To my understanding, the idea behind the heat death of the Universe is that nothing interesting happens anymore, not that there is absolutely nothing.
Sorry, are you talking about the heat death of the universe or the last five Marvel movies?
2. The heat death of the universe does not mean one gigantic black hole. I’m just a hobbyist but my understanding of the theory is that black holes will continue to form, but through Hawking radiation, they eventually radiate out all their energy until it is all dispersed, ultimately leading to uniformity across the entire universe, max entropy, where “work” can no longer take place.
(It is an interesting question then whether information is actually destroyed through Hawking radiation?)
Thank you for saying that :)
I have a couple of external SSDs and USB flash drives start to flash their busy lights after letting them idle for a couple of minutes. These "bursts of activity" generally is proportional to the I/O work they have done before the idle period.
One of my flash drives did that GC/zeroing thing when it's ejected. After ejection and drive disconnect, its busy light flashed for some time depending on the work needs to be done. If you didn't hammer it when you used it, it did nothing, but if you did tons of work, it worked up to a minute or so after being ejected.
If you're one of the lucky 10,000 that don't know the reference:
https://stackoverflow.com/questions/1732348/regex-match-open...
And if you're one of the lucky 10,000 that don't know the other reference:
Obviously there are potential hiccups. But a lot of good can be done with encryption and metadata.
The analogy here is: there are laws regarding confidentiality that probably were broken here.
While I agree that it is unacceptable to use customer data without consent (as suggested by OP's post), I disagree with the implicit assumption behind the comment that I responded to:
Namely the implicit assumption that human/biological intelligence/agents are somehow superior to artificial intelligence/agents in face of secrecy/confidentiality.
It boils down to the question whether it's possible or not to create algorithms that outperform humans in tasks involving secrecy and confidentiality.
While I can't think of any reason why that should not be possible in general, I agree that the current SOTA of generative LLMs is not sufficient for that.
Is throwing lots and lots of data and RLHF training on an LLM enough in order to make the probability of customer data leaks small enough to be acceptable?
I don't know. But I don't trust MBAs who salivate with dollar signs in their eyes to know either. And I fear that their lack of technical understanding will lead to bad decisions. And I fear those might lead to scandals that make Gemini's weird biases in image generation pale in comparison.
Yes, the user bears the cost when their confidential data is leaked and the company derives the economic benefit of mishandling it, which is why this keeps happening.
There was a young lady I had to reintroduce myself to every week or so. I think of her every so often.
I’m certain she doesn’t think of me.
Simply classify data into buckets for each value of each variable such that each unique combination of buckets for every variable contains at least N items for a suitably large number of N. It is then mathematically impossible to match an individual data point to any set of original data points less than N.
That's not what this data is and that's broadly not what LLMs are trained on.
So repeat after me...
and obviously partial information is very useful for exactly those with some other partial information.
this is why OSINT is very effective. it's possible to piece together enough constraints on these datasets that in the end the likelihood of a successful deanonymization is unexpectedly high.
What are the variables and what is the value of N when you're talking about "freeform text contracts containing PII"?
I know this is a bit of an absurd example, but I'm trying to understand the mechanics more than to provide a realistic counterexample.
But understand what you are doing: you are adding noise to individual's data to make them less identifiable within a specified set of data. The amount of noise you need to add depends on the size of the set they are within.
The OP is also right: For arbitrary customer data (within arbitrary sets of data) you can't guarantee anonymization.
There are also multi-party computation systems, homomorphic encryption and federated learning approaches that all provide different approaches for parts of the private machine learning problem.
It's hard.
https://en.wikipedia.org/wiki/K-anonymity
k-anonymity is an attempt to solve the problem "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful."
One serious problem with that approach is that no matter how well you k-anonymise your data set, someone else's dataset may deanonymise yours.
Nothing in there is anywhere near impossible unless the system doesn't have the data.
If reliability is zero, so is the risk.
There's no sure-fire way to automatically anonymize arbitrary customer data.
There's no sure-fire way to automatically anonymize arbitrary customer data.
There's no sure-fire way to automatically anonymize arbitrary customer data.
You can't anonymize it by hand. You can't anonymize it by machine. If you're taking blobs of customer data and dumping it into a machine, customer data can come out of the machine. Neither humans nor machines are error-free. If you believe DocuSign is going to do a perfect job, I've got a bridge to sell you.