i agree that there will be "early adopter" type use cases and others that might take a while (e.g. healthcare with hipaa compliance)
it is still the early days. goal is to give the developer tools to do this easier.
it is still the early days. goal is to give the developer tools to do this easier.
It's not the early days.
Not by a country mile.
To quote Cory Doctorow
> I don’t see any path from continuous improvements to the (admittedly impressive) ”machine learning” field that leads to a general AI any more than I can see a path from continuous improvements in horse-breeding that leads to an internal combustion engine.
You can counter it doesn't necessarily need an AGI here but that doesn't change the fact you can't crank this engine harder and expect it to power an airplane.
And, as always https://hachyderm.io/@inthehands/112006855076082650
> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.
> Alas, that does not remotely resemble how people are pitching this technology.
Similarly, but from my far-less notable-self in another discussion today:
> [H]uman exuberance is riding on the (questionable) idea that a really good text-correlation specialist can effectively impersonate a general AI.
> Even worse: Some people assume an exceptional text-specialist model will effectively meta-impersonate a generalist model impersonating a different kind of specialist!
therein lies the nuance. some people expect to get a natural language answer back. others expect to get a data table back. others expect to get correct SQL back. this is why it's so important to understand the use case and not bucket everything together.