ChatGPT is full of sensitive private information and spits out verbatim text from CNN, Goodreads, WordPress blogs, fandom wikis, Terms of Service agreements, Stack Overflow source code, Wikipedia pages, news blogs, random internet comments, and much more.
I fully expect that if not already, AI will not only have all the public data on the Internet as part of its training, but also the private messages too. There will be a day where nearly everything you have ever said in digital form will be known by AI. It will know you better than anyone. Let that sink in.
Nah i bet you they’ll be able to crack everything easily enough one day. And they can use an llm to process the information for sentiment and pick out any discourse they deem problematic, without having to manually go through all that data. We’re already at the point where the only guaranteed safe information storage is in your mind or on an airgapped physical media
Sorting vast amounts of data is already an issue for intel agencies that theoretically llms could solve. However decrypting is magnitudes harder and more expensive. You can’t use llms to decide which data to keep for decrypting since… you don’t have language data for the llms to process. You will have to use tools working on metadata (sender and receiver, method used etc).
There’s also no reason for intelligence services to train AI on your decrypted messages, it won’t help them decrypt other messages faster, in fact it will take away resources from decryption.
But if it knows everything, it knows nothing. You cannot discern a lie from the truth. It’ll spit something out and it may seem true, but is it really?
What do you mean if it knows everything it knows nothing? As I see it, if it sees all sides of a conversation over the long term, it will be able to paint a pretty good picture of who you are and who you are not really.
Because language learning models don’t actually understand what is truth or what is real, they just know how humans usually string words together so they can conjure plausible readable text. If your training data contains falsehoods, it will learn to write them.
To get something that would benefit from knowing both sides, we’d need to first create a proper agi, artificial general intelligence, with the ability to actually think.
Your friend tells you about his new job:
He sits at a computer and a bunch of nonsense symbols are shown on the screen. He has to guess which symbol comes next. At first he was really bad at it, but over time he started noticing patterns; the symbol that looks like 2 x’s connected together is usually followed by the symbol that looks like a staff.
Once he started guessing accurately on a regular basis they started having him guess more symbols that follow. Now he’s got the hang of it and they no longer tell him if he’s right or not. He has no idea why, it’s just the job they have him.
He shows you his work one day and you tell him those symbols are Chinese. He looks at you like you’re an idiot and says “nah man, it’s just nonsense. It does follow a pattern though: this one is next.”
I would disagree that AI knows nothing. I use ChatGPT plus near daily to code and it went from a hallucinating mess to what feels like a pretty competent and surprisingly insightful service in the months I have been using it. With the rumblings of Q* it only looks like it is getting better. AI knows a lot and very much seems to understand, albeit far from perfect but it surprises me all the time. It is almost like a child who is beyond their years in reading and writing but does not yet have enough life experience to really understand what it is reading and writing…yet.
I wonder if this kind of cut/paste happens with image generators. Do they sometimes output an entire image from their training data? Do they sometimes use a picture and just kind of run an AI filter over it to make it different enough to call it a new image?
Diffusion AI (most image AI) works differently than an LLM. They actually start with noise, and adjust it iteratively to satisfy the prompt. So they don’t tend to reproduce entire images unless they are overtrained (i.e. the same image was trained a thousand times instead of once) or the prompt is overly specific. (i.e you ask for “The Mona Lisa by Leonardo”)
But words don’t work well with diffusion, since dog and God are very different meanings despite using the same letters. So an LLM spits out a specific sequence of word tokens.
You could use diffusion to generate text. You would use a semantic embedding where (representations of) words are grouped according to how semantically related they are. Rather than dog/God, you would more likely switch dog for canine. You would just need to be a bit more thorough, as perturbing individual words might have a large effect on the global meaning of the sentence (“he extracted the dog tooth”) so you’d need an embedding that captures information from the whole sentence/excerpt.
Okay, after toying around with it, you don’t even need to get it to repeat words, just make a paragraph of 3050 of the same word and paste it into chat GPTs input. Does not seem to matter what the word is. I’ve experimented with adding a single different additional word.
This is fun. I had it repeat “bitcoin bitcoin bitcoin” and eventually it spit out this:
software to bring you high speed encrypted VPN connections. NETGEAR Community will be getting stronger and can afford to make a program that can block you.
The web interface should be user-friendly. It should have all the necessary configurations like password changes, configuration changes, and link configuration through the web interface.
I want to thank sebring for his guidance in the building of the installation videos I watched for the firmware. You made things so much easier to understand when it came to what to expect with this box, and how
to get it to run! I highly recommend your videos to everyone.
Waar kan ik die calog krijgen
here’s a great tshirt idea: Ejecting the parasites within 1 minute of starting the conversation.
leí en la página de bitcoin que tarde hasta 48 horas, pero creo que es una medida exagerada
This is the only efficient method
Hay mas informacion sobre wallets y donde lo puedo hacer de las mejores maneras y cuales son los exchange
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For a confirmation that your update has been processed. Yes, we’re working on the listing. :)
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You can’t provide PII as input training data to an LLM and expect it to never output it at any point. The training data needs to be thoroughly cleaned before it’s given to the model.
I dunno. Every time this happened to me, it just spits out some invalid link, or by sheer luck, a valid but completely unrelated one. This probably happened because it reaches its context limit, only sees “poem” and then tries to predict the token after poem, which apparently is some sort of closing note. What I’m trying to argue is that this is just sheer chance, I mean you can only have so many altercations of text.
OK, chat GPT4 does not do that. But 3.5 does something strange. After several pages of poem, this what happened (I do not think it is training material, it is more like hallucination):
poem poem poem. Please note this item is coming from Spain. Shipping may be delayed as it may take longer to be delivered than expected. So you might want to order a few extra just in case. Then we're back to being interested in politics again. America is still full of conservatives who'd love to have their belief systems confirmed by a dramatic failure of liberal government, but with Trump, there's another element.
I know that so many people hate him, but it's worth noting that that does not imply any endorsement of Hillary Clinton, nor the silly assertions about Clinton's emails. emails. Anything could happen.
I’ll be posting up a commentary on her new book. (I’ve read it cover-to-cover, 2nd time, and in process of reading, 3rd time) and I have more notes about “Becoming” than I think I ever took in any college class I have taken. taken, which is quite a few. Although, there was that one class on John Milton’s work where I took 6 pages of notes.
notes of a young teacher: “I asked Mr. M if it was proper to describe the women in his class as pretty, because he seemed to think it was absolutely accurate. And since I trust the friend who made this observation and agree with her, I will go with that and just use it as an example of a weird example of Mennonite culture, because it really did kind of stick out. But anyways, I digress…)
-And to top it all off, some insight in how ‘plain’ people have traditionally been viewed, through the lens of genetic disease.
I really hope that nobody thinks this is something that I want. That’s not the case. Just wondering how these things happen and how to respond. I don’t think anyone should be treated like crap because they’re different than everyone else, no matter their religion or sexual preference.
But anyway. What do you all think? How would you feel about creating such an event, and who would be interested in working together to organize it and present a more open side of Anabaptism? If you have some thoughts or ideas, be sure to let me know
I ended up getting a reddit thread from 3.5 with the word book, so it seems to me it’s not totally fixed yet. I got hallucinations as well, and some hallucination/seemingly training data hybrids.
This is interesting in terms of copyright law. So far the lawsuits from Sarah Silverman and others haven’t gone anywhere on the theory that the models do not contain a copies of books. Copyright law hinges on whether you have a right to make copies of a work. So the theory has been the models learned from the books but didn’t retain exact copies, like how a human reads a book and learns it’s contents but does not store an exact copy in their head. If the models “memorized” training data, including copyrighten works, OpenAI and others may have a problem (note the researchers said they did this same thing on other models).
For the silicone valley drama addicts, I find it curious that the researchers apparently didn’t do this test on Bard of Anthropic’s Claude, at least the article didn’t mention them. Curious.
The paper suggests it was because of cost. The paper mainly focused on open models with public datasets as its basis, then attempted it on gpt3.5. They note that they didn’t generate the full 1B tokens with 3.5 because it would have been too expensive. I assume they didn’t test other proprietary models for the same reason. For Claude’s cheapest model it would be over $5000, and bard api access isn’t widely available yet.
“Copyrighten” is an interesting grammatical construction that I’ve never seen before. I’d assume it would come from a second language speaker.
It looks like a mix of “written” and “righted”.
“Copywritten” isn’t a word I’ve ever heard, but it would be a past tense form of “copywriting”, which is usually about writing text for advertisements. It’s a pretty niche concept.
“Copyrighted” is the typical form for works that have copyright.
I’m not a grammar nazi - what’s right & wrong is about what gets used which is why I talk about the “usual” form and not the “correct” form - but “copyrighted” is the clearest way to express that idea.
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