The hype surrounding them is not as a pa and tbh a lot of these use cases already have existing methods that work just fine. There are ways to find key information in files already, and speedy meeting minutes is really just a template away.
I was not able to get meeting transcription in that quality that cheap ever before. I followed dictation software for over a decade and tx to ML the open source software is suddenly a lot better than ever before.
Our internal company search with state of the art search indexes and search software was always shit. Now i ask an agent about a product standard and it just finds it.
Image generation never existed before.
Building a chatbot in a way that it actually does what you expect and its more complicated than answering the same 10 theoretical features it can do was hard and never really good and it now just works.
Im also not aware of any software rewriting or even writing documents for me, structer them etc.
If this was 'a simple user error' or 'not using the right tool for the job' than this was an error from smart people and it still got fixed by using AI/ML in an instant.
With this, my argument still stands even if it would be for a different reason which i personally doubt.
Often big companies are the least efficient. And big companies can still make mistakes or have very inefficient processes. There was already a perfectly simple solution to the issue that could have been utilised prior to this and overall still the most efficient solution.
Also, everyone does dumb things, even smart people do dumb things. I do research in a field that many outsiders would say you must be smart to do (not my view) and every single one of us does dumb shit daily. Anyone who thinks they don't isn't as smart as they think they are.
Well, LLMs are the right tool for the job. They just work.
I mean if you are going to deny their usefulness in the face of plenty of people telling you they actually help, it’s going to be impossible to have a discussion.
They can be useful, however for admin tasks, there are plenty of valid alternatives that really take no longer time wise so why bother using all that computing power.
They don't just work though, they are not fool proof and definitely require double checking.
> valid alternatives that really take no longer time wise
That’s not my experience.
We use them more and more at my job. It was already great for most office tasks including brainstorming simple things but now suppliers are starting to sell us agents which pretty much just work and honestly there are a ton of things for which LLMs seem really suited for.
CMDB queries? Annoying SAP requests for which you have to delve through dozens of menus? The stupid interface of my travel management and expense software? Please give me a chatbot for all of that which can actually decipher what I’m trying to do. These are hours of productivity unlocked.
We are also starting to deploy more and more RAG on select core business dataset and it’s more useful than even I anticipated and I’m already convinced. You ask, you get a brief answer and the documents back. This used to be either hours of delving through search results or emails with experts.
As imperfect as they are now, the potential value of LLMs is already tremendous.
How do you check accuracy of these? You stated brainstorming as an example that they are great at. As obviously experts are experts for a reason.
My issue here is that a lot of this is solved by good practice, for example,travel management and expenses have been solved, company credit card. I don't need one slightly better piece of software to manage one terrible piece of software to solve an issue that has a solution.
Because LLMs send you back links to the tools and you still get the usual confirmation process when you do things.
The main issue never was knowing what to do but actually getting the tools to do it. LLMs are extremely good at turning messy stuff into tools manipulation especially where there never was an API available in the first place.
It’s not a question of practices. Anyone who has ever worked for a very large company knows that systems are complicated by need and everything move at the speed of a freighter ship if you want to make significant changes.
Of course we need one slightly better piece of software to manage terrible pieces of software. There are insane value there. This is a major issue for most companies. I have seen millions spent into getting better dashboards from SAP which paid for themselves in actual savings.
Ok take Transcription, they were trying to use free as in cost tools instead of using software that works efficiently that has been effective for decades now.
Microsoft is absolutely selling them as pa and already selling a lot. I think HNers being mostly software developers live in a bubble when it comes to the reality of what LLMs are actually used for.
Speedy minutes are absolutely not a template away. Anyone who ever had to write minutes for a complicated meetings knows it’s hard and requires a lot of back and forth for everyone to agree about what was said and decided.
Now you just turn on Copilot and you get both a transcript and an adequate basis for good minutes. Bonus point: it’s made by a machine so no one complains it has bias.
3. Involve the messiness of the real world enough that you can't write exact code to do it without it being insanely fragile
LLMs suddenly start to tackle these, and tackle them kind of all at once. Additionally they are "programmed" in just English and so you don't need a specialist to do something like change the tone of the summary or format, you just write what you want.
Assuming the models never get any smarter or even cheaper, and all we get is neater integrations, I still think this is all huge.
Do you really believe the outlay in terms of computer power is worth it to change the tone of an email? If it never gets better, this is a vast waste of an enormous amount of resources.
That's not what I've talked about them being for, but regardless it depends on the impact surely. If it can show you how someone may misunderstand your point and either help correct it or just show the problem then yes that can easily be worth spending a few cycles on. The additional energy cost of further back and forths caused by a misunderstanding could very easily be higher. At full whack, my GPU draws something like 10x what my monitor does, so fixing something quickly and automatically can easily use less power than doing it automatically.
Again though, that's not at all what I've talked about.
This is a business practice issue and staff issue, not a meeting minutes issue. I have meetings daily, and have never had this issue. You make it clear what is decided during the meeting, give anyone a chance to query or question, then no one can argue.
You would be wrong. I am actually quite perky, I just don't suffer foolish admin tasks easily. I only have meetings with a goal (not just for the sake of it), and then I simply make sure it is clear what the solution is, no matter whose idea it was. I don't care about being right or wrong in a meeting, I care that we have a useful outcome and it isn't an hour wasted. Having a meeting whereby the outcome of a meeting is unclear is a complete waste of time, and is not solved by tech, it is solved by how your meetings are managed.
What I see LLMs at this point is simplified input and output solutions with reduced barriers of entry. So application could become more widespread.
Now that I think of it, maybe this AI era is not electricity, but rather GUI - like the time when Jobs(or whoever) figured out and adopted modern GUI on computers allowing more widespread uses of computer
Do they only have reduced barriers of entry if you aren't fussed about the accuracy of the output? If you care that everything works correctly and is factually correct, do you not need the same competency as just doing the task by hand.
It's a good analogy because the key development does seem to have been the interface. Instead of wrapping it up as a text autocomplete (a la google search), openai wrapped it up as an IM client, and we were off to the races.
