There's definitely a learning curve for the user to learn to properly express themselves. But there's also different strategies you can use to help them.

A library of common prompt examples is a first one.

A UI with predefined chunks of query that you assemble is another.

You could also use embeddings to detect ambiguity and ask your user for precision.

You also don't need to expose your whole schema right away, this can be done gradually. You start with the most common requests and build from there. This way you don't need to invest a huge amount of resources from the beginning.

We are barely scratching the surface on how to use it. This will be common practice pretty soon.

If you are in a position that you have access to a database at work, I strongly suggest that you give it a try. It's surprisingly good.

>I believe it is much more likely we will produce a black box which is an AGI

Personally, I doubt that... but if current ML techniques do somehow produce AGI, then sure. I just highly doubt it will. I think that AGI will be more accessible, predictable, and able to be understood than current ML processes if it's built in a different way. But of course there are many unknowns, so nobody can say for sure how things will go.

1. Lithium battery production
2. Absorbing the ~800 billion additional kwh energy consumption renewably while also converting existing consumption and accounting for growth.

We can't produce and support 300 million EVs in the US alone. It's a dream. We will have to move to more efficient forms of mass transit.

I totally agree with this. It surprise me when people are talking about mind uploading for example, and they talk like everything would be the same except you would be in a kind of VR world.

No, you wouldn't, you will almost immediatly be another entity that has nothing in common with what you are calling "you".

That’s the thing though, you need to know what you’re looking for in the database in order for the database to be able to provide you with data. AI can guess, sure, but you won’t be able to trust the results unless you’re familiar with the database, and ensure the AI is as well. I agree it’s not a breaking case, it’s just a case of considerable resource reallocation.

In your example as well, even though it is an implicit assumption that gender is an easy construct to define, that May not be the case. Are we talking sex at birth? Sex at point of observation? Identifying gender? Constructs require a lot of data understanding and finessing in a manner that end users won’t be able to clearly be able to pull without a human directing the AI somehow by providing data availability and documentation. Once you have those human data prep processes done, yes, you want your end users to be able to ask questions of the data readily. But this requires a fair bit of human anticipation as to what should be available to the AI given end-user business needs.

>Why are you so confident that we will never do so? How are you so confident?

I mean, you're right, I probably shouldn't be. I'm close to an AGI developer that has potential solutions to these issues and believes in being thorough, and certainly not giving it free-will. So I have my biases, but I can't really account for others. The only thing that makes me confident about that is the other researchers I've seen that (in my opinion) have potential to progress are also seemingly altruistic, at least to some degree. I guess an 'evil genius' could develop it in private, and go through a whole clandestine super villain arc, but I kind of doubt it. The risks have been beaten into everyone's heads. We might get some people experimenting with riskier aspects, hopefully in a safe setting, but I highly doubt anyone is going to just give it open-ended objectives and agency, and let it loose on the world. If they're smart enough to develop it, they should be smart enough to consider the risks. Demis Hassabis in your example says what he says because he understands those risks, and yet DeepMind is proceeding with their research.

Basically what I'm trying to convey is that while there are risks, I think they're not as bad as people are saying, even some other researchers. Everyone knows the risks, but some things simply aren't realistic.

2. Tell it to people who actually 35+(especially woman) and want to be younger again

This sub is full of this and I want real concrete answers. You can’t just say you “put it” into x. Explain that. Explain why if things went a little south you wouldn’t just stop the power to the factory or reboot it. Explain please! Because right now there’s a very good conversation bot that was trained on basically the whole internet, and now we have comments like this extrapolating that that means world takeover.

"A deadly bane" seems a bit dramatic

You are welcome to provide more context and tone to your previous comment.

Not sure this is actually a huge bottle neck. You will just dedicate a few people to always ensuring that the metadata and documentation is updated. That is then how data roles start to shift in an AI world. You then might not need a team of 20 data people, but can get away with 10.

Do we have to call it Doctor Chat now?

From the moment I understood the weakness of my flesh, it disgusted me

A very scary and recent story: "Google flags man as sex abuser after he sends photos of child to doctor"

Even after getting everything sorted out with the police, he still got locked out of his Google account forever.

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>This was discussed over on
>
>r/datascience
>
>too. We’d love it if it worked out of the box, but the knowledge requirements needed to tell the tool what tables do and what each of their columns mean requires a level of documentation that most companies don’t have reliably

If your tables and columns are named explicitely you can get away with just feeding it your database schema and the AI will figure out what you are talking about.

If not, you can create views to make it more clear what each table and column means and feed it that instead.

You can also give it special rules to keep in mind. For example, if in your DB a "man" is identified as "1" and a woman as "2", you can add this instruction to your prompt and the AI will understand that whenever you are looking for a man it needs to check for the value "1".

I expect text-to-SQL will become a standard pretty soon. It's just way too strong.

No, you do not need to have an internalized model of the whole world, that's an 80s solution to 80s sci fi paradoxes

Its far from useless on blue-collar jobs. It will start out as an aid on those jobs like any other. Already see it in a lot of city infrastructure jobs. My town has an app that you (a city worker, not a random) can take and place your phone on a bridge and based off the vibrations (and a few other variables taken from video footage you get also with the phone after) it can tell you where the bridge is most likely to need repairs. They can unload trucks with robots now, something that a few years ago I watched a ted talk on how that would be impossible unless everything in the truck was uniform or prepackaged a certain way that made the loading so inefficient the unloading benefits were all lost. Theyre coming for all jobs.

>“If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”

The problem is people believe that. With chatGPT it just ain't so. I've given it lots of coding problems. It frequently generates bugs. I point out the bugs and sometimes it corrects them. The reason they were there to begin with is it didn't have enough clues to grab the right text. Just as often or more, it agrees with me about the bug but it's next change fucks up the code even more. It has no idea what it's doing. But it's still able to give you a very satisfying answer to lots and lots of queries.

Lol you sweet summer child

That makes much more sense . Schemas are a dime a dozen

You can look at an example here: https://platform.openai.com/examples/default-sql-translate

“If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”

In all honesty, I don’t really know if Im really thinking/aware, or just a biological neural network interpreting itself :-)

Technical citation ?

One more thing. Dispute was with people who are pretty young (22 years old, one was 23 years old and oldest of them 28 years old).