Most people here don’t understand what this is saying.
We’ve had “pure” human generated data, verifiably so since LLMs and ImageGen didn’t exist. Any bot generated data was easily filterable due to lack of sophistication.
ChatGPT and SD3 enter the chat, generate nearly indistinguishable data from humans, but with a few errors here and there. These errors while few, are spectacular and make no sense to the training data.
2 years later, the internet is saturated with generated content. The old datasets are like gold now, since none of the new data is verifiably human.
This matters when you’ve played with local machine learning and understand how these machines “think”. If you feed an AI generated set to an AI as training data, it learns the mistakes as well as the data. Every generation it’s like mutations form until eventually it just produces garbage.
Training models on generated sets slowly by surely fail without a human touch. Scale this concept to the net fractionally. When 50% of your dataset is machine generated, 50% of your new model trained on it will begin to deteriorate. Do this long enough and that 50% becomes 60 to 70 and beyond.
Human creativity and thought have yet to be replicated. These models have no human ability to be discerning or sleep to recover errors. They simply learn imperfectly and generate new less perfect data in a digestible form.
Collapse faster, please. Sick of ai bullshit clogging up my searches.
My team has been calling models that use ai generated data “Habsberg models”
I feel there is a good joke here, but I miss the knowledge to understand it. Care to enlighten me?
Maybe we need to label AI-generated content to, you know, avoid confusion.
Oh goody. I’ve been wanting to use this since my slashdot days… today is my first chance!
Your post advocates a [x] technical [ ] legislative [ ] market-based [ ] vigilante approach to fighting (ML-generated) spam. Your idea will not work. Here is why it won't work. [One or more of the following may apply to your particular idea, and it may have other flaws which used to vary from state to state before a bad federal law was passed.] [ ] Spammers can easily use it to harvest email addresses [ ] Mailing lists and other legitimate email uses would be affected [ ] No one will be able to find the guy or collect the money [ ] It is defenseless against brute force attacks [ ] It will stop spam for two weeks and then we'll be stuck with it [ ] Users of email will not put up with it [x] Microsoft will not put up with it [ ] The police will not put up with it [x] Requires too much cooperation from spammers [x] Requires immediate total cooperation from everybody at once [ ] Many email users cannot afford to lose business or alienate potential employers [ ] Spammers don't care about invalid addresses in their lists [ ] Anyone could anonymously destroy anyone else's career or business Specifically, your plan fails to account for [ ] Laws expressly prohibiting it [x] Lack of centrally controlling authority for email^W ML algorithms [ ] Open relays in foreign countries [ ] Ease of searching tiny alphanumeric address space of all email addresses [x] Asshats [ ] Jurisdictional problems [ ] Unpopularity of weird new taxes [ ] Public reluctance to accept weird new forms of money [ ] Huge existing software investment in SMTP [ ] Susceptibility of protocols other than SMTP to attack [ ] Willingness of users to install OS patches received by email [ ] Armies of worm riddled broadband-connected Windows boxes [x] Eternal arms race involved in all filtering approaches [x] Extreme profitability of spam [ ] Joe jobs and/or identity theft [ ] Technically illiterate politicians [ ] Extreme stupidity on the part of people who do business with spammers [x] Dishonesty on the part of spammers themselves [ ] Bandwidth costs that are unaffected by client filtering [x] Outlook and the following philosophical objections may also apply: [x] Ideas similar to yours are easy to come up with, yet none have ever been shown practical [ ] Any scheme based on opt-out is unacceptable [ ] SMTP headers should not be the subject of legislation [ ] Blacklists suck [ ] Whitelists suck [ ] We should be able to talk about Viagra without being censored [ ] Countermeasures should not involve wire fraud or credit card fraud [ ] Countermeasures should not involve sabotage of public networks [ ] Countermeasures must work if phased in gradually [ ] Sending email should be free [x] Why should we have to trust you and your servers? [ ] Incompatiblity with open source or open source licenses [x] Feel-good measures do nothing to solve the problem [ ] Temporary/one-time email addresses are cumbersome [ ] I don't want the government reading my email [ ] Killing them that way is not slow and painful enough Furthermore, this is what I think about you: [x] Sorry dude, but I don't think it would work. [ ] This is a stupid idea, and you're a stupid person for suggesting it. [ ] Nice try, assh0le! I'm going to find out where you live and burn your house down!I traced this baby back to January 19th, 2004: https://craphound.com/spamsolutions.txt
Back when i was though concept art as a subject at college my teacher had a name for this.
“Incest” cause every generation of art that references other art becomes more and more strange looking and detached from reality.
If you thought Skyrim weapons look ridiculous you should have seen my classmates Skyrim inspired weapons.
Anecdotally speaking, I’ve been suspecting this was happening already with code related AI as I’ve been noticing a pretty steep decline in code quality of the code suggestions various AI tools have been providing.
Some of these tools, like GitHub’s AI product, are trained on their own code repositories. As more and more developers use AI to help generate code and especially as more novice level developers rely on AI to help learn new technologies, more of that AI generated code is getting added to the repos (in theory) that are used to train the AI. Not that all AI code is garbage, but there’s enough that is garbage in my experience, that I suspect it’s going to be a garbage in, garbage out affair sans human correction/oversight. Currently, as far as I can tell, these tools aren’t really using much in the way of good metrics to rate whether the code they are training on is quality or not, nor whether it actually even works or not.
More and more often I’m getting ungrounded output (the new term for hallucinations) when it comes to code, rather than the actual helpful and relevant stuff that had me so excited when I first started using these products. And I worry that it’s going to get worse. I hope not, of course, but it is a little concerning when the AI tools are more consistently providing useless / broken suggestions.
