Following this logic, why write anything at all? Shakespeare's sonnets are arrangements of existing words that were possible before he wrote them. Every mathematical proof, novel, piece of journalism is simply a configuration of symbols that existed in the space of all possible configurations. The fact that something could be generated doesn't negate its value when it is generated for a specific purpose, context, and audience.
Invented might be a bit strong, but he is certainly the first written record of the word. Dress existed as a verb already, as did the generic reversing “un”, but before Shakespeare there is no evidence that they were used this way. Prior to that other words/phrases, which probably still exist in use today, were used instead. Perhaps “disrobe” though the OED lists the first reference to that as only a decade before Taming Of The Shrew (the first written use of undress) was published, so there are presumably other options that were in common use before both.
It is definitely valid to say he popularised the use of the word, which may have been being used informally in small pockets for some time before.
Following that logic, we should publish all unique random orderings of words. I think there is a book about a library like that, but it is a great read and is not a regression to the mean of ideas.
Writing worth reading as a non-child surprises, challenges, teaches, and inspires. LLM writing tends towards the least surprising, worn out tropes that challenge only the patience and attention of the reader. The eager learner, however will tolerate that , so I suppose that I’ll give them teaching. They are great at children’s stories, where the goal is to rehearse and introduce tropes and moral lessons with archetypes, effectively teaching the listener the language of story.
FWIW I am not particularly a critic of AI and am engaged in AI related projects. I am quite sure that the breakthrough with transformer architecture will lead to the third industrial revolution, for better or for worse.
But there are some things we shouldn’t be using LLMs for.
RAG could largely be replaced with tool use to a search engine. You could keep some of the approach around indexing/embeddings/semantic search, but it just becomes another tool call to a separate system.
How would you feel about becoming an expert in something that is so in flux and might disappear? That might help give you your answer.
That said, there's a lot of comparatively low hanging fruit in LLM adjacent areas atm.
> How would you feel about becoming an expert in something that is so in flux and might disappear?
Isn't that true for almost every subject within computers though, except more generalized concepts like design/architecture, problem solving and more abstract skills? Say you learn whatever popular "Compile-to-JS" language (probably TypeScript today) or Kubernetes, there is always a risk it'll fade in popularity until not many people use it.
I'm not saying it's a problem, as said by someone who favors a language people constantly claim is "dying" or "disappearing" (Clojure), but more that this isn't exclusive to the LLM/ML space, it just seems to happen slightly faster in that ecosystem.
So instead, embrace change, go with what feels right and learn whatever seems interesting to you, some things stick around, others don't (like Coffeescript), hopefully you'll learn something even if it doesn't stick around.
I expect it will wind up like search engines where you either submit urls for indexing/inclusion or wait for a crawl to pick your information up.
Until the tech catches up it will have a stifling effect on progress toward and adoption of new things (which imo is pretty common of new/immature tech, eg how culture has more generally kind of stagnated since the early 2000s)
In a research context, it provides pointers, and keywords for further investigation. In a report-writing context it provides textual content.
Neither of these or the thousand other uses are worthless. Its when you expect working and complete work product that it's (subjectively, maybe) worthless but frankly aiming for that with current gen technology is a fool's errand.
I can write something with Microsoft tech and expect it with reasonable likelihood to work in 10 years (even their service-based stuff), but can't say the same about anything from Google.
That alone stops me/my org buying stuff from Google.
I'm not contending that Microsoft and Google are equivalent in this regard, I'm saying that Microsoft does have a history of releasing technologies and then letting them stagnate.
Imo the con is picking the metric that makes others look artificially bad when it doesn't seem to be all that different (at least on the surface)
> we use a stricter evaluation setting: a model is only considered to solve a question if it gets the answer right in four out of four attempts ("4/4 reliability"), not just one
This surely makes the other models post smaller numbers. I'd be curious how it stacks up if doing eg 1/1 attempt or 1/4 attempts.
Specifically within the last week, I have used Claude and Claude via cursor to:
- write some moderately complex powershell to perform a one-off process
- add typescript annotations to a random file in my org's codebase
- land a minor feature quickly in another codebase
- suggest libraries and write sample(ish) code to see what their rough use would look like to help choose between them for a future feature design
- provide text to fill out an extensive sales RFT spreadsheet based on notes and some RAG
- generat some very domain-specific realistic sounding test data (just naming)
- scaffold out some PowerPoint slides for a training session
There are likely others (LLMs have helped with research and in my personal life too)
All of these are things that I could do (and probably do better) but I have a young baby at the moment and the situation means that my focus windows are small and I'm time poor. With this workflow I'm achieving more than I was when I had fully uninterrupted time.
