complain that changing pronouns is too hard 🧐

That pup looks like they’re into craft beer and bands you won’t have heard of.

Just as a fun example of a really basic language model, here’s my phones predictive model answering your question. I put the starting tokens in brackets for illustration only, everything following is generated by choosing one of the three suggestions it gives me. I mostly chose the first but occasionally the second or third option because it has a tendency to get stuck in loops.

[We know LLMs are not intelligent because] they are not too expensive for them to be able to make it work for you and the other things that are you going to do.

Yeah it’s nonsense, but the main significant difference between this and an LLM is the size of the network and the quantity of data used to train it.

I’m possibly just vomiting something you already know here, but an important distinction is that the problem isn’t that ChatGPT is full of “incorrect data”, it’s that it is has no concept of correct or incorrect, and it doesn’t store any data in the sense we think of it.

It is a (large) language model (LLM) which does one thing, albeit incredibly well: output a token (a word or part of a word) based on the statistical probability of that token following the previous tokens, based on a statistical model generated from all the data used to train it.

It doesn’t know what a book is, nor does it have any memory of any titles of any books. It only has connections between token, scored by their statistical probability to follow each other.

It’s like a really advanced version of predictive texting, or the predictive algorithm that Google uses when you start typing a search.

If you ask it a question, it only starts to string together tokens which form an answer because the network has been trained on vast quantities of text which have a question-answer format. It doesn’t know it’s answering you, or even what a question is; it just outputs the most statistically probable token, appends it to your input, and then runs that loop.

Sometimes it outputs something accurate - perhaps because it encountered a particular book title enough times in the training data, that it is statistically probable that it will output it again; or perhaps because the title itself is statistically probable (e.g. the title “Voyage to the Stars Beyond” will be much more statistically likely than “Significantly Nine Crescent Unduly”, even if neither title actually existed in the training data.

Lots of the newer AI services put different LLMs together, along with other tools to control output and format input in a way which makes the response more predictable, or even which run a network request to look up additional data (more tokens) but the most significant part of the underlying tech is still fundamentally unable to conceptualise the notion of accuracy, let alone ensure they uphold it.

Maybe there will be another breakthrough in another area of AI research of which LLMs will form an important part, but the hype train has been running hard to categorise LLMs as AI, which is disingenuous. Theyre incredibly impressive non-intelligent automatic text generators.

Just to add some cool etymology to your reply: the word silhouette comes from a type of affordable portrait made by quickly painting or cutting out a persons profile in black paper. These, and portrait miniatures, fell quickly out of favour with the advent of photography.

The word silhouette is derived from the name of Étienne de Silhouette, a French finance minister who, in 1759, was forced by France’s credit crisis during the Seven Years’ War to impose severe economic demands upon the French people, particularly the wealthy.[3] Because of de Silhouette’s austere economies, his name became synonymous with anything done or made cheaply and so with these outline portraits.[4][5] Prior to the advent of photography, silhouette profiles cut from black card were the cheapest way of recording a person’s appearance.[6][7]

https://en.wikipedia.org/wiki/Silhouette

This is also an interesting article on the subject of pre-photographic portraiture: https://en.m.wikipedia.org/wiki/Portrait_miniature

I built my family tree and thought - oh neat, even more ancestors to disappoint.

Already we know we must lead all roads to this place.