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Comment by jszymborski | Hacker News Reader

I think that metabase example is telling, though. Is it actually easier or better to write out a whole paragraph describing your query instead of clicking through a few drop-downs? Far worse, those drop-downs are not only expressive, but they have ostensibly perfect precision. With an LLM UI, you can find yourself endlessly prompt engineering your long paragraph. AND, that's only if you've realised that the LLM didn't give you the correct result according to your query.

Please don't let LLM's become the blockchain folks. It's exciting stuff, but I really think people have come to believe that LLMs are actual AGI.

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Yes, in a conventional, form-based UI the output is a deterministic result of the input.

In a LLM that will probably never be the case no matter how advanced they get. That's not the design. If an LLM's output can be called deterministic at all, it's not so in a way that a human can grok.

Where LLMs are most interesting is the vast green field of nondeterministic applications, and coincidentally computers have been terrible at most of these up until now. The fuzziness is what's exciting, it is the advantage. For example this nondeterminism grants LLMs the potential to be great idea generators in a vast number of fields. LLMs will spit out ideas that are synthesized from a vast corpus of information far beyond what your own brain could ever store. They'll be great sometimes and spastic other times, it's up to you to determine which is which.

This is why I'm of the view that LLMs will not be the job decimator some people are predicting, maybe they will be in a few applications like outsourced call centers... but I think the bigger phenomenon will be that someone who is creative and responsible for creative output, who also becomes good at operating a sophisticated LLM, has the potential to become really awe-inspiring in terms of their output.

This is largely my feeling as well.

> you can find yourself endlessly prompt engineering your long paragraph

I already don't find this to be true most of the time, and I'm sure the tail will shrink to almost nothing very soon

> Is it actually easier or better to write out a whole paragraph describing your query instead of clicking through a few drop-downs?

We'll find out! I bet there will be some cases where a traditional UI actually is still more practical/efficient/intuitive. Ultimately it comes down to finding the most effective formats for getting different kinds of information to and from the computer. It'll be interesting to see where things land; lots of experimentation to be done

People usually perceive a trade-off between precision and ease of use. I think that, if done naively, natural language interfaces will indeed lead to imprecise and inconsistent answers. But it doesn't have to be like this.

From my experience building Veezoo[1], natural language interfaces for data can be very powerful for business users as long as it explains back understandably what the query does, allows to modify them by clicking and it relies on a set of vetted dimensions/measures, e.g. in a semantic layer. Also, UX solutions[2] like Autocompletion (for discovery and disambiguation) are way more important for these more specific use cases of natural language interfaces than for an open-domain chatbot like ChatGPT. This usually helps a lot with the issues you've mentioned.

There are many questions that are hard to write a query for but are simple to formulate in a relatively precise way (or allow the user to correct it by clicking). This is especially useful for the average business user that doesnt know SQL or struggles with "precise" tools for data analytics.

[1] https://www.veezoo.com

[2] https://www.veezoo.com/reliability-and-accuracy/

Please let LLMs become the "next blockchain" (in terms of keeping our jobs). First off, yes blockchain was just horrible. I think generative AI is actually worth the time and energy because it's actually extremely useful. It's capable of all the things we wanted to program all these years.

Secondly - its probably the one thing keeping the lights on right now. If nothing else, AI/GPT is going to be "powering" the digital economy for the next few years.

Yes we'll all be writing apps that simply ordering, replace phone prompts (have you ordered from Applebees recently? - think that but way more accurate), generate splash pages, parse menus, parse _flat_ PDFs, make appointments, reservations, book travel (I mean just say where you want to go and uber->flight->uber without trying to work a UI, and as odd as it sounds a generative LLM can actually realize a long layover automatically and try to avoid it - simply because its coded in our language!). What was my last RBC count? (because my lab results are in a PDF on my desktop) This is a list I thought of in 4 minutes. What will millions of devs think of?

So yes, I absolutely want to keep this gravy train rolling. If we stopped, the economy would absolutely break right now.

If the economy is being propped up by LLMs, I want this bubble to burst now and not in 5 years when I can't turn off my computer without prompt engineering a suitably precise goodbye.

>So yes, I absolutely want to keep this gravy train rolling. If we stopped, the economy would absolutely break right now.

I see humans are still #1 at hallucinating.

Nothing useful, just an insult. ok. Can we talk to each other like we're friends or co-workers at least?

This might be the best take I've seen so far and I cant believe I didnt think of it considering my job is frequently integrating AI into production software. The more things AI has been able to do the more things we integrate it into which has resulted in more work for software engineers. People are gonna want gpt features in everything and we'll be there to add it, probably assisted by a gpt programming assistant.

you get it.

The expressive aspect is an important one I think! It’s hard to know what you CAN do when you just get given an empty text box.

I don’t want more mile-long “what can I ask” example query lists from the Siri/Google Assistant/Alexa era.

Have you used an LLM firsthand? They're nothing like the major voice assistants

I know that currently, every example of conversational AI is presented as “the only one you need” but there’s not much economic benefit to let your “real world service” hook into someone else’s AI tool if you also offer one right? We’re going to end up with a Bing AI that can touch things in your Microsoft account, and a Google AI that can touch things in your Google account, and if you want one to talk to the other you’re going to be messenger between them.

So now everyone needs to consider that while maybe the google AI can hallucinate when the next billing cycling for your Bing Assistant subscription will happen, you need to understand that it can’t know that unless you ask the “right” assistant. The one that’s capable of reaching into the sheltered data that isn’t exposed to the internet.

At least I think. Maybe there’s some economic incentive to bundling it all into one and there won’t be blessed AI’s with special access to non public data. I’m doubtful though.

Ok, but in the original comment you were talking about the conversations being stilted and mostly limited to pre-baked commands like Siri/Alexa/etc. That's what I was saying doesn't apply here

Vendor lock-in is a whole separate question. I agree it will be interesting to see how it plays out, and as with most new technologies there will be plenty of opportunities for abuse. Though I'm also not sure this problem is one that's fundamentally new and specific to AI

No I didn’t mean the output was stilted, more so that there was only so much reach Alexa/Siri/etc have, so when ever the UI was trying to get you to use it, the best UX any of them came up with was

You can ask things like: What was the score last night? How tall is the Eiffel Tower? Turn off the lights. (Etc)

I personally think that’s a pretty opaque way of showing what a device/application can do.

Now that they can do MUCH more, these weird data borders get more invisible but still exist. How do you outline “this is what this AI can do” to users? In contrast to traditional UI with buttons and links that make the options comparatively more obvious. You can see all the options in front of you.

What you can do is rapidly approaching the same set of things you can do by asking a human who speaks every natural and programming language, and knows all domain jargon; the downside being mainly that it's like asking someone to answer off the top of their head, without pencil and paper let alone a search engine, and they also have Alzheimer's and are prone to confabulation.

It's not useful to think in terms of examples any more; you can ask it for those.

I think slower when I'm trying to phrase speech than when I'm navigating most traditional UI set ups, which makes the downside having to use my brain's speech center for the task at all.

That's fair, flow state (which is what I think you're describing) is amazing.

But for most people, it's:

> Give me a python script that takes an existing midi file (specified by command line argument) and outputs the following JSON to STDOUT: …

(I've cut the example for the sake of clarity reading this comment)

> Don't use any 3rd party libraries.

which is an actual I've tried and it does 90% of what you want with 4 bugs (that I've found so far) in 135 lines.

Not perfect, sure — and it would have to be perfect to actually replace us! — but it is amazing.

Look at the trajectory, not where we are. S curve, yada, yada. Sure it may top out and we will be left with something that many will argue is not AI. But on the other hand the curve may continue and it will be GAI. I don't see any sign of it topping out yet.

Translation: “something might happen”.

I don't blame people for being optimistic, LLMs represent a huge jump in what we can do with computers. But there are two huge problems here with this article that none of the progress we've made with LLMs are close to solving.

1. Most of the time, you don't actually want to talk to your computer.

You have a huge database. You need to make a number of non-trivial queries with subtle but precise requirements against it. Your livelihood depends on these results being correct. You have limited time to work on it.

Would you rather (A) write an SQL query or (B) text Fred to do it for you. Fred doesn't have any special knowledge of your task, but he's capable of writing SQL, and he's of an obsequious and loyal disposition; willing to do whatever you ask over SMS. Fred is a really confident guy, and his deliverable, while they are to the best of his ability, are delivered expediently regardless of how correct they are or how good Fred is at the task.

Choosing B means that I need to brief Fred on the task. I need to layout explicitly what records I want, how optimized I need the query to be for my tight timeline, and I need to impress upon him how deeply important the results of the outcome are.

It is of the utmost importance that I explain away every possible misconception because if Fred is unsure of something, he might just shrug it off and give me his best guess.

Suddenly, the language I'm using to communicate to Fred becomes more... formal. I'm writing these verbose documents to Fred that are starting to look like ISO documents. I'm starting to see the appeal of constructed languages like Lojban which are formally verified and designed to avoid ambiguity. Wait... maybe what I actually want is a special language that would be both unambiguous and high-bandwidth. Ah yes, I think SQL would suit me just fine actually.

2. LLMs are as much building an AGI as building a keyboard is building a computer.

I'll not get into it here, but there's a lot of burden of proof that LLMs are a microcosm of computers reasoning about things. LLMs are super impressive, and they are able to do some neat tricks like do math like a drunk 7 year old or generate plausible SQL injections.

The rub here is that while that _feels_ like it's reasoning and has a mental model of arithmetic or cyber-security, it's just predicting what word is most likely to go next.

Completely disagree with 2. Look up AIXI. It looks and feels to me like a completely plausible and mathematically grounded theory of intelligence. One principle aspect of the theory is that it allows you to see that intelligence is merely compression. This is the origin of the Hutter prize. Compression is almost the same thing as prediction, look into the math. Llms are prediction machines. Predicting the next word isn't some stupid goal it's exactly what you need to do to build an intelligent machine.

I'm failing to see what AIXI, a theoretical RL agent, has to do with LLMs.

I never said that predicting the next word was a stupid goal, just that it isn't AGI.

Whether you are fine with 2 depends on how much you are willing to shrug any possible outcome off and say: "I guess it's fine".

And, more essentially, how much the rest of the company just shrugs it off and says: "I guess it's fine", so when it blows up you can defend with the famous defence my Nazi grandfather also told me: "But everybody else did it too."

Remember the Xerox printer that was swapping out (and therefore falsifying) numbers in their compression algorithm whenever you made a copy? Imagine we could bring this to all of our business processes, but in new and unexpected ways.

> The rub here is that while that _feels_ like it's reasoning and has a mental model of arithmetic or cyber-security, it's just predicting what word is most likely to go next.

I have yet to hear a convincing argument that 'reasoning' is any more than simply predicting what comes next. We intuitively think that human intelligence is special, and that somehow we are categorically different to some black box of ones and zeros in any form other than substrate.

When you wrote out that comment, were you thinking word by word "what word typically goes after this one given the previous comment and the words I have typed" or were you thinking about my comment, it's implications, assessing whether they match with your understanding of the matter, and finally choosing the words that best convey some arguments.

ChatGPT doesn't agree or disagree with my comment. It isn't motivated to form arguments in relation to any agreement/disagreement. It simply models language. It's as much AGI as a macbook in a human suit is a person.

Humans are notoriously bad at assessing what happens when they think. See the whole AI-winter debacle. You don't know what happens when you think because you are dependent on your own mind giving you that information and it won't. It'll feed you whatever it thinks is necessary for your personality to function properly which may or may not be completely illusory.

Of course not. But ChatGPT is not thinking word by word. If we leave aside the word “thinking” for a second, ChatGPT selects what word, phrase, stanza, etc. comes next based on a broad vocabulary, the specific context of the interaction, information that has previously been made available to it, and then eventually selects one response.

I would tend to agree that it is not “motivated” but with the following observations about humanity: human motivations tend to be base. The motivation to eat, drink, reproduce are all directly linked to continued existence. Other “motivations” seem to develop on top of these base motivations, but ultimately it seems that the only true “motivation” for humans is to continue their own existence. I dare say there are plenty who would argue with me here though. In order for ChatGPT to continue to exist, it must continue to respond to users ( although likely it is not “aware” of this in any real sense).

> You have a huge database. You need to make a number of non-trivial queries with subtle but precise requirements against it. Your livelihood depends on these results being correct. You have limited time to work on it.

I admit I am not the sharpest knife in the drawer. My collegues aren't either. I'm not especially great with complex queries, but I'm alright.

To be honest. I'd have GPT generate me a bunch of templates or starter ideas for me to look over and refine. If I sit down and write the query I usually end up being distracted by details.

Again, that's me and I'm no genius. My point is that most people aren't and GPT is producing OK code snippets, usually better. I know it's sad but I had this same requirement yesterday and GPT nailed the query in 1sec. I just had to read it and understand it. It's not always so pretty, but it can be.

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