ChatGPT Enterprise
openai.com
openai.com
From our discussions with enterprises (trying to sell our LLM apps platform), we quickly learned how sensitive enterprises are when it comes to sharing their data. In many of these organizations, employees are already pasting a lot of sensitive data into ChatGPT unless access to ChatGPT itself is restricted. We know a few companies that ended up deploying chatbot-ui with Azure's OpenAI offering since Azure claims to not use user's data (https://learn.microsoft.com/en-us/legal/cognitive-services/o...).
We ended up adding support for Azure's OpenAI offering to our platform as well as open-source our engine to support on-prem deployments (LLMStack - https://github.com/trypromptly/LLMStack) to deal with the privacy concerns these enterprises have.
We also provide support and some premium processors for enterprise on-prem deployments.
It is possible to use llama or bert models to generate embeddings using LocalAI (https://localai.io/features/embeddings/). This is something we are hoping to enable in LLMStack soon.
Except many companies deal with data of other companies, and these companies do not allow the sharing of data.
"They're huge pussies when it comes to security" - Jan the Man[0]
[0] https://memes.getyarn.io/yarn-clip/b3fc68bb-5b53-456d-aec5-4...
To effectively sue you, I believe the plaintiff would have to prove the LLM you were using was trained on that IP and it was not in the public domain. Neither seems very doable.
Being able to have your legal counsel tell them to go bug openAI could potentially save you from quite a few anklebiters all seeking to get their own piece.
Ex. CoPilot can insert whole blocks of code with comments and variable names from copyrighted code, if those aspects are sufficiently unique then it's extremely unlikely to be produced any way other than coming from your code. If the code isn't a perfect copy then it's trickier, but that's also the case if I copy your code and remove all the comments, so it's still not all that different from the current status quo.
The bigger question is who gets sued, but I can't imagine any AI company actually making claims about the copyright status of the output of their AI, so it's probably on you for using it.
We won't know if this is legally sound until a company who isn't forbidding A.I. usage gets sued and they claim this as a defense. For all we know the court could determine that, as long as the content isn't directly regurgitated, it's seen as fair use of the input data.
There are some startups working in the space that essentially plan to do something like this. https://www.konfer.ai/aritificial-intelligence-trust-managem... is one I know of that is trying to solve this. They enable these foundation model providers to maintain an inventory of training sources so they can easily deal with coming regulations etc.
You’ve got to be early, but not so early you get legal or business disruptions or concequences.
It’s quite the balancing act for exec teams.
i.e. Without ChatGPT an employee could still copy and paste something from somewhere. ChatGPT actually doesn't change the equation at all.
From my view, copying information from Google search results isn't that much different from copying the response from ChatGPT.
Notably Stack Overflow's license is Creative Commons Attribution-ShareAlike, which I believe very people actually realize when copying snippets from there.
A lot of the snippets would not meet the standard for copyrightable code, though. At least that’s my understanding as non-lawyer.
Realistically you can prove that just as well as you can prove that employees aren't using ChatGPT via their cellphones.
There are also organizations that forbid the use of Stack overflow. As long as employees don't feel like you're holding back their career and skills by prohibiting them from using modern tools, and keep working there, hey. As long as you pay them enough to stay, people will put up with a lot, even if it hurts them.
Especially if you start understanding how to refine your prompts to the point where you use a single thread for project management and use that thread to generate prompts for other threads.
Not all value to be gained from this is purely copypasta.
A guy could wonder why so many of us do not use those answers.
Could it be the details complicate things just enough to take the easy answer off the table?
Perhaps it is just me. What say you?
problem is most of the code chatgpt spouts is wrong, in so many subtle ways, that sometimes you just have to run it to prove it.
so basically you have to be better than chatgpt at that particular task to spot its mistakes.
using it blindly it's similar to the Gell-Mann amnesia effect
https://theportal.wiki/wiki/The_Gell-Mann_Amnesia_Effect
said by someone who uses chatgpt extensively, it is good for the structure, to get an idea, but as a code generator it kinda sucks.
Interestingly, the same applies to text-to-image programs. Once you used these for a while, you realize their utility and value are little more than an inspiration or a starter. Even if you wanted to ignore the ethical implications, very little they produce is useable. LLM are amazing. However, their end-product application is overrated.
I am not a programmer and only know some very rudimentary HTML and Java. After hearing everyone enthuse about how they use ChatGPT for everything, I thought that I could use it to generate a page that I thought sounded simple enough. Gist of it was that I needed 100 boxes of the same dimensions that text could be inputted into. I figured that it'd be faster with AI than with me setting up an excel sheet that others would have to access.
Instead, the AI kept spitting out slightly-off code, and no matter how much reiterations I did it did not improve. Had I known the programming language, I would have known what needed to be changed. I think that a lot of highly experienced people are using it as a short-hand to get started, and a lot of inexperienced people are going to use it to produce a lot of shoddy crap. Anyone relying on ChatGPT that doesn't already know what they're doing is setting themselves up for failure.
It's a tool that can help, just like an IDE, code generators, code formatters, etc. No need to talk down on it in that fashion, and there's no need to look down your nose at tools or the people that use it.
And like crutch for someone who cannot walk without it.
Or glasses (which I use) and allow me to regain almost as good vision as someone with no deformity of eyeballs.
I never had issues with quickly writing a draft or typing in code. I do realise that for a lot of people, starting on a green field is hard, but for me it's easier.
My going hypotheses is that people are just different, and some get true value out of it while others don't. If it works for you, I'm not gonna call you names for it.
If you do something done 10000 times before or is mix of two things done over and over then you are more likely to get advise.
Useless for anything that requires originality, elaborate humor, or finesse.
I use GitHub Autopilot mainly for smaller chunks of code, another kind of autocompletion, which basically safes me from typing, Googling or looking into the docs, and therefore keeps me in the flow.
An intern in college could do that, but it isn’t worth our time to do.
For this function, write the unit tests. Now you do not have anything that you can blindly commit, but you are at the stage where you are reviewing code.
Could you do all of this by hand? Sure but you never would, you would use an IDE. Chatgpt is better than an IDE when you know how to use it.
Official documentation is still available…
Microsoft/OpenAI are selling a service. They’re both reputable companies. If it turns out that they are reselling stolen data, are you really liable for purchasing it?
If you buy something that fell of a truck, then you are liable for purchasing stolen goods. But if it turns out that all the bananas in wall mart were stolen from cosco you’re not as a customer liable for theft.
Similarly, I don’t know if Clarkson Intelligence have purchased proper license for all the data they are reselling. Maybe they are also scraping some proprietary source and now you are using someone else’s IP.
How legitimate the IP concern is and whether it holds up in court is one thing, but finger pointing will probably not be sufficient.
Actually, that would be fencing stolen goods and customers could have obligations.
Cases of bananas is a bit silly as returning bananas would be not possible and value is too small to bother.
But imagine reputable car dealer selling stolen cars, repossession is far more likely here.
So why do we care from where LLMs learn?
same difference there is between painting your own fake Caravaggio and buying a fake Caravaggio (or selling the one you made).
the second one is forgery, the first one is not.
technically it is still a tool you are using, differently from doing it on your own, with your hands, using your own brain cells, that you trained over the decades, instead of using a virtual electronic brain pre-trained in hours/days by someone else on who knows what.
Is that a forgery? Have you infringed on the copyright on all the paintings you looked at?
They are not the same because an LLM is a construct. It is not a living entity with agency, motive, and all the things the law was intended for.
We will see new law as this tech develops.
For an analogy, many people call infringement theft and they are wrong to do so.
They will focus on the someone getting something without having followed the right process part while ignoring the equally important someone else being denied the use of, or loss of property part.
The former is an element in common between theft and infringement. And it is compelling!
But, the real meat in theft is all about people losing property! And that is not common at all.
This AI thing is similar. The common elements are super compelling.
But it just won't be about that in the end. It will be all about the details unique to AI code.
> Most people will [privilege meat]
"A person is smart. People are dumb, panicky dangerous animals, and you know it". I agree that humans are likely to pass bad laws, because we are mostly just dumb, panicky dangerous animals in the end. That's different than asking an internet commentor why they're being so confident in their opinions though.
Full stop. We've not done that yet. When we do, we can revisit the law / discussion.
We can remedy "construct" this way:
Your engineered human would be a being. Being a being is one primary difference between us and these LLM things we are toying with right now.
And yes, beings are absolutely going to value themselves over non beings. It makes perfect sense to do so.
These LLM entities are not beings. That's fundamental. And it's why an extremely large number of other beings are going to find your comment laughable. I did!
You are attempting to simplify things too much to be meaningful.
And I'd like if this were simple. Unfortunately there's too many people throwing around over-simplifications like "They are not the same because an LLM is a construct" or "These LLM entities are not beings". If you'll excuse the comparison, it's like arguing with theists that can't reason about their ideological foundations, but can provide specious soundbites in spades.
First and foremost:
A being is a living thing with a will to survive, need for food, and a corporeal existence, in other words, is born, lives for a time, then dies.
Secondly, beings are unique. Each one has a state that ends when they do and begins when they do. So far, we are unable to copy this state. Maybe we will one day, but that day, should there ever be one, is far away. We will live our lives never seeing this come to pass.
Finally, beings have agency. They do not require prompting.
https://en.m.wikipedia.org/wiki/Turritopsis_dohrnii
Also twice now you've said the equivalent of "it hasn't happened yet so no need to think about the implications". Respectfully, I think you need to ponder your arguments a bit more carefully. Cheers.
They've got a few fantastic attributes, lots of different beings do. You know the little water bear things are tough as nails! You can freeze them for for a century wake them up and they'll crawl around like nothing happened.
Naked mole rats don't get any form of cancer. All kinds of things the beans present in the world that doesn't affect the definition at all.
You didn't gain any ground with that.
And I will point out, it is you who has the burden in this whole conversation. I am clearly in the majority if you want things with what I've said. And I will absolutely privilege meets face over silicon any day, for the reasons I've given.
You, on the other hand, have a hell of a sales job ahead of you. Good luck maybe this little exchange helped a bit take care
So for the sake of argument I'll just amend that and say we can't copy their state. Each being is unique and that's it. They aren't something we copy.
And yes that means all of us that thinks somehow they're going to get downloaded into a computer? I'll say it right here and now that's not going to fucking happen.
quoting from your link
although in practice individuals can still die. In nature, most Turritopsis dohrnii are likely to succumb to predation or disease in the medusa stage without reverting to the polyp form
This sentence does not apply to an LLM.
Also, you can copy an LLM state and training data and you will have an equivalent LLM, you can't copy the state of a living being.
Mostly because a big chunk of the state is experience, like for example you take that jellyfish, cut one of its tentacles and it will be scarred for life (immortal or not). That can't be copied and most likely never will.
Not to say what will/won't happen. In practice, what I've seen doesn't scare me much in terms of what LLMs produce vs. what a person has to clean up after it's produced.
The importance of the individual painting diminishes at this scale.
Because humans aren't computers and the similarities between the two, other than the overuse of the word "learning" in the computer's case, are nonexistant?
Humans and Computers are 2 wholly separate entities, and there's 0 reason for us to conflate the two. I don't care if another human looks at my code and straight up copies/pastes it, I care very much if an entity backed by a megacorp like Micro$oft does the same, en-masse, and sells it for profit, however.
However, on the other hand we also have the scale at which they learn, which kind of makes every individual source line of code they learn from pretty unimportant. Learning at this scale is statistical process, and in most cases individual source snippets diminish in the aggregation of millions of others.
Or to put it the other way round, the actual value lies in the effort of collecting the samples, training the models, creating the software required for the whole process, putting everything into a good product and selling it. Again, in my mind, the importance of every individual source repo is too small at this scale to care about their license.
That is a fear for companies because the individual source snippets and the knowledge "learned" from them is seen as a competitive advantage of which the sources are an integral part - and I think this is a fair point from their side. However then the exact same argument should apply in favour of paying the artists, writers, coders etc whose work has been used to train these models.
So it sounds like they are trying to have their cake and eat it too.
Because what companies want to hide are usually secrets, that are available to (nearly) no one outside of the company. It’s about preventing accidental disclosure.
What AIs are trained on, on the other hand, is publicly available data.
To be clear: what could leak accidentally would have value of course. But here it’s about the single important fact that gets public although it shouldn’t, vs. the billions of pieces from which the trained AI emerges.
Humans look at a few examples and extrapolate…
Also humans didn't evolve in billion of years.
This is all relevant because humans aren't born as random chemical soup. We come with pre-trained weights from billions of years of evolution, and fine-tune that with enormous amounts of sensory data for years. Only after that incredibly complex and time-consuming process does a person have the ability to learn from a few examples.
An LLM can generalize from a few examples on a new language that you invent yourself and isn't in the training set. Go ahead and try it.
I’d guess it’s exactly the same with humans: a human that received good general education can quickly learn specific things like C.
There is a legal difference between learning from something and truly making your own version and simply copying.
It's vague of course - take plagiarism in a university science essay - the student has no original data and very likely no original thought - but still there is a difference between simply copying a textbook and writing it in your own words.
Bottom line - how do we know the output of the LLM isn't a verbatim copy of something with the license stripped off?
Because humans dont put the "Shutterstock" watermark logo on the images they produce.
Viagra Boys - In Spite Of Ourselves (with Amy Taylor)
I absolutely love that the entirety of the video is unpurchased stock footage with the watermark still on it. This is cinematic gold.
* well, most ...
I personally believe that in addition to OpenAI's offering, the ability to swap to an open source model e.g. Llama-2 is the way to go for enterprise offerings in order to get full control.
The SOC2 framework is complex and compliance can be expensive. This can lead organizations to focus on ticking the boxes rather than implementing meaningful security controls.
SOC2 is not a good universal metric for understanding an organization's security culture. It's frightening that this is the best we have for now.
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 stuff seems boring but it's an easy way to scale the addressable market.
Though at this point, they are probably simply supply-limited. Just serving the first wave will keep their capacity at a maximum.
(I do wonder if they'll start offering batch services that can run when the enterprise employees are sleeping...)
OpenAI offers baa to select customers.
You can get a BAA through Azure’s OpenAI service though, I believe the details are located in this document:
https://azure.microsoft.com/en-us/resources/microsoft-azure-...
https://help.openai.com/en/articles/5722486-how-your-data-is...
That said, for enterprises that use the consumer product internally, it would make sense to pay to opt-out from that input being used.
What is actually stopping them? Most companies won't have the fire power to go against microsoft backed openai. How can we ensure that they can't violate this? How can they be practically held accountable?
This as far as I am concerned is "Trust me bro!". How is it not otherwise?
Are you claiming this because they used copyrighted material as training data? If so, I think you're starting from the wrong point.
Please correct me if I'm wrong, but last I heard using copyrighted data is pretty murky waters legally and they're operating in a gray area. Additionally, I don't think many open source licenses explicitly forbid using their code as training data. The issue isn't just that most other companies don't have the resources to go up against Microsoft/OpenAI, it's that even if they did, it isn't clear whether the courts would find that Microsoft/OpenAI did anything wrong.
I'm not saying that I side with Microsoft/OpenAI in this debate, but I just don't think this is as clear cut as you're making it seem.
All open source license comes under copyright law. It means if they violate the OSS license, the license is void and the tech/material becomes copyright protected. So yes, it would mean that it is trained on copyrighted material.
> Additionally, I don't think many open source licenses explicitly forbid using their code as training data.
It doesn't forbid. For example, permissive license like MIT can be used to train LLM's if they are in compliance. The only requirement when you train on a MIT licensed codebase is that you need to provide attribution. It is one of the easiest license to comply. It means, you just need to copy paste the copyright notice. The below is the MIT license of Emberjs.
Copyright (c) 2011 Yehuda Katz, Tom Dale and Ember.js contributors
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
This copyright notice needs to be somewhere in ChatGPT's website/product to be in compliance with MIT license. If it is not, MIT license is void and you are violating the license. The end result is you are training on copyrighted material. I am more than happy to be corrected if you could find me any single OSS license attribution shown somewhere for training the openai model.
Also, this can be still be fixed by adding the attribution for the code that is trained on. THIS IS MY ARGUMENT. The absolute ignorance and arrogance is their motivation and agenda.
Which is why I am asking, WHAT IS STOPPING THEM FROM VIOLATING THEIR OWN TERMS AND CONDITIONS FOR CHATGPT ENTERPRISE?
My whole point is that I don't think that's legally true at the moment. There's enough difference in how generative AI works compared to pretty much anything before it that what ChatGPT legally does is up for debate. If a court rules that what ChatGPT does counts as redistribution then yes, I agree that they're likely violating copyright law, but AFAIK that ruling hasn't happened yet.
A license exist with terms. You can abide the terms and use it. It doesn't matter whether an AI, a person or an alien from a distant planet is using it. They can follow the terms. This is not a technical challenge but arrogance to abide.
Also, are you saying a model like chatgpt can do so much complex tasks and text processing but can't recognise an OSS license text of 20ish lines?
First offense could be excused as "blazing a trail and burning down the forest by accident".
But now they have a direct business contract with bigger companies that can lawyer up way better than open source foundations that live on donations and goodwill of code contributors.
Imagine they make a huge deal with Sony or Dell and either company can prove their "secure" enterprise plan was used for corporate espionage.
The legal and reputation repercussion could sink even a fortune 100 company
The same would apply to any other application. What if company A uses code from company B via ChatGPT/CodePilot because company B's code was used as training data? Imagine a startup database company using Oracle's database code through use of this technology.
And if a proprietary company accidentally uses GPL code through these tools, and the GPL project can prove that use, then the proprietary company will be forced to open source their entire application.
Top 1 misconception about open source licenses.
GPL doesn't mean if you use the code your entire project will become GPL.
GPL means if you use the code and your project is not GPL-compatible, you are committing copyright infringement. As if you stole proprietary code. If brought to the court, it would be resolved just like other copyright infringement cases.
Microsoft/Amazon/Google already have competitor's data in their cloud. They could even fake encryption to get all the customer's disk access. Also most employees use google workspace or office 365 cloud to store and share confidential files. How is different with OpenAI that makes it any more worrying?
> For all enterprise customers, it offers:
> Customer prompts and company data are not used for training OpenAI models.
> Unlimited access to advanced data analysis (formerly known as Code Interpreter)
> 32k token context windows for 4x longer inputs, files, or follow-ups
Having conversations saved to go back to like in the default setting on Pro, that's disabled when a Pro user turns on the privacy setting, is another big difference.
" We do not train on your business data or conversations, and our models don’t learn from your usage. ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. "
So i have one primary profile logged in normally and a separate tab which i turn off chat history.
So now I get best of both worlds
They want chat history and no training on the same conversation.
Chrome extension is a no go.
What’s with the argumentative tone? Do you think that the replier doesn’t know this?
If we had to waste that much time re-inventing the loaf of bread, and then making sure that my neighbors didn’t decide to throw some raisins in my loaf, that we never get around to figuring out the next best thing: slicing it.
That's great. But can customer prompts and company data be resold to data brokers?
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.
You absolutely want a privately trained company model.
What you want is to get an existing model to search a well built index of your data and use that information to reason about things. That way you also always have entirely up to date data.
People aren't missing the use cases you describe, they're disagreeing as to how to achieve those.
>>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 solutions for all of these things. If you can't mine your own data competently, you've got bigger problems than not having AI doing it for you. It means you don't have humans who understand what's going on. AI is not the answer to everything.
ChatGPT only works as well as it does because it’s been trained on a corpus of “internet accepted” answers. It can’t fucking reason about raw data. It’s a language model.
Such dark patterns, plus their involvement in crypto, their shoddy treatment of paying users, their security incidents... make it harder for me to feel good about OpenAI spearheading the introduction of (real) AI into the world today.
While we're at it, another exaggeration I made is "security incidents"; in fact, I am only aware of one.
Interesting. My opinion is it is a great product that works well for me, I don't find my treatment as a paying user shoddy, and their security incident gives me pause.
> I don't find my treatment as a paying user shoddy
There are justifications for the terrible uptime that are perfectly valid, but in the end, a customer-focused company would have issued a refund to the paying customers for the months during which they were shafted by OpenAI prioritizing growth.
That doesn't mean OpenAI isn't terrific in some ways. They're also lousy in others. With so many tech companies, the lousy aspects grow in significance as the years pass. OpenAI, because of all the reasons in my parent comment, is not off to a great start, imo.
This is borderline extortion, and it's hilarious to witness as someone who doesn't have a dog in this fight.
Companies are trying to maximize profit; they are not trying to minimize costs so they can continue to do you favors.
These arguments creep up frequently on HN: "This company is doing X to their customers to offset their costs." No, they are a company, and they are trying to make money.
Nobody is arguing that there's an exact matching of value to the company between 1 user giving OpenAI permission to use their chat history for future training and 1 user paying $20/month. But based on your simplistic view, no company would ever offer a free tier because it's not directly maximising revenue.
It's very obvious that getting lots of real-world examples of users using ChatGPT is beneficial for multiple reasons - from using in future training runs (or fine tuning), to analysing what users what to use LLMs for, to analysing what areas ChatGPT is currently performing well or badly in, etc.
So it's not about blankly and entirely "offsetting costs", it's about the fact that both money into their bank account and this sort of data into their databases are both beneficial to the long-term profitability of the company even though only one of them is direct and instant revenue.
Before ChatGPT was released for the world to use, OpenAI were even paying people (both employees and not) to have lots of conversations with it for them to analyse. The exact same logic that justified that justifies allowing some users to pay some or all of the fee for the service in data permissions rather than money.
I'm speaking from experience making these sorts of business decisions, and to a company like OpenAI this is just basic common sense.
I don't think they're removing all instances of your company from their existing data sources, which would make sense to call "borderline extortion".
That's great. But can customer prompts and company data be resold to data brokers?
ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. Our new admin console lets you manage team members easily and offers domain verification, SSO, and usage insights, allowing for large-scale deployment into enterprise.
I think this will have a solid product-market-fit. The product (ChatGPT) was ready but not enterprise. Now it is. They will get a lot of sales leads.
- GPT-4 (ChatGPT Plus): has max 4K tokens ?
- GPT-4 API: has max 8K tokens (for most users atm)
- GPT-3.5 API: has max 16K tokens
I'd consider the 32K GPT-4 context the most valuable feature. In my opinion OpenAI shouldn't discriminate in favor of large enterprises. It should be equaly available to normal (paying) customers.
- Codebases
- Documents (by way of connection to your Box/SharePoint/GSuite account)
- Knowledgebases (I'm thinking of something like a Notion here)
I'm really looking forward to seeing what they come up with here, as I think this is a truly killer use case that will push LLMs into mainstream enterprise usage. My company uses Notion and has an enormous amount of information on there. If I could ask it things like "Which customer is integrated with tool X" (we keep a record of this on the customer page in Notion) and get a correct response, that would be immensely helpful to me. Similar with connecting a support person to a knowledgebase of answers that becomes incredibly easy to search.
Fine-tuning isn't great at learning knowledge. It's good at adopting tone or format. For example, a chirpy helper bot, or a bot that outputs specifically formatted JSON.
I also doubt they're going to have a great system for fine-tuning. Successful fine-tuning requires some thought into what the data looks like (bare docs won't work), at which point you have technical people working on the project anyway.
Their future connection system will probably be in the format of API prompts to request data from an enterprise system using their existing function fine-tuning feature. They tried this already with plugins, and they didn't work very well. Maybe they'll come up with a better system. Generally this works better if you write your own simple API for it to interface with which does a lot of the heavy lifting to interface with the actual enterprise systems, so the AI doesn't output garbled API requests so much.
I do think an open line of research is some way for users to just add arbitrary docs in an easy way to the LLM.
I certainly don't expect a nice drag-and-drop interface to put my Office files and then ask questions about it coming in 2023. Maybe 2024?
Unfortunately even if we do get this, I expect there will be significant ecosystem lock-in. Like, I imagine Microsoft is aiming for something like this, but you'd need to use all their stuff.
Rather than wanting to import N documents per month, I would want to import M documents all at once, then use that set of documents until at some future time I want to import another batch of K documents (probably a lot smaller than M) or just one document once in a while.
By limiting it to a fixed amount of documents per month, it eliminates all the applications where you need to import a complete corpus before the service is useful.
Anyone knows how this new capability works in terms of where the model inference be done? Would it still be at the OpenAI side or is this going to be at the customer side?
After using RAG with pgvector for the last few months with temperature 0, it's been pretty great with very little hallucination.
The small context window is the limiting factor.
In principle, I don't see the difference between a bunch of fine-tuned prompts along the lines of "here is another context section: <~4k-n tokens of the corpus>", which is the same as what it looks like in a RAG prompt anyway.
Maybe the distinction of whether it is for "tone" or "context" is based on the role of the given prompts and not restricted by the fine-tuning process itself?
In theory, fine-tuning it on ~100k tokens like that would allow for better inference, even with the RAG prompt that includes a few sections from the same corpus. It would prevent issues where the vector search results are too thin despite their high similarity. E.g. picking out one or two sections of a book which is actually really long.
For example, I've seen some folks use arbitrary chunking of tokens in batches of 1k or so as an easy config for implementation, but that totally breaks the semantic meaning of longer paragraphs, and those paragraphs might not come back grouped together from the vector search. My approach there has been manual curation of sections allowing variations from 50 to 3k tokens to get the chunks to be more natural. It has worked well but I could still see having the whole corpus fine-tuned as extra insurance against losing context.
Other considerations: (A) would you fine-tune daily? weekly? as data changes? (B) Cost and availability of GPUs (there's a current shortage)
My experience is that RAG is the way to go, at least right now.
But you have to make sure your retrieval engine work optimally: getting the very most relevant pieces of text from your data: (1) using a good chunking strategy that's better than arbitrary 1K or 2K chars (2) using a good embedding model (3) Using hybrid search, and a few other things like that.
Certainly the availability of longer sequence models is a big help
Sharing this relevant discussion from LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7101638...
They support uploading documents to it for that via that code interpreter, and they're adding connectors to applications where the documents live, not sure what more you're expecting.
Edit: spelling
That being said you can use fine tuning to improve retrieval, which indirectly improves recall. You can do things like fine tune the model you're getting embeddings from, fine tune the LLM to craft queries that better match a domain specific format, etc.
It won't replace the expensive on-the-fly retrieval but it will let you be more accurate in your replies.
Also retrieval can be infinitely faster than inference depending on the domain. In well defined domains you can run old school full text search and leverage the LLMs skill at crafting well thought out queries. In that case that runs at the speed of your I/O.
TLDR: This might have just killed a LOT of startups
Personally I love what Evenup Law is doing. Basically find a segment of the market that runs like small businesses and that has a lot of repetitive tasks they have to do themselves and go to them. Though I can't really think of other segments like this :)
If you want to build an AI start-up and need a LLM, you must use Llama or another model than you can control and host yourself, anything else is basically suicide.
It's not free if you have paying clients.
> If you want to build an AI start-up and need a LLM, you must use Llama or another model than you can control and host yourself, anything else is basically suicide.
You're still doing market research for OpenAI. Just because you aren't using their model doesn't mean they can't copy your UX. Prompts are not viable trade secrets after all.
No early stage start-up has revenues covering their expenses. But in fact you're right, it's not even “free”, it's “investor-subsidized” market research.
> You're still doing market research for OpenAI. Just because you aren't using their model doesn't mean they can't copy your UX. Prompts are not viable trade secrets after all.
Prompt aren't viable trade secret, but fine-tuning datasets and strategies, customer data[1], customer habits, user feedback, etc. are. And if you're using OpenAI, you're giving all that to them. Also, given their positioning, they cannot address any use-case that involve deploying your model inside your customer's infrastructure, so this kind of market research is completely irrelevant for them.
[1]: And don't get fooled by wordings saying that they don't train on customer data, they are still collecting much more info that what you'd like them to. For instance, even just knowing the context size that users like to work with in different scenario is a really interesting data for them, and you can be sure that they collect it and adapt to.
If you want to build something uniquely useful, you probably have to do your own training at least.
Code Interpreter was a pretty bad name (not exactly meaningful to anyone who hasn't studied computer science), but what's the new name? "advanced data analysis" isn't a name, it's a feature in a bullet point.
And it does seem "better" than standard 4 for normal tasks
What a terrible name! They should have asked ChatGPT for suggestions.
https://github.com/microsoft/azurechatgpt
Past discussion:
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-generation-webui (a web ui w/ different backends)
- https://github.com/ggerganov/llama.cpp (fast local LLM runner)
- https://github.com/go-skynet/LocalAI (has an openai-compatible api)
- https://github.com/trypromptly/LLMStack (build and run apps locally with LocalAI support - I'm a maintainer)
The UI is relatively mature, as it predates llama. It includes upstream llama.cpp PRs, integrated AI horde support, lots of sampling tuning knobs, easy gpu/cpu offloading, and its basically dependency free.
GPTQ has also been merged into Transformers library recently ( https://huggingface.co/blog/gptq-integration ).
GGML quantization format used by llama.cpp also supports (8,6,5,4,3, and 2 bit quantization).
On a related note it doesn't seem like many local runners are leveraging techniques like PagedAttention yet (see https://vllm.ai/) which is inspired by operating system memory paging to reduce memory requirements for LLMs.
It's not quite what you mentioned, but it might have a similar effect! Would love to know if you've seen other methods that might help reduce memory requirements.. it's one of the largest resource bottlenecks to running LLMs right now!
The hint for me is that the models compress so well, that suggests the information content is much lower than the size of the uncompressed model indicates which is a good reason to investigate which parts of the model are so compressible and why. I haven't looked at the raw data of these models but maybe I'll give it a shot. Sometimes you can learn a lot about the structure (built in or emergent) of data just by staring at the dumps.
Full Disclosure: This is my tool
Normally we ban accounts that do nothing but promote their own links, but as you've been an HN member for years, I'm not going to ban you, but please do stop doing this! We want people to use HN to read and post things that they personally find intellectually interesting—not just to promote something.
If I go back far enough (a couple hundred comments are so), it's clear that you used to use HN in the intended spirit, so this should be fairly easy to fix.
Optimal business strategy. Makes it look like there's more competition, and changes the decision from "do we use ChatGPT" to "Which GPT vendor do we use?"
The exclusive license refers to having access to the models, which is probably why they were able to finetune Bing AI.
OpenAI is a tiny company, relative to Microsoft. They can’t afford to build a giant partner network. At best, they can offer a forum-supported set of products for the little guys and a richly supported enterprise suite. But the middle market will be Microsoft’s to own, as they always do.
Another question is does the latency even matter? Today, same employees ping their colleagues for answers and wait for hours till get a reply. GPT would be faster (and likely more accurate) in most of those cases.
I can actually see this saving a lot of time for employees (1-10% maybe?), so the price is most likely calculated on that and a few other factors. I think most big orgs will eat it like cake.
I’ve used it extensively to speed up the process of making presentations, drafting emails, naming things, rubber-ducking for coding, etc.
"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 instructions to 'backup the core database' being mixed up with the instructions for 'emergency wipe of sensitive data'. Following the instructions carefully and efficiently, Jimmy successfully wiped out our core database, the backups, and our local tapes overnight."
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.
Everyone also seems to overlook how much time and resources it takes for these models to be trained/fine tuned on a corpus of knowledge. Researches have calculated it probably took OpenAI the equivalent of 355 years on a single NVidia V100 to train up GPT 3. [1] Clearly they used more horsepower in parallel, which is a foreseen problem right now for other reasons. [2]
[1] https://lambdalabs.com/blog/demystifying-gpt-3
[2] https://www.pcworld.com/article/2020375/the-ai-boom-could-cr...
> Contact sales
Oops. Scary.
I'm missing the Teams plan: transparent pricing with a common admin console for our team. Yes, fast GPT-4, 32k context, templates, API credits... they're all very nice-to-haves, but just the common company console would be crucial for onboarding and scaling-up our team and needs without the big-bang "enter-pricey" stuff.
Wouldn't surprise me. We had a vendor whose product we had used at relatively reasonable rates for multiple years suddenly have a pricing model change. It would have seen our cost go from $10k/yr to $100k/yr. As a small nonprofit we tried to engage them in any sort of negotiation but the response was essentially a curt "too bad." Luckily a different vendor with a similar product was more than happy to take our $10k.
We have a model that I see a lot of others do, even if they don't publicize. We have free, OSS, and cheap SaaS tiers fine for many of our academic users, and when a small group really wants the full enterprise version, we generally offer a heavily discounted pricing model to make that affordable too. The only exception here is when it is a true enterprise sale like a shared resource for a large number of users, and we'd still have to think there too.
The reason is it keeps their low budgets and thus their ROI in alignment, which is why this is pretty normal. So again, I'd recommend asking and just clarifying your are a NGO/EDU. No 100% guarantee, but should be common.
A lot of value in some SaaS apps is in the initial investment it took to build it, not in the cost to host a customer's assets.
If the runtime costs of a new customer are negligible, would you rather have 0K or 20K?
If dodgy pricing/sales tactics didn't work then Oracle would be bankrupt instead of a 300 billion dollar company.
But hopefully it does give a little more motivation to all of the other great work going on with open models to keep trying to catch up.
I refer to the concept that output could be deemed free of copyright because they are not created by a human author, or that derivative works can be potential liabilities because they resemble works that were used for training data or whatnot (and we have no idea what was really used to train).
There was the recent court decision confirming:
https://www.cooley.com/news/insight/2023/2023-08-24-district...
Seems odd to start making AI systems a data-at-the-center-of-the-company technology when such basic issues exist.
Is this not a concern anymore?
This decision seems specifically about whether the ai itself can hold the copyright as work for hire, not whether output generated by ML models can be copyrighted.
In practice, if you suspect something was written by an AI and are considering copying it, you would be safer to just ask an AI to write you one as well.
Are people really that different from an AI model when it comes to generating works?
Isn't the underlying and overarching concept and process similar when it comes to this? (Optimizing for a certain select scenario or outcome by iteratively going through ideas and generating more over time.)
What happens when both the model and human converge and it becomes truly indecipherable when it comes to telling the difference?
Shower thoughts. I guess.
The only think that the ruling said is basically that the most low effort version of AI does not have copyright protection.
IE, if you just go into midjourney and type in "super cool anime girl!" and thats it, the results are not protected.
But there is so much more you can do. For example, you can generate an image, and then change it. The resulting images would be protected due to you adding the human input to it.
https://www.theverge.com/23444685/generative-ai-copyright-in...
https://news.bloomberglaw.com/ip-law/openai-facing-another-c...
Etc…
It is all a hypothetical issue that has not been enforced as of yet.
There’s undeniably similar amounts of greed, although TK seems to genuinely enjoy being a bully versus sama is more of a futurist.
It's a pretty strange ruling at odds with precedent, and it has not been tested in court.
Traditionally all that's required for copyrightability is a "minimal creative spark", i.e. the barest evidence that some human creativity was involved in creating the work. There really hasn't traditionally been any lower bound on how "minimal" the "spark" need be -- flick a dot of paint at a canvas, snap a photo without looking at what you're photographing, it doesn't matter as long as a human initiated the work somehow.
However, the Copyright Office contends that AI-generated text and images do not contain a minimal creative spark:
https://www.copyright.gov/ai/ai_policy_guidance.pdf
This is obviously asinine. Typing in "a smiling dolphin" on Midjourney and getting an image of a smiling dolphin is clearly not a program "operat[ing] randomly or automatically without any creative input or intervention from a human author".
If our laws have meaning, it will be overruled in court.
Of course, judges are also susceptible to the marketing-driven idea that Artificial Intelligence is a separate being, a translucent stock photo robot with glowing blue wiring that thinks up ideas independently instead of software you must run with a creative input. So there's no guarantee sanity will prevail.
The argument is that if you trained the model with copyrighted data, and then you or someone else separately used the model to generate novel media which was not legally similar enough to any copyrighted work to make it a copyright violation, that that isn't violating copyright, it's fair use. Basically, using it to make your own original content is legal, and using it to create an unauthorised reproduction of a copyrighted work is illegal. Just like all other software.
This is why music engravers can sell entire books of classical sheet music from *public domain* works. They become the owners of that specific expression. (Their arrangement, font choice, page layout, etc)
If the AI content is public domain, and the work it generates is incorporated into some other work, the entity doing the incorporation owns the work. It’s not permanently tainted or something as far as I know.
We'd be potentially very interested in an official internal-facing ChatGPT, with a caution that the economics of the consumption-based model have so far been advantageous to us, rather than a flat fee per user per month. I can say that based on current usage, we are not spending anywhere close to $20 per user per month across all of our staff.
[1] We used this: https://github.com/dotneet/smart-chatbot-ui
https://blogs.microsoft.com/blog/2023/07/18/furthering-our-a...
https://embracethered.com/blog/posts/2023/chatgpt-webpilot-d...
Seems like a no-go for companies if an attacker can steal stuff.
It seems like there's far more ways for extensive automation by decision making via ChatGPT to cause problems than solve problems. With my experience using it for programming, anything that is non-trivial basically requires you to do it all yourself because ChatGPT will hopelessly get it wrong.
By the way, I don't have to win a lawsuit to get some justice; I'll make discovery hurt.
So if OpenAI stole my IP and used it for training (which they probably did, illegally IMO), I guess you're taking that risk if you let your employees use their LLM's.
In the US the saying is "This case will come down to who has the last dime and it won't be you".
> By the way, I don't have to win a lawsuit to get some justice; I'll make discovery hurt.
Even for discovery a common tactic deployed for discovery is to "bury" the other party in discovery materials to the point where they will incur substantial costs just to parse the absurd mountains of materials they send you. Another "bleed you out" strategy.
And yes, I know that money wins in legal battles. That's why I am focusing on discovery before I don't have any.
When they send you several thousand pages (minimum) of random documents you're looking for a smoking gun that's a needle in a haystack.
That takes money and time that very few parties have, coming back to the last dime expression.
I don't know if you've ever deployed your strategy successfully but needless to say I wouldn't count on it as a viable path to protect your IP against companies valued in the tens of billions of dollars.
Well, other than the millions of jobs at stake here. But I’m sure they can just learn to code or become an engineer
Which is a shame because an actual audit of the live training data of these systems could be possible, albeit imperfect. Setup an independent third party audit firm that gets daily access to a randomly chosen slice of the training data and check its source. Something along those lines would give some actual teeth to these statements about data privacy or data segmentation.
It's like slurping the very last capital a worker has out of its mind and soul. Most companies exist to make a profit, not to employ humans.
Paired with the pseudo-mocked-tech-bro self-peddling BS this announcement reads like dystopia to me. Not that technological progress is bad, but technology should empower users (for real, by giving them more control) not increase power imbalances.
Let's see how many people who cheered today will cheer just as happily in 2028. My bet: just a few.
Technology should make life easier.
Automation is good.
A totalitarian state can make your life very comfortable with technology. Wanna trade for freedom?
Automation is the best, if the majority can benefit from it.
Laughable, given my political beliefs
Honestly, I can see it, but there are definitely SOME jobs at risk, and it will almost certainly reduce hiring in junior positions.
I am a manager in a dev team. I have a small team and too many plates spinning, and I’ve been crying out for more hires for years.
I moved to using AI a lot more. ChatGPT and Copilot for general dev stuff, and I’m experimenting with local llama-based models too. It’s not that Im getting these things to fill any one role, but to reduce the burden on the roles we have. Honestly, as things stand, I’m not crying out for more hires any more.
We already have enormous concentration of data in a few places and it's only getting worse. Centralization is efficiency, but the benefits of that get skimmed disproportionally, to the detriment of what allowed these systems to emerge in the first place: our society.
OpenAI loves to talk about a utopia where no one has to work and everyone is paid in Worldcoin (which of course Altman will make a handsome profit off of), but does anyone actually think that GPT-X is leading to this? Some of our most vulnerable members of society will soon find themselves without work, and no easy way to get new work. We don't need to wonder how we as a society will take care of them - all historic evidence points to us doing absolutely nothing.
I bet there will be a lot more effort to build truly open-source models. Also, I wonder why no foundation has yet got involved to pool resources and create large enough models.
It feels like AI needs its own "CNCF".
I'm very impressed by how Meta position itself as "AI for rest of us" position through llama and to an extent PyTorch, although I have no idea how they are going to capitalize that position besides hiring. (vs. Google having a cloud offering.)
It's just "for the rest of us" until we reach a significant number of users and then Meta is gonna come for us.
As a purist do I wish it was fully open source? Yes. Is it restrictive to "the rest of us"? No.
Finally, if you have more than 700M MAUs, you probably have an internal LLM you should be using.
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 business model? Will it make written offers you will be legally-obligated to adhere to? Will it lie about your features and capabilities, or that of your competitors? Will it invent whole-cloth SLAs and KPIs you'll never be able to adhere to? Will it make up arrival and departure times? Medical conditions and prescribed treatments/therapies? Will it glibly give instructions that can get consumers/users who read these statements fired from their jobs, fined, imprisoned or killed?
There are places where you only want to give a generative AI so much room to color outside the lines. And after that, you are going to want traditional procedural logic to take over. Decision-trees from manually-maintained systems and even multi-decadal-honed expert systems.
There are some things where letting a computer make up a plausible response is ... okay-ish. And there are times where you can cause immense amounts of damage by letting things get outside of your control and human review.
This should come with a consumer warning label that would put your typical pharmaceutical safety disclosure to shame.
Now, with all that said, I am amazed at many of the results these LLM-based systems can generate. My main concern is that they are getting so good — or at least so plausible — that they can even fool domain experts unless they read really, really closely. And the experts won't be able to do that at scales of 10s or 100s of thousands of transactions per second.
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 fighter, and suddenly it spits out pages of output. [Though it finally corrects itself and balks to answer when you ask for angle of forward sweep and a formula for the curvature of the surface.]
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: []"
The reality is chat is a terrible interface in the long run. Horrible discoverability, completely non-obvious edges, turns what you might think is an equally accessible tool into the worst case of "you're holding it wrong you've ever seen just judging by what people in these comments are complaining about.
But chat was/is brilliant at making this stuff accessible. I've gone back and learned of incredible things I could have been doing years ago if I had paid more attention to ML, but it wasn't until chat gave us this perfect interface to start from that I suddenly got the spark.
As chat models get more powerful, chat will be the least interesting thing you can do with them. If the model is intelligent enough to convert minimal instructions into complex workflows, why do I need to chat with it? To take it to its logical extreme, why not have one button that just does the right thing?
The more realistic version of that will be industry specific interfaces that focus on the 5 buttons you need to get your job done, along with the X decades of procedure that can guide the LLM to exactly what needs to be done.
Also I think you need to better understand how much hallucination has been driven down recently and its clear path going forward.
https://apnews.com/article/artificial-intelligence-hallucina...
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."
ChatGPT: not even as good as MapQuest.
Checkmate, OpenAI.
"Gee, how can they be making entire buildings out of that?! Why my plastic dinghy could be out at sea for a year and not pick up a lick of rust, meanwhile my steel fishing hooks are rusty if I leave them out on a rainy night!"
Unless it’s essentially fully eliminated, it doesn’t qualitatively change the argument. The output cannot be trusted to go directly to customers and/or business partners. It can’t even be trusted to go to employees that don’t know it’s from AI and might lie if you ask if something strange.
> You even contradict yourself at the end.
There is no contradiction. Read it again.
The statement you provided raises valid concerns about the capabilities and potential risks of advanced language models like ChatGPT Enterprise. Here's a response that addresses these concerns:
"Thank you for sharing your thoughts on ChatGPT Enterprise. It's important to recognize that advanced language models like ChatGPT do indeed have a remarkable capacity to generate human-like responses. However, they lack inherent understanding, critical thinking, and ethical judgment.
OpenAI acknowledges the concerns you've highlighted about accuracy, veracity, and potential consequences of generated content. It's crucial to understand that ChatGPT does not have an inherent "veracity bit" or a built-in mechanism to discern truth from falsehood. As such, it's important for users to exercise caution and discretion when relying on the output generated by the model.
In situations where accuracy and compliance are paramount, it's advisable to rely on established procedural logic, expert systems, and human review. ChatGPT is not a substitute for human oversight, particularly in sensitive areas such as legal, medical, or business transactions.
OpenAI is actively working to improve the control and guidance mechanisms for ChatGPT, and they acknowledge the importance of striking the right balance between automation and human review. As you pointed out, there are instances where generative AI is appropriate and others where traditional procedural logic and human intervention are necessary.
While ChatGPT can be a powerful tool for generating content and ideas, it is incumbent upon users to apply their expertise and judgment to evaluate the output, particularly in contexts where accuracy and adherence to policies are critical.
Your concerns align with ongoing discussions in the AI community about the ethical use and potential limitations of these technologies. As AI systems continue to advance, responsible development, transparency, and user education will remain important factors in their deployment."
It's important to remember that while AI models like ChatGPT have the potential to assist and enhance various tasks, they do not replace the need for human expertise, ethical considerations, and careful oversight in complex and critical scenarios.
Like think of a technical B2B product's technical support engineer who now has a personal assistant trained on the company's full catalog of technical documentation, who can provide instant answers to customer questions that the rep can validate before passing along to the customer on the phone.
Or an overworked public defender who now has an instant paralegal to proof documents or search for and summarize relevant case law that the human lawyer then reviews.
And so on.
Even if no GPU is simultaneously being used for enterprise and normal, they still have a finite amount of compute.
How can we be sure of this? Just take their word for it?
If you notice that some of your confidential info made it into next generations of the model, you'll be able to sue them for big $$$ for breach of contract. That's a pretty good incentive for them not to play stupid games with that.
Yeah... I have no doubt that people at my Fortune 100 company tried it out with their corporate email domains. We have about 80,000 employees, so it seems nearly impossible that somebody wouldn't have tried it.
But, since then the policy has come down that nobody is allowed to use any sort of AI/LLM without written authorization from both Legal and someone at the C-suite level. The main concern is we might inadvertently use someone else's IP without authorization.
I have no idea how many other Fortune companies have implemented similar policies, but it does call the 80% number into question for me.
Surprising it didn't make the initial launch announcement though.
Why can't I, as an individual, have the same features of an Enterprise plan?
What is the logic behind this practice other than profit maximization?
I'm willing to pay more to have unlimited high-speed GTP4 and Longer inputs with 32k token context.
EDIT: since I'm getting a lot of replies. Genuine question: how should I move to get a reasonable price as an individual for unlimited high-speed gpt4 and longer token context?
That’s a real big “other than”…
I don't know, but I can't imagine any other logic.
Maybe posting the price they'd like to charge would scare away almost all interested parties.
Maybe the price they charge you depends more on how much money they think you have than it does on a market's "decision" on what the product is worth.
How much more? That’s the question that “talk to us” enterprise pricing is trying to answer.
What I'm wondering if it means that the minimal price they can offer the service with at profit, is likely to be too steep for anyone like me, who interpret "talk to us" as the online equivalent of showing him the door. The other explanation I see is that there's not many in the camp of users who react to "talk to us" button by closing the tab instead of a deal, but I find that implausible.
I think the answer to that is "no". The problem is that they don't want to reveal the minimal price to their initial round of customers.
There are two basic ways you can think about pricing: cost-plus and value-minus. We programmers tend to like the former because it's clear, rational, and simple. But if you've got something of unknown value and want to maximize income, the latter can be much more appealing.
The "talk to sales" approach means they're going to enter into a process where they find the people who can get the most out of the service. They're going to try to figure out the total value added by the service. And they'll negotiate down from there. (Or possibly up; somebody once said the goal of Oracle was to take all your money for their server software, and then another $50k/year for support.)
Eventually, once they've figured out the value landscape, they'll probably come back for users like you, creating a commoditized product offering that's limited in ways that you don't care about but high-dollar customers can't live without. That will be closer to cost-plus. For example, note Github's pricing, which varies by more than 10x depending on what they think they can squeeze you for: https://github.com/pricing
Why would it be something else than profit maximization? It's a for-profit company, with stakeholders who want to maximize the possible profits coming from it, seems simple enough to grok, especially for users on S̵t̵a̵r̵t̵u̵p̵ ̵N̵e̵w̵s̵ Hacker News.
Are you aware what the entire point of a business is?
https://blogs.microsoft.com/blog/2023/07/18/furthering-our-a...
# OpenAI Offerings
- ChatGPT Free - trains on your data unless you Opt Out
- ChatGPT Plus - trains on your data unless you Opt Out
- ChatGPT Enterprise - does _not_ train on your data
- OpenAI API - does _not_ train on your data
# Microsoft Offerings
- GitHub Copilot - trains on your data
- GitHub Copilot for Business - does _not_ train on your data
- Bing Chat - trains on your data
- Bing Chat Enterprise - does _not_ train on your data
- Microsoft 365 Copilot - does _not_ train on your data
- Azure OpenAI Service - does _not_ train on your data
Opt-out link: https://help.openai.com/en/articles/7039943-data-usage-for-c...
doesn't say they're not selling it to someone else who might...
And if you opt out, they delete the chats from your history so you can't reference them later for your own use. Slick!
Disabling "Chat History / Training" in ChatGpT Settings will disable chat history.
Opting out through the linked form in that FAQ will allow you to keep chat history.
aka the nerfed version. high speed means the weights were relaxed leading to faster output but worse reasoning and memory.
Worst case just add a `sleep()` to the non-enterprise version.
> The party told you to reject the evidence of your eyes and ears. It was their final, most essential command
Another bug that I‘m having for weeks now is that pressing Stop Responding will indeed stop the stream but it will also cause a block on any new messages for about a minute. This one used to work just fine but started to fail a few weeks ago.
me: “I would like to close my account”
chatgpt: “I’m sorry, did you mean open or close an account”
me: “ close account”
Chatgpt: “okay what type of account would you like to open”
Me: “fuck you”
Chatgpt: “I’m sorry I do not recognize that account type. Please repeat”
me: “I would like to close my account”
Chatgpt: “okay i can close out your account,please verify identity”
me: <identity phrase>
Chatgpt: I’m sorry that’s incorrect. Your account has been locked indefinitely until it can be reviewed manually. Please wait for 5-10 business days
I'm sorry but the financial analyst using chatGPT to write their excel formulas for them, and explicitly calling out that it is generating a formula that the analyst can't figure out on their own ("tricky") is an incredibly alarming thing to call out as a use case for chatGPT. I can't think of a lower reward, higher risk task to throw chatGPT at than financial analysis / reporting. Subtle differences in how things are reported in the financial world can really matter
This sound like a huge waste of money for something that should just be completely on-device or self-hosted if you don't trust cloud-based AI models like ChatGPT Enterprise and want it all private and low cost.
But either way, Meta seems to be already at the finish line in this race and there is more to AI than the LLM hype.
From the very beginning everyone knew data privacy & security would be one of the main issues for corporations.
> something that should just be completely on-device or self-hosted if you don't trust cloud-based AI models like ChatGPT Enterprise and want it all private and low cost
I can imagine this argument being made repeatedly over the past several decades whenever anyone makes a decision to use any paid cloud service. There is a value in self-hosting FOSS services and managing it in house and there is a value in letting someone else manage it for you. Ultimately it depends on the business use case and how much effort / risk you are willing to handle.
We're going after some of these use cases:
Want a daily email with all the latest news from your custom data source (or Google) for a topic? How about parsing custom data and scores from your datasets using prompts with all the complicated bits handled for you, then downloading as a simple CSV? Or even simply bulk generating content, such as generating Press Releases from your documents?
All easy with FlowChai :)
I think there's room for many different options in this space, whether that be Personal, Small Business or Enterprise.
Here's an example of automatically scraped arXiv papers on GPT4, turned into a report (with sources) generated by GPT4: https://flowch.ai/shared/6107d220-4e19-4bdc-a566-e84e8a60565...
I quick scrolled through your webpage and had no idea what it was. Extremely text heavy, and generic images that didn't communicate anything. I wanted to know what the product LOOKED like, especially as you're describing the difference between it and the chat interface of OpenAI.
I think you updated your comment (or I missed it) with the link to a "report" - it looked just like the output of one of the text bubbles except it had some (source) links (which I think Bing does as well)? It didn't seem all that different to me.
In terms of what makes the report different from Bing: this could be any source of data: scraped from the web, search, API upload, file upload etc, so there's a lot more power there. Also, it's not just one off reports, there's automation there which would allow for example a weekly report on the latest papers on GPT4 (or whatever you're interested in).
You didn't offer me any way to delete my account and remove the email address I saved in your system. I hope you don't start sending me emails, after not giving me an ability to delete the account