None of the mainstream paid services ingest operating data into their training sets. You will find a lot of conspiracy theories claiming that companies are saying one thing but secretly stealing your data, of course.
Nothing is really preventing this though. AI companies have already proven they will ignore copyright and any other legal nuisance so they can train models.
It's not really a conspiracy when we have multiple examples of high profile companies doing exactly this. And it keeps happening. Granted I'm unaware of cases of this occuring currently with professional AI services but it's basic security 101 that you should never let anything even have the remote opportunity to ingest data unless you don't care about the data.
“How can I control whether my data is used for model training?
If you are logged into Copilot with a Microsoft Account or other third-party authentication, you can control whether your conversations are used for training the generative AI models used in Copilot. Opting out will exclude your past, present, and future conversations from being used for training these AI models, unless you choose to opt back in. If you opt out, that change will be reflected throughout our systems within 30 days.” https://support.microsoft.com/en-us/topic/privacy-faq-for-mi...
At this point suggesting it has never and will her happen is wildly optimistic.
While this isn't used specifically for LLM training, it can involve aggregating insights from customer behaviour.
This is objectively untrue? Giants swaths of enterprise software is based on establishing trust with approved vendors and systems.
Many of the top AI services use human feedback to continuously apply "reinforcement learning" after the initial deployment of a pre-trained model.
https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
What? That’s literally my point: Enterprise agreements aren’t training on the data of their enterprise customers like the parent commenter claimed.
Do you have any citations or sources for this at all?
Inference (what happens when you use an LLM as a customer) is separate from training.
Inference and training are separate processes. Using an LLM doesn’t train it. That’s not what RLHF means.
Merely using an LLM for inference does not train it on the prompts and data, as many incorrectly assume. There is a surprising lack of understanding of this separation even on technical forums like HN.
The enterprise user agreement is preventing this.
Suggesting that AI companies will uniquely ignore the law or contracts is conspiracy theory thinking.
The big companies - take Midjourney, or OpenAI, for example - take the feedback that is generated by users, and then apply it as part of the RLHF pass on the next model release, which happens every few months. That's why they have the terms in their TOS that allow them to do that.
“You can use an LLM to paraphrase the incoming requests and save that. Never save the verbatim request. If they ask for all the request data we have, we tell them the truth, we don’t have it. If they ask for paraphrased data, we’d have no way of correlating it to their requests.”
“And what would you say, is this a 3 or a 5 or…”
Everything obvious happens. Look closely at the PII management agreements. Btw OpenAI won’t even sign them because they’re not sure if paraphrasing “counts.” Google will.
"Meta Secretly Trained Its AI on a Notorious Piracy Database, Newly Unredacted Court Docs Reveal"
https://www.wired.com/story/new-documents-unredacted-meta-co...
They even admitted to using copyrighted material.
"‘Impossible’ to create AI tools like ChatGPT without copyrighted material, OpenAI says"
https://www.theguardian.com/technology/2024/jan/08/ai-tools-...
"We will train new models using data from Free, Pro, and Max accounts when this setting is on (including when you use Claude Code from these accounts)."
https://www.vice.com/en/article/meta-says-the-2400-adult-mov...
However, let's say I record human interactions with my app; for example when a user accepts or rejects an AI sythesised answer.
This data can be used by me, to influence the behaviour of an LLM via RAG or by altering application behaviour.
It's not going to change the weighting of the model, but it would influence its behaviour.