The market is definitely there for enterprise LLMs. Everyone is using GPT for work. I use it to provide stubs for memos and to brainstorm - but the real value comes from replace internal “tribal knowledge” with an AI who knows your org in and out.
Sure its not everyone, but the people who arent using them are signaling a major red flag IMO.
They are resistant to change, even if they don't understand the technology, what else are they resisting from their managers/leadership team? Further, I think of the people in my life who have refused to even try it, they all seem to have a screw or two loose, even if they are making 200k/yr successful.
All IMO of course, but in tech, I imagine something needs to be 'off' to never try it.
EDIT: Seems I'm getting criticism from people who are using it for inappropriate use cases. I don't use a screwdriver to hammer nails.
Consistent conditional logic makes more sense than a risk-laden hallucinating LLM for a lot of workflows.
"Everyone" doesn't need to hammer nails because there's more than just one career and industry. The acceptable quality of the job output varies drastically too.
"It kind of boggles my mind" that people can't see beyond their own life.
But it's definitely good at some other things. Writing boiler plate texts of various sorts and giving instructions on how to do certain things notably.
It seems to mostly synthesize common knowledge rather than learning anything. But that can be very useful, a lot of people's job involves doing things like that today.
To test reading comprehension, the source should be in the prompt, not the training set.
It's great for generating sample code snippets or refactoring code, but I can't paste my company's intellectual property into it
If I could train a customized version of it on all my company's Slack messages, Jira tickets, e-mails, etc it'd be insanely useful . . . . but I don't think any big company would actually want that, since it wouldn't be able to keep secrets from anyone with access to it
maybe it is easier to go through actual verified information than to double check everything an AI says.
I only use LLMs to restate information that I can half piece together so I can remember the missing bits (like a math proof or derivation), or to point me to recommendations of actual resources. And even those two things i am very wary off.
Wrong tool for the job. You don't ask it information questions. Ask it brainstorming questions.
(it makes it easier for some junior to replace you some day)
And the junior could leave the company, an AI won't.
Also in the news: https://www.cnbc.com/2023/05/02/chegg-drops-more-than-40perc...
>but the real value comes from replace internal “tribal knowledge” with an AI who knows your org in and out
I bet Microsoft is already working on that.
They are now trying to build a search index and feeding it in-context results.
Honestly not seeing much value over a search index, but hey if it makes the internal data easily searchable under the banner of AI hype it's a win.
We currently use GPT-4 combined with an internal knowledge base we had earlier since the beginning, and we practically have to fire our chief of staff and the admin team. Just kidding, but it's made her team's work a ton easier that she can devote more time to the nitty-gritty hard stuff.
The interesting part is that I had a bit of a personality touch added as part of its context, so the AI's character is quite.... villainous.
Enterprise self-hosted ChatGPT is going to be huge.
Currently we use an initial semantic search for context injection, which is then passed to GPT for completions. If any LLAMA company were to make that second pretrained bit self-hostable for some license fee, I know a bunch of companies in finance of all sizes which would readily pounce on that tool. But I'm fairly certain that's not what Open AI wants to do.