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ChatGPT Enterprise

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Re: ChatGPT Enterprise

#311

Post by Racing0461 »

bagels wrote: post #212
Racing0461 wrote: post #205

> unlimited higher-speed GPT-4 access aka the nerfed version. high speed means the weights were relaxed leading to faster output but worse reasoning and memory.

Do you have any references on this? I have only seen a lot of speculation.

It's been discussed on twitter and /r/chatgpt but i've noticed it myself. I always find it funny when people say chatgpt hasn't changed since launch when i see it with my own eyes.

> The party told you to reject the evidence of your eyes and ears. It was their final, most essential command

Re: ChatGPT Enterprise

#312

Post by lijok »

PeterCorless wrote: post #298
lijok wrote: post #295

Earlier quoted context omitted.

Should get better over time as the tool gets better incorporated into the industry. People always misuse new tools

In such cases, I believe there are (and should be) guardrails. It already has some guardrails internally set: ChatGPT already knows how to say: "I am not a lawyer, but I can provide you with some general information on this topic." The same thing for medicine: "I'm not a medical professional, but I can provide some general information..." But, for instance, ask it to design a canard for a 4th generation supersonic fi…

> There needs to be a way to "sniff out" if there's topics that people are getting too close to danger zones for it to answer. Ways for organizations themselves to set those 'bounding boxes.'

I wonder if this can be achieved with the "Custom Instructions" feature. With enterprise, these can be managed by the admin. Could tell ChatGPT something along the lines of: "Make sure to state you are not an expert if you are asked to comment on any of the following: []"

Re: ChatGPT Enterprise

#313

Post by sheeshkebab »

ivalm wrote: post #271
pradn wrote: post #254

Non-use of enterprise data for training models is table-stakes for enterprise ML products. Google does the same thing, for example. They'll want to climb the compliance ladder to be considered in more highly-regulated industries. I don't think they're quite HIPAA-compliant yet. The next thing after that is probably in-transit geofencing, so the hardware used by an institution reside in a particular jurisdiction. This…

> don't think they're quite HIPAA-compliant yet OpenAI offers baa to select customers.

Fedramp? High?

Re: ChatGPT Enterprise

#314

Post by ladzoppelin »

brianleb wrote: post #289
dahwolf wrote: post #248

Earlier quoted context omitted.

Can't believe the pushback I'm getting here. The use case is stunningly obvious. Companies want to dump all their Excels in it and get insights that no human could produce in any reasonable amount of time. Companies want to dump a zillion help desk tickets into and gain meaningful insights from it. Companies want to dump all their Sharepoints and Wikis into it that currently nobody can even find or manage, and finall…

>>Companies want to dump all their Excels in it and get insights that no human could produce in any reasonable amount of time. >>Companies want to dump a zillion help desk tickets into and gain meaningful insights from it. >>Companies want to dump all their Sharepoints and Wikis into it that currently nobody can even find or manage, and finally have functioning knowledge search. Mature organizations already have solu…

So these "mature" orgs are using something better than openai, can you explain ?

Re: ChatGPT Enterprise

#315

Post by throwuwu »

NBJack wrote: post #285
louzell wrote: post #272

Earlier quoted context omitted.

One aspect of working in a big company is figuring out where all the bits of specialized knowledge live, and what the company-specific processes are for getting things done. One use case for an internal chatGPT is essentially a 100% available mentor inside the company.

This is going to make for some highly entertaining post mortems. "Management believed Jimmy Intern would be fine to deploy Prod Model Sysphus vN+1; their Beginner Acceleration Divison (BAD) Team was eager to show off the new LLM and how quickly it could on-board a new employee. To his credit, Jimmy asked the BAD model the correct questions for the job. That's when the LLM began hallucinating, resulting in the instruc…

Yawn. This is probably The hundredth time I’ve seen this scenario trotted out and knowledge base retrieval and interpretation has been solved since before bing chat was on limited sign up.

You don’t even need to fine tune a model to do this, you just give it a search API to your documentation, code and internal messaging history. It pulls up relevant information based on queries it generated from your prompt and then compiles it into a nicely written explanation with hyperlinked sources.

Re: ChatGPT Enterprise

#317

Post by jmorgan »

jacquesm wrote: post #125
jmorgan wrote: post #95

Earlier quoted context omitted.

Seemed like a great project. Hope to see it come back! There are some great open-source projects in this space – not quite the same – many are focused on local LLMs like Llama2 or Code Llama which was released last week: - https://github.com/jmorganca/ollama (download & run LLMs locally - I'm a maintainer) - https://github.com/simonw/llm (access LLMs from the cli - cloud and local) - https://github.com/oobabooga/text…

Ollama is very neat. Given how compressible the models are is there any work being done on using them in some kind of compressed format other than reducing the word size?

There are different levels of quantization available for different models (if that's what you mean :). E.g. here are the versions available for Llama 2: https://ollama.ai/library/llama2/tags which go down to 2-bit quantization (which surprisingly still happens to work reasonably well).

Re: ChatGPT Enterprise

#318

Post by jacquesm »

jmorgan wrote: post #317
jacquesm wrote: post #125

Earlier quoted context omitted.

Ollama is very neat. Given how compressible the models are is there any work being done on using them in some kind of compressed format other than reducing the word size?

There are different levels of quantization available for different models (if that's what you mean :). E.g. here are the versions available for Llama 2: https://ollama.ai/library/llama2/tags which go down to 2-bit quantization (which surprisingly still happens to work reasonably well).

No, what I mean is that it seems as though there is quite a bit of sparseness to the matrix and I was wondering if that can somehow be used to further shrink the model, quantization is another effect (it leaves the shape of the various elements as they are but reduces their bit-depth).

Re: ChatGPT Enterprise

#319

Post by PeterCorless »

tsunamifury wrote: post #300
PeterCorless wrote: post #288

I hereby dub this "Baron-von-Munchausen-as-a-Service." Now you can pay real money for a chatbot to make stuff up about your company and its products. While it is pretty incredible stuff, until or unless they have a veracity bit — some sort of "please don't lie" flag in it, I'd be wary of what it produces. Will ChatGPT offer off-the-cuff pricing, discounts, rebates and refunds that are in line with your actual busines…

This sounds a bit like word salad, and your argument is worse than an LLM output. You even contradict yourself at the end. Also I think you need to better understand how much hallucination has been driven down recently and its clear path going forward.

I just asked ChatGPT to give me directions to my favorite shop in Manhattan from Penn Station. It gave me wrong public transit directions about which subway to take. It also gave me wrong walking directions, putting me blocks off course and as to which side of the street I'd find the shop on.

The only thing it got right was: "Please note that subway schedules and routes can vary, so it's a good idea to use a navigation app like Google Maps or the official MTA website for real-time information and step-by-step directions."

Re: ChatGPT Enterprise

#320

Post by TillE »

atleastoptimal wrote: post #266
warthog wrote: post #159

Well the message in this video certainly did not age well: https://www.youtube.com/watch?v=smHw9kEwcgM TLDR: This might have just killed a LOT of startups

There's a typical presumed business intuition that any large company will confer business to a host of "satellite companies" who offer some offshoot of the product's value proposition but catered to a niche sector. Most of these are however just "OpenAI API + a prefix prompt + user interface + marketing". The issue is (which has been brought up since the release of the GPT-3 API 3 years ago) that no startup can offer…

This has been the weirdest part of the current wave of AI hype, the idea that you can build some kind of real business on top of somebody else's tech which is doing 99.9% of the work. There are hard limits on how much value you can add.

If you want to build something uniquely useful, you probably have to do your own training at least.

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