Wait, isn’t it the other way around? You should arrive in NY earlier than you left London, since NY is 5 hours behind London. So if you leave at 8:30 and arrive 1.5 hours later, it should only be 5AM when you arrive.
You might need a third breakfast before your elevenses in that case.
Jerboa is solid, but it’s not feature-rich. Not great for media browsing. It’s still my main client since I use Lemmy mostly for text, not images or videos.
Eternity and Voyager are worth looking at, too.
Interesting read, thanks! I’ll finish it later, but already this bit is quite interesting:
Without access to gender, the ML algorithm over-predicts women to default compared to their true default rate, while the rate for men is accurate. Adding gender to the ML algorithm corrects for this and the gap in prediction accuracy for men and women who default diminishes.
We find that the MTEs are biased, signif-icantly favoring White-associated names in 85.1% of casesand female-associated names in only 11.1% of case
If you’re planning to use LLMs for anything along these lines, you should filter out irrelevant details like names before any evaluation step. Honestly, humans should do the same, but it’s impractical. This is, ironically, something LLMs are very well suited for.
Of course, that doesn’t mean off-the-shelf tools are actually doing that, and there are other potential issues as well, such as biases around cities, schools, or any non-personal info on a resume that might correlate with race/gender/etc.
I think there’s great potential for LLMs to reduce bias compared to humans, but half-assed implementations are currently the norm, so be careful.
Being factually incorrect about literally everything you said changes nothing? Okay.
More importantly, humans are capable of abstract thought. Your whole argument is specious. If you find yourself lacking the context to understand these numbers, you can easily seek context. A good starting place would be the actual paper, which is linked in OP’s article. For the lazy: https://www.nature.com/articles/s41598-020-61146-4
It’s 14,000 to 75,000, not millions.
Microplastics are in the range of one micrometer to five millimeters, not nanometers.
They link to the full source paper: https://www.nature.com/articles/s41598-020-61146-4
That seems more like your problem than OP’s.
After all these years, I’m still a little confused about what Forbes is. It used to be a legitimate, even respected magazine. Now it’s a blog site full of self-important randos who escaped from their cages on LinkedIn.
There’s some sort of approval process, but it seems like its primary purpose is to inflate egos.
It was an SEO hellhole from the start, so this isn’t surprising.
Do Forbes next!
I think there are two problems that make this hard to answer:
Not all sentences that can be parsed grammatically can also be parsed logically.
Human-language sentences do not contain all the information needed to evaluate them.
It is impossible to fully separate context from human language in general. The sentence “it is cold” is perfectly valid, and logically coherent, but in order to evaluate it you’d need to draw external information from the context. What is “it”? Maybe we can assume “it” refers to the weather, as that is common usage, but that information does not come from the sentence itself. And since the context here is on the Internet, where there is no understanding of location, we can’t really evaluate it that way.
It’s hot somewhere, and it’s cold somewhere. Does that mean the statement “it is cold” is both true and false, or does that mean there is insufficient information to evaluate it in the first place? I think this is largely a matter of convention. I have no doubt that you could construct a coherent system that would classify such statements as being in a superposition of truth and falsehood. Whether that would be useful is another matter. You might also need a probabilistic model instead of a simple three-state evaluation of true/false/both. I mean, if we’re talking about human language, we’re talking about things that are at least a little subjective.
So I don’t think the question can be evaluated properly without defining a more restrictive category of “sentences”. It seems to me like the question uses “sentence” to mean “logical statements”, but without a clearer definition I don’t know how to approach that. Sentences are not the same as logical statements. If they were, we wouldn’t need programming languages :)
Apologies for the half-baked ideas. I think it would take a lifetime to fully bake this.
I claim ownership of the microorganisms in and on my body. I am not merely human; I am a glorious amalgamation of trillions of distinct beings, working in harmony to bring you shitposts!
Yeah, they were able (and thus legally required) to hand over the user’s recovery email address, which is what got them caught. You don’t need to enter a recovery email address, and you can of course choose to use an equally-secure service for recovery.
One big technical issue to note is that Proton doesn’t use end-to-end encryption for email headers, which includes recipients and subject lines, among other things. So that’s potentially exposed to law enforcement as well. I believe Tuta does encrypt headers.
I don’t think the free accounts cost them a whole lot. You only get 1GB of mail space, and the free versions have minimal features (e.g. you can only create one email filter). They make their money on paid accounts, which seems legit to me.
As a begrudging Comcast customer myself, allow me to explain. They are the least shitty option because the only alternatives in my area are 5G and Verizon DSL. Verizon DSL has a max download speed less than 1mbps.
So yeah, I use Comcast. And I hate it.
However, it is still comparatively easy for a determined individual to remove a watermark and make AI-generated text look as if it was written by a person.
And that’s assuming people are using a model specifically designed with watermarking in the first place. In practice, this will only affect the absolute dumbest adversaries. It won’t apply at all to open source or custom-built tools. Any additional step in a workflow is going to wash this right out either way.
My fear is that regulators will try to ban open models because the can’t possibly control them. That wouldn’t actually work, of course, but it might sound good enough for an election campaign, and I’m sure Microsoft and Google would dump a pile of cash on their doorstep for it.
There’s a way to say this that isn’t so gross: good working conditions are valuable. Quality of life is valuable. Work-life-balance is valuable. Mental and physical health is valuable. Not having raging shitbags in management is valuable.
The problem is that you can’t focus on secondary factors until the primary factor is taken care of. And the primary factor is that people need a living wage. Rent is expensive. Food is expensive. God help you if you need to pay for childcare.
If you’re already paying your employees a fair living wage, then yes, you should absolutely think about how you can improve working conditions.
As an example, if my company gave me the option to switch to a 4-day workweek for the same pay, or stay at a 5-day workweek for a 25% raise, I’m honestly not sure which one I’d prefer. But we all know that’s never going to happen; instead the choice would be to take a 20% pay cut or maintain the status quo. I wouldn’t take that deal because I’m not making enough money to live on 20% less.
simply logging out or using an alt account
It is increasingly difficult to use X without an account. Not sure what the signup process is like nowadays. IIRC it used to require phone number verification in the Twitter days, but perhaps Musk relaxed the requirements in order to better pad the usage stats with spambots?
I haven’t seen this movie in like 25 years, but I still read this in Marisa Tomei’s voice.