41–50 of 171 posts
I doubt it's useful for complex queries or databases without proper relation info in the database schemes. So it's limited to rather simple queries for users without SQL knowledge. But I doubt they should have direct access to the database tables.
And you’re right in that the end user wouldn’t have access to the schema, although perhaps via prompt injection they could.
Earlier quoted context omitted.
do you not trust the setting OAI provides to exclude your conversation from training data?
Your average SQL query is worthless, there no special sauce in SQL queries, their value comes from the dataset they run on. These small SQL LLMs are indeed worthless, since LLM performance on SQL queries is quite limited right now, every % of accuracy matters.
0 employment contracts or user facing ToS I've read make a distinction between sharing schema vs data with unvetted third parties. In my opinion, schemas reveal pretty valuable info about an application (and they're absolutely valuable in aggregate because you can use them to train AI data scientists).
Maybe I sound like a curmudgeon but since I have the option of running the AI locally with a 5 percentage point accuracy loss, I absolutely will. If GPT-4 was 100% that would be different because you could build totally different things, but 83% has most of the same design problems as 78%.
Earlier quoted context omitted.
1) OpenAI has consistently gone back on commitments it has made 2) Sam Altman has a shady track record publicly, and if you believe the things people say privately he has consistently done business very dishonestly throughout his career. He is the CEO, and virtually the entire executive team are people he brought in from his network. It’s his company. 3) To give one example of many, OpenAI recently changed the terms…
Hm. I disagree that executive level shenanigans translate to real world non-compliance with privacy law when there is an explicit request to delete/not store data.
This is a big improvement, but I'm not a believer that SQL is the most appropriate query lang here. Personally am more bullish on language models being trained with ORMs, as those normally capture much more information about the fields. e.g. Passing in some of my more complex table schemas related to flight data and asking about overflights, the model struggles to resolve out information related to aviation. However,…
When you're on a higher abstraction level, it also allows you to make clear definitions (e.g. for certain KPIs) and define business logic that always needs to be applied to get the correct results.
There you don't want to leave it up to chance that a filter gets hallucinated in or out when you ask e.g. about your company's revenue.
At Veezoo (https://www.veezoo.com) we have taken the approach that instead of going directly to SQL. So when a user asks a question, Veezoo translates it first into a query against the Knowledge Graph (which represents the business objects, their relationship etc.). From there we compile it into a SQL query depending on the target database (they all have slight differences) without any AI involvement. In this compilation step we also make sure that the business logic is properly applied.
So it looks like it scores 76.5% on SQL-Eval [0], a bit behind GPT-4 at 83% and sqlcoder-15b at 78%. What kind of applications would this be useful for? What can you build with an AI data science intern that's right 75% of the time? As a programmer who always has to look stuff up when I SQL, I could definitely see asking something like this for a first draft of a query but it seems like I'm slightly better off asking…
1. If you are a a programmer I think you should learn SQL. It will give you a different perspective on programming that I think is invaluable. (I.e. programming without for loops) 2. Combining and slicing data is a craft, and doing it subtly wrong in one step can lead to fatal errors in the outcome. And most importantly, it can be very difficult to notice. Numbers don't smell. That is why I would be very hesitant to…
Ever heard of LATERAL joins/CROSS APPLY?
SELECT loop.value, x.squared
FROM generate_series(1,5) AS loop(value)
CROSS JOIN LATERAL (SELECT loop.value * loop.value AS squared) AS x;So it looks like it scores 76.5% on SQL-Eval [0], a bit behind GPT-4 at 83% and sqlcoder-15b at 78%. What kind of applications would this be useful for? What can you build with an AI data science intern that's right 75% of the time? As a programmer who always has to look stuff up when I SQL, I could definitely see asking something like this for a first draft of a query but it seems like I'm slightly better off asking…
1. If you are a a programmer I think you should learn SQL. It will give you a different perspective on programming that I think is invaluable. (I.e. programming without for loops) 2. Combining and slicing data is a craft, and doing it subtly wrong in one step can lead to fatal errors in the outcome. And most importantly, it can be very difficult to notice. Numbers don't smell. That is why I would be very hesitant to…
> Maybe it could be useful to ask questions that are similar to writing testcases "how can I verify that my query is doing the right thing"?
that seems like a good thread to pull on!
Earlier quoted context omitted.
1. If you are a a programmer I think you should learn SQL. It will give you a different perspective on programming that I think is invaluable. (I.e. programming without for loops) 2. Combining and slicing data is a craft, and doing it subtly wrong in one step can lead to fatal errors in the outcome. And most importantly, it can be very difficult to notice. Numbers don't smell. That is why I would be very hesitant to…
What makes you think that SQL doesn't have "for loops"? Ever heard of LATERAL joins/CROSS APPLY? SELECT loop.value, x.squared FROM generate_series(1,5) AS loop(value) CROSS JOIN LATERAL (SELECT loop.value * loop.value AS squared) AS x;
Earlier quoted context omitted.
1) OpenAI has consistently gone back on commitments it has made 2) Sam Altman has a shady track record publicly, and if you believe the things people say privately he has consistently done business very dishonestly throughout his career. He is the CEO, and virtually the entire executive team are people he brought in from his network. It’s his company. 3) To give one example of many, OpenAI recently changed the terms…
Hm. I disagree that executive level shenanigans translate to real world non-compliance with privacy law when there is an explicit request to delete/not store data.
Uber
Airbnb
The entire financial industry
also, every huge corporation that paid a vast (but relative to their market cap, insignificant) settlement, long after incidents in question, without admitting guilt.
Corporations have essentially culled vast numbers of humans until forced to stop. What’s a little lucrative nosiness in that context?
The massive tide of legally gray (including very dark gray) media hoovered up by training data vacuums isn’t exactly an industry secret. Whether any known player is more serious about protecting data source interests over their own ambitions remains to be verifiably demonstrated.
Someone once said, “it is easier to ask forgiveness than permission”. Someone might add, “if ever, or only performatively for congressional testimony theatre purposes, and definitely only after requiring a more punitive legal/regulatory moat to lock in the benefits of your non-compliance”.
ANY sketchiness should be taken seriously. Not making accusations. But sooner or later, somebody is going to say, “it’s a lot easier to cry and complain than claw back information someone took from you, who has billions to spend on lawyers”.