I mean, sure, I guess that's neat, but Kofax OmniPage Ultimate has been a thing for years at this point and does the whole OCR from a crappy scan thing, including reformatting it into whatever format you want. Can even take data from forms and digitize it into editable text or stick it into a database. Goes back to the '80s. Sure, different methodology, but it's not something we haven't already been doing for yonks. Users have even been able to train OmniPage Ultimate to recognize someone's bizarre and unique handwriting styles in seconds.

OmniPage Ultimate is an entire workflow that's turn-key and ready-to-go.

Neat? Neat!?

Generally speaking, Kofax -- or any other off-the-shelf OCR tool -- can't process tables properly. They get confused by headings, total rows, and the like. Hence the R&D effort by that Microsoft team to develop a purpose-built AI-driven tool that can specifically identify these elements and then output the result not just as ASCII text, but as a spreadsheet.

Either way, whether you are talking about the Microsoft AI or the Kofax tool, the effort is measured in man-years, man-decades, or perhaps even man-centuries.

You can just ask ChatGPT to do similar tasks for you in seconds.

A real-world use case I had for GPT is to fix up the formatting of badly copy-pasted tables. E.g.: I had created a cloud VM and pasted the summary tab into text editor, and only then noticed that the cells ended up on individual rows, interleaved with the headings. Even if I had used a regex to undo the damage, the headings mess up the alternating row interleave. E.g.:

    Overview
    Hardware
    SKU
    A2
    Zone
    2
    Software
    Operating System
    Windows
    Image
    Licensed
    Yes

Chat GPT can "undo the damage" because it understands not only what text is likely to be a heading, property, or value... but it has also seen specifically the properties of the type of cloud VM I was working on! It understands these things to the same level as I do, and can fix up the formatting the same way I would.

Sit down for a second, and ask yourself: How much time and effort would you have to invest to solve this style of problem, in general. E.g.: given some random, broken table formatting for an arbitrary but well-known subject, fix up the formatting.

Years of effort?

Decades?

And yet, my default go-to example with me being personally unimpressed with ChatGPT 3 and 4 is getting it to output a valid NGINX configuration without hallucinating, despite feeding it the required documentation, the original Apache .htaccess file, and telling it what URL rewrites would be required. This should not be a difficult thing, but it constantly hallucinates things that aren't there because it's an LLM, and that's what LLMs do, they predict the next word.

Whilst to the untrained eye, sure, the NGINX config looked great, but it didn't function, and it didn't include the URL rewrites as instructed, hallucinated a bunch of rewrites that didn't exist and would serve no purpose, and despite refining the prompts over many days, and consulting with self-proclaimed "prompt engineers", it still didn't give the expected output.

It's neat. Reliable? Not in my experiences for my needs, but I am genuinely glad you're making it work for what you need, that's definitely cool, provided it doesn't hallucinate in an unnoticed capacity. It's a lot of trust to place in an LLM.

These things have limits, definitely. In almost every use-case I've tried, I've bumped up against them.

The trick is to know what they can and can't do, and use them where they're useful enough.

Someone here on HN quipped that ChatGPT is "isn't intelligent" because it couldn't come up with a revolutionary new battery chemistry.

Like... what are you expecting? A god?

LLMs are useful when you can validate the output yourself. Similarly, they're useful when the output doesn't have to be precise but the inputs are English.

For example, they're awesome "filters" for human input as measured against human metrics. "Is the following text rude? Output YES or NO only?" is very useful and works well enough, right now.

They're also useful when you need to iterate to find something, or when your search parameters are incredibly vague but have a narrow Venn diagram intersection.

I've been using ChatGPT as a replacement for the /r/tipofmytongue sub-Reddit. It knows everything well enough to be able to interactively find what I'm looking for to an extent that is super-individual-human, and in some ways beyond what even a very large collection of humans can achieve.

I think you and I are pretty aligned in our thoughts on ChatGPT. I think its revolutionary, and I use it every day. But I don't consider it generally intelligent, I do think that novel thoughts of demonstrable value are the real test for that, and a useful invention would convince me beyond all doubt.

> a useful invention

In my opinion, the mental model you should use when evaluating LLMs-vs-Human capability: When asked a question, the LLM has to answer without being able to iterate, back-track, or even use a scratch pad such as pen&paper or a text editor. Basically it's the same as an "oral exam", where you have to stand in the middle of a room and get grilled by a professor to determine your knowledge on a subject.

Don't compare "human with tools and unlimited time" to "LLM with no tools and seconds of time". Compare "human being interrogated in an empty room" and then it is much more clear where an LLM rates.

GPT 4 is definitely super-human in some areas, such as general knowledge and translation between languages.

No human knows as much, or can speak as many languages.

Ask yourself this: can any human, when asked to "invent something", just do it, then and there?

Give it a billion subjective hours. Give it databases and network access. Do you really think it will produce something? You definitely do a good job of explaining one of the limitations an LLM faces compared to a general super-intelligence.

I'm really surprised it cannot output a valid NGINX config when you give it that amount of context.

Can you share the prompt and your expected output? I'm interested to see where it goes wrong.

Here's an experiment that showcases GPT-4 deep (meta)semantic abilities:

https://pastebin.com/8FwQzDiE

TL;DR. I start by asking it to generate sentences where subject-object inversion yields a meaningful sentence where the verb's meaning is shifted metaphorically. For instance "I smoke the cigarette vs the cigarette is smoking me". After some back and forth it comes up with:

The painter captures the landscape vs. The landscape captures the painter. The gardener shapes the garden vs. The garden shapes the gardener. The chef creates the dish vs. The dish creates the chef.

Maybe that will change your mind.

Nginx/Apache config is not an easy task. I would call this an incredibly hard problem to solve. I certainly never trust that I've done the config correctly until I've tested it carefully. The way this eventually works is you tell the AI "I want Nginx config that has these properties" and the AI generates config and tests it against Nginx until it has the requisite config and it's tested.

>> You can just ask ChatGPT to do similar tasks for you in seconds.

And how well does it do them? Correct me if I'm wrong, but for the time being all you know about its ability to replace that other MS tool is a "yes, yes it can" by a MS dev? Don't you want to withhold judgement until after you've spent some time actually using ChatGPT to do OCR?

So what you're saying is that one piece of software with years of labor from a team of engineers can match another piece of software with years of labor from a team of engineers? You're incorrectly comparing the creation of the software with the usage of the software. Yeah, now all you need to do is give ChatGPT the file and prompt it for a solution, whereas before, you... gave the file to a different piece of software and hit a button.

ChatGPT isn't a complete workflow and doesn't even get off the starting blocks for doing what OmniPage Ultimate does.

This is like saying you could do CAD via ChatGPT, sure, but it's an objectively crappy experience versus an actual piece of CAD software with an appropriate UX.

Using different pieces of software isn't a problem for most adults.