1,014 karma · joined February 16, 2014
President & Managing Member of Plyint, LLC (https://plyint.com)
A few examples:
- The Printing Press
- The Steam Engine
- Factories
- The Internal Combustion Engine
- The Internet
- "Smart" Phones
- Social Networks
- Bitcoin (the orange site loves this one)
It is entirely possible to manage funds in crypto for growth and move some amount into more liquid USD denominated assets or MMFs when you need liquidity.
No, it's not the problem with GDPR. As explained earlier it has to do with jurisdictional overreach.
> It doesn't matter. It's irrelevant to the general enforcement issue. Most DPAs seem to be failing to enforce even the simplest of cases. Let's chat about the edge cases and jurisdiction when the clear cut cases are being taken care of reliably.
Edge cases and jurisdiction are at the heart of this issue and exactly why it is a bad law. This is exactly the baggage that bad laws create!
How long have these laws been out and we are still dealing with these issues. They seem to have gotten worse, not better.
> How does it solve itself?
People build services that don't track others and people pay for those services. It's pretty simple.
> Due to website operators doing illegal things.
If it was so illegal it would be stopped, but apparently businesses are indeed complying with the law.
> Why would people care about something they don't know about?
It's well known that cookies track you across sites and some people choose not to use those sites. The sites are required to disclose this information, so users are definitely aware.
Investigating murders is enforceable. If law enforcement isn't doing their job then that is a different problem. By virtue of being on the Internet, tracking cookies span many legal jurisdictions (even ones outside of the EU that never agreed to GDPR) and therefore run into all sorts of different legal obstacles. Apples and oranges and all that.
> This is just a libertarian fairy-tale that is designed to sound sensible and rational while being malicious in practice. It exploits information asymmetry, human ignorance, network effects, and our general inability to accurately assess long-term consequences, in order to funnel profits into the hands of the most unscrupulous businesses.
No, it allows people to be adults and vote with their feet. We do this all the time in many other areas and it works. (Exactly what the free market is based on) This is not to say that there shouldn't be any privacy and anti-spam laws, but when it comes to allowing marketing/advertising the trade-off has been well understood for some time. We are all funneling a lot of profits into companies that provide software to serve up the cookie banner warnings now and the advertisers still end up getting lots of people's data. A poorly designed law is a bad law. Legally requiring consent upfront and the ramifications of that decision should have been thought through much more thoroughly.
If people care about privacy, then over time they will migrate to companies and services that respect their privacy. Government laws are broad based policies that always lack nuance. This is why it is better to let markets drive better outcomes organically.
Now, there does appear to be some shenanigans going on with circular financing involving MSFT, NVIDIA, and SMCI (https://x.com/DarioCpx/status/1917757093811216627), but the usefulness of all the modern LLMs is undeniable. Given the state of the global economy and the above financial engineering issues I would not be surprised that at some point there isn't a contraction and the AI hype settles down a bit. With that said, LLMs could be made illegal and people would still continue running open source models indefinitely and organizations will build proprietary models in secret, b/c LLMs are that good.
Since we are throwing out predictions, I'll throw one out. Demand for LLMs to be more accurate will bring methods like formal verification to the forefront and I predict eventually model/agents will start to be able to formalize solved problems into proofs using formal verification techniques to guarantee correctness. At that point you will be able to trust the outputs for things the model "knows" (i.e. has proved) and use the probably correct answers the model spits out as we currently do today.
Probably something like the following flow:
1) Users enter prompts
2) Model answers questions and feeds those conversations to another model/program
3) Offline this other model uses formal verification techniques to try and reduce the answers to a formal proof.
4) The formal proofs are fed back into the first model's memory and then it uses those answers going forward.
5) Future questions that can be mapped to these formalized proofs can now be answered with almost no cost and are guaranteed to be correct.
Not sure if there is really anything attempting to implement essentially Twitter with this model or not? I would be interested though if someone has run across or is working on a system like that.
In the cookingforengineers.com (COE) format, the order is to do each step in the first column and then move right to the next column and do those steps, etc.
Using the mushroom soup recipe as an example:
(1) Melt the butter.
(2) Wash and dice the onions, celery, and leeks.
(3) Sweat the melted butter from step (1) and the diced onions, celery, and leeks from step (2) together for 6 minutes.
Here's an example. You'll need to scroll down to see the actual recipe format. https://www.cookingforengineers.com/recipe/194/Cream-of-Mush...