Regex can easily defeat [at] type obfuscation.
Email obfuscation rendered almost ineffective against ChatGPT
101–110 of 204 posts
Re: Email obfuscation rendered almost ineffective against ChatGPT
#102the obvious solution is to use slurs in your email address
Re: Email obfuscation rendered almost ineffective against ChatGPT
#103Earlier quoted context omitted.
the power cord has to have a data link (USB? or just networking over power line itself) through which the power outlet can tell the computer how much does the energy cost at any given time. this is a very welcome but very expensive addition to the infrastructure.
Why would you put it in the cord? The phone/computer can handle this fine, and already has all the bits.
my wall outlet supplies power from 3 different sources: grid, solar on the roof and/or powerwall, depending on the weather, grid status (which sucks where I live) and time of day. computer only knows time of day off the bat, everything else it has to learn in a complex way.
if I have a "cost" or, better, "status" information integrated into the power itself, smart appliances (like computer) can make decisions what they can or can not run. right now I can start training my models on my 4090 at night, we have an outage, and 4090 will happily drain the powerwall, so I'll not have an A/C in the morning. models can wait, they're stupid anyway, or at least I like the A/C better.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#104Earlier quoted context omitted.
You do realize how possible it is to fine tune a task like this (along with a hundred others in a similar vein) on a tiny model you can scale on your own hardware? I've run hundreds of millions (150m so far in a couple of weeks of non-continuous running as I tweaked things) of tokens through my 2x 3090 with a 13b llama2 model I fine tuned on tasks like: summary, knowledge graph generation, writing using the knowledge…
How does one efficiently learn how to do such things, and what kinds of problems such approaches are fruitful for? I find there to be a giant gap in learning about this stuff between material that boils down to "use magic words and system prompts to improve results from one of the big models" and "how do LLMs work from first principles". I still haven't found a great resource that covers this middle ground, which see…
So I described my approach to how I fine tune a specific task below to another user, but I'll copy it here:
> Design your tasks to be repeatable and small steps, call the OpenAI API and log all requests/responses. > Filter out any bad responses and take a representative sample of the data you have collected from OpenAI,and train a Mistral or Llama2 model with the request/response pairs.
> Measure the quality of your model vs OpenAI for the same inputs, and then swap out the model in your workflow once happy with the results.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#105Earlier quoted context omitted.
The grid, of course, is less sure about this compromise.
The grid can be negotiated with if we put in the infrastructure.
That would cost money and lower the profits of the people that own/control the grid. I sometimes wonder how much money these robber barons spend on lobbying and other PR campaigns to convince that climate change isn't a problem and that the grid is just fine. It's one of those unanswerable questions I'm sure, but how much progress could be made by redirecting that amount of money to actually improving the grid itself?
Re: Email obfuscation rendered almost ineffective against ChatGPT
#106No large-scale email scraper has the budget necessary to run the content it scrapes through a LLM. So as far as real-world goes, nothing changes: the .2 cents it would cost to run ChatGPT on a page to extract potentially obfuscated emails would cost magnitudes more than it could ever bring in revenues. Regarding the examples provided, there is nothing there that a simple regex couldn't achieve, so I don't really see…
You do realize how possible it is to fine tune a task like this (along with a hundred others in a similar vein) on a tiny model you can scale on your own hardware? I've run hundreds of millions (150m so far in a couple of weeks of non-continuous running as I tweaked things) of tokens through my 2x 3090 with a 13b llama2 model I fine tuned on tasks like: summary, knowledge graph generation, writing using the knowledge…
It's even easier than that. There's no need to even fine tune an LLMs to do it. Here's a screenshot[1] of a 4 bit quantised version of an off the shelf open LLM (WizardLM 13B v1.2) doing it on my Mac.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#107most of those are also ineffective against anybody who knows what a regular expression is.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#108My brother used to work for a web scraping company years before chatGPT. He informs me that all the obfuscation techniques people used were pointless and the scraper got around all of them. If you think about it this makes sense: All you need is a list of regexes.
He said the one thing that still works is by writing your email out to an image file. OCR exists, but not en masse in scrapers like that.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#109No large-scale email scraper has the budget necessary to run the content it scrapes through a LLM. So as far as real-world goes, nothing changes: the .2 cents it would cost to run ChatGPT on a page to extract potentially obfuscated emails would cost magnitudes more than it could ever bring in revenues. Regarding the examples provided, there is nothing there that a simple regex couldn't achieve, so I don't really see…
I get very very little "obvious" email SPAM these days. I get a great deal of email from conference badge scans, retail that I've done business with, and a great number of other sources that I often/mostly don't care about but I'm not sure how you would algorithmically prevent outside of some labor-intensive and error-prone whitelist.
I'm sure some people would prefer to basically block email by default but that's not most of us.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#110Earlier quoted context omitted.
How does one efficiently learn how to do such things, and what kinds of problems such approaches are fruitful for? I find there to be a giant gap in learning about this stuff between material that boils down to "use magic words and system prompts to improve results from one of the big models" and "how do LLMs work from first principles". I still haven't found a great resource that covers this middle ground, which see…
So I described my approach to how I fine tune a specific task below to another user, but I'll copy it here: > Design your tasks to be repeatable and small steps, call the OpenAI API and log all requests/responses. > Filter out any bad responses and take a representative sample of the data you have collected from OpenAI,and train a Mistral or Llama2 model with the request/response pairs. > Measure the quality of your…
If you do this, be careful how/if you publish your weights trained on OpenAI output as if they look into how it was generated and it becomes clear you broke the ToS, they'll most likely ban you from the platform.