It is even worse than the article says. You can, though these dialogs, wind up having folder on your hard drive moved around, have your desktop or other folders which you do not intend to be, cloud-synced. Then you get home and all of a sudden two PCs you mean to keep separate have new apps installed, background changed, etc...
Obviously ~nobody has read this yet... But I did have a question based on the opening:
"If you wanted to create a tool that would enable the destruction of
institutions that prop up democratic life, you could not do better than artificial intelligence. Authoritarian leaders and technology oligarchs are deploing [sic] AI systems to hollow out public institutions with an astonishing alacrity"
So in the first two sentences we have hyperbole and typos? Hardly seems like high-quality academic output. It reads more like a blog post.
I haven't had much success yet with this. My ratings follow.
Reading and interpreting datasheets: A- (this has gotten a LOT better in the last year)
Give netlist to LLM and ask it to check for errors: C (hit or miss, but useful because catching ANY errors helps)
Give Image to LLM and ask it to check for errors: C (hit or miss)
Design of circuit from description: D- (hallucinates parts, suggests parts for wrong purpose. suggests obsolete parts. Cannot make diagrams. Not an F because its textual descriptions have gotten better. When describing what nodes connect to each other now its not always wrong. You will have to re-check EVERYTHING though, so its usefulness is doubtful)
This is not energy output (production, usage), it is that plus an adjustment for the in->out energy efficiency. It would production if all energy sources in the mix has the same factor.
Because fossil fuels have higher in/out losses this is number is larger than usage. This metric is generally used to track decarbonization.
Using the IEA number you can see the hydro+solar+wind production is about 9.5% of the total, not 18%.
ChatGPT or you favorite LLM can explain in greater detail, just send it the plot image and ask.
If, for ethical reasons, fewer people were willing to take these jobs, then either salaries would have to rise or the work would be done less effectively.
If salaries rise, the business becomes more expensive and harder to scale.
If effectiveness drops, the systems are less capable of extracting/using people’s data.
Either way, refusing these jobs imposes real friction on the surveillance model.
If you want a deontological answer:
You have a responsibility not to participate in unethical behavior, even if someone else would.
"Dollar General stores have failed more than 4,300 government price-accuracy inspections in 23 states since January 2022" Is this a lot or a little? There no context for national chain
Article Context-free raw #'s, no comparisons to traditional grocery stores AFAIK. Dad journalism.
You should not update on this article unless you have some outside knowledge of the industry.
I had AI look into it, it found a national report found that dollar stores had pricing errors at about twice (3.5%) the rate of traditional supermarkets (1.7%) but lower than convenience stores (4.9%).
Why is this worth doing? What wrong with the status quo? The author does not give any examples of Oracle threatening people for using the JavaScript (tm) name.
The authors largest bullet point is on author identification. What are the arguments against this and do people really feel they outweigh the benefits?
My view: I would think given how many code supply chain attacks we've see recently this would be be regarded as (at worst) a necessary evil. How much software used by large numbers of people does the open source community think will be done by anons?
Sidenote: The author implies SyncThing development was stopped due to author ID but the post linked does not say this and gives a completely different reason (forced updates)
Papers make specific conclusions based on specific data. The paper I linked specifically rebuts the conclusions of the paper. Gary makes vague statements that could be interpreted as being related.
It is scientific malpractice to write a post supposedly rebutting responses to a paper and not directly address the most salient one.
Many people drive places other (further) than work multiple times a year. "75 mile battery" wouldn't even be good enough for a one-way trip of this kind let alone there and back again.
The authors don't say anything like this that I can see. Their conclusion specifically identifies these as weaknesses of current frontier models.
Furthermore we have clearly seen increases in reasoning from previous frontier models to current frontier models.
If the authors could /did show that both previous-generation and current-generation frontier models hit a wall at similar complexity that would be something, AFAIK they do not.
People are drawing erroneous conclusions from this.
My read of this is that the paper demonstrates that given a particular model (and the problems examined with it) that giving more thought tokens does not help on problems above a certain complexity. It does not say anything about the capabilities of future, larger, models to handle more complex tasks. (NB: humans trend similarly)
My concern is that people are extrapolating from this to conclusions about LLM's generally, and this is not warranted
The only part about this i find even surprising is he abstract's conclusion (1): that 'thinking' can lead to worse outcomes for certain simple problem. (again though, maybe you can say humans are the same here. You can overthink things)
If not it sounds like you are describing a separate phenomenon.