You still need someone who understands why you should use which approach to get the data you need without getting completely wrong numbers back that _look_ perfectly fine but reflect fantasy, not reality.
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.
The truth is everyone knows LLMs can't tell correct from error, can't tell real from imagined, and cannot care.
The word "hallucinate" has been used to explain when an LLM gets things wrong, when it's equally applicable to when it gets things right.
Everyone thinks the hallucinations can be trained out, leaving only edge cases. But in reality, edge cases are often horror stories. And an LLM edge case isn't a known quantity for which, say, limits, tolerances and test suites can really do the job. Because there's nobody with domain skill saying, look, this is safe or viable within these limits.
All LLM products are built with the same intention: we can use this to replace real people or expertise that is expensive to develop, or sell it to companies on that basis.
If it goes wrong, they know the excited customer will invest an unbillable amount of time re-training the LLM or double-checking its output -- developing a new unnecessary, tangential skill or still spending time doing what the LLM was meant to replace.
But hopefully you only need a handful of such babysitters, right? And if it goes really wrong there are disclaimers and legal departments.
Our standards for AI are too high.
If an autonomous car causes one wreck per ten million miles, people set the cars on fire.
When someone finds an LLM that suggests eating a small rock every day, that anecdote is used to discredit all LLM results.
This shit makes errors. But what is the alternative? Human analysts who get joins wrong four times in ten? Human drivers who cause wrecks 30 times per ten million miles? Human social media recommendations about nutritional supplements?
Decisions should be made against an alternative, not against some fictitious perfect solution.
To me it seems a more risky use-case since you don't have control/observability over what an untrusted user is asking for?
Who is asking?
There is no requirement to learn SQL for most of the applications built today.
Of course there's a lot of incompetent people who have no idea what they're doing, if it seems to work they ship it. That leads to a lot of nonsensical bullshit and unnecessarily slow systems.
> There is no requirement to learn SQL for most of the applications built today.
In the same way that because Linked List libraries were invented 50 years ago, there is no requirement to learn what linked lists are for most of the applications built today?
You aren't getting past the requirement to learn relational databases "because ORM", and there is no material or course that teaches relational databases without teaching SQL.
The unfortunate result of this is that people who boast about knowing $ORM while not knowing SQL have never learned relational databases either.
Weirdly, I was just thinking about using an LLM to form sql queries for me, because I've forgotten much of what I knew. First time I had that thought and 5 minutes later, this fascinating idea rolls into my feed to pull me in further. I know I'm not exactly the target audience, but now I'm intrigued.
I went through a coding/design bootcamp a while back and there was virtually no focus on SQL, so a lot of my classmates were hesitant to jump into relational dbs for projects. I could see it being used in a tool for new devs or those who've focused on a JS stack and need some help with SQL.
Or they could buy a book like Learning SQL. Or spend a weekend on Youtube.
I agree that you should just learn SQL but that doesn't change the fact that a lot of companies want this right now. SQLAI claims to have hundreds of thousands of customers.
I’ve tried to teach SQL to PMs, bug triage specialists, etc. even a couple of days is too much time for them to learn something not critical or core to their job. Their alternative is to bug data teams with adhoc requests, which data people hate.
A tool like this would probably save 15% of a data teams time, and reduce the worst part of their job. At companies with hundreds, or even thousands, of data folks - that’s massive
And the users are smart people. They can read SQL to see if it looks like the right filters are applied. The “accuracy” issue exists but for certain use cases, it’s honestly not the biggest concern.
Not sure why the tone in this thread is so negative. To the founders, thank you!