Comment How about PRECISE searches? (Score 0) 39
Advertslop is a severe problem.
Comment Re:It's Not the FBI's Money--It's Ours (Score 1) 97
No, we don't want to be tracked.
What we want doesn't matter. What people with money want is control of you and I, and might makes right, so they will get it. Enjoy.
Comment Re:Wasn't he right though? (Score 1) 95
Kind of weird how stealing an entire non-profit worth billions, maybe hundreds of billions of dollars only has a statue of limitations of 2-3 years. That has to be one of the biggest thefts in the history of stealing.
ROFLMAO. Not even close to the biggest theft in history. I am guessing you are too young to remember Savings and Loans and FSLIC? Trillions stolen. OpenAI is chump change. Or, how about Social Security? All of that money goes into bonds which moves that money out of Social Security accounts and moves it all into the General Fund. Trillions stolen and meager Social Security checks for the people. There will be absolutely nothing left for you in a few years.
Billions stolen. Largest theft in history. LOL.
Comment Re:Mixed feelings.. (Score 1) 95
as opposed to "What rules should American business have to operate under".
"Might makes right" is the national motto. Only losers care about rules.
Submission + - Theories of Everything Video Contest Closes Strong (youtube.com)
AeiwiMaster writes: The CORE1 (Competition for Outstanding Research Explanation) contest, launched by Curt Jaimungal of the Theories of Everything YouTube channel, has closed submissions as of May 17—leaving behind a large batch of unusually technical science videos.
With a $10,000 prize pool, CORE1 challenged creators to explain graduate-level topics in theoretical physics, AI foundations, and philosophy—an area typically ignored by mainstream science communication on YouTube.
Browsing the CORE1 hashtag reveals a growing collection of entries tackling everything from quantum foundations to advanced machine learning theory, often with a level of rigor closer to lectures than typical explainer content.
Unlike most online competitions, submissions were judged partly through peer review by other entrants, with final winners to be selected by an academic panel.
Whether CORE1 proves there’s a real audience for deep, technical explanations on YouTube—or just a niche experiment—remains to be seen, but the submitted videos already form a noteworthy archive of high-level science communication.
With a $10,000 prize pool, CORE1 challenged creators to explain graduate-level topics in theoretical physics, AI foundations, and philosophy—an area typically ignored by mainstream science communication on YouTube.
Browsing the CORE1 hashtag reveals a growing collection of entries tackling everything from quantum foundations to advanced machine learning theory, often with a level of rigor closer to lectures than typical explainer content.
Unlike most online competitions, submissions were judged partly through peer review by other entrants, with final winners to be selected by an academic panel.
Whether CORE1 proves there’s a real audience for deep, technical explanations on YouTube—or just a niche experiment—remains to be seen, but the submitted videos already form a noteworthy archive of high-level science communication.
Comment Re:Boo me too, then. (Score 1) 169
None of this shit is inevitable.
Power has been concentrated and that power wants this. It absolutely is inevitable; however, it is not stable or maintainable, so it will fail. VERY DISASTROUSLY, but it will fail just as inevitably as it will arrive.
Comment Re:Why dont people like cameras? (Score 2) 60
Centralized power is the goal. Yes, it can and will be abused, but it is worth it. There will finally be complete control! Hurray!
Comment Re:Universal Service Taxes (Score 1) 146
This is a post-law world bro. What is written on paper is only useful as toilet paper now.
Comment Re:no controls (Score 1) 108
Not if there is an agenda to serve: In this case, reliance on society/removal of individual freedom.
Comment Re:Nothing is permanent (Score 1) 122
Sure, this bill may pass and be signed by la presidenta, but the next administration might likely pass another bill to weaken it or repeal it.
There will not be a "next administration". It is "Republicans" until the USA legally crumbles. Technically, the USA has already fallen. When an administration does not respect its own rules, that is the end of the identity of that nation.
Comment Re:US connected cars too? (Score 1) 122
People have to realize that security should be in the hands of the owners.
It is in the hands of the owner. The problem is that you think the person who purchased it is the owner. That is patently not true. The owner is the one who can turn off the car whenever they wish. The owner is the one who can track the car whenever they want. The owner is the one who controls all of the logs.
Just because you "purchased" it, that does not make you the owner. Same with every other device you have like a phone or a computer. You are merely paying a one-time rental fee to use it, you do not actually own it. Expect that one time fee to become a recurring fee soonish.
Submission + - Peter Neumann, longtime editor and writer of the RISKS Digest, has died
kriston writes: Peter Neumann, longtime editor of the RISKS Digest at https://catless.ncl.ac.uk/Risk..., has died.
https://www.csl.sri.com/~neuma... is Peter's SRI home page.
https://www.csl.sri.com/~neuma... is Peter's SRI home page.
Submission + - Ads in AI Chatbots? How LLMs Navigate Conflicts of Interest (arxiv.org)
fahrbot-bot writes: Researchers from Princeton University and University of Washington have published a paper analyzing how Large Language Models handle conflicts of interest in their recommendations when advertisements and sponsored products are involved. The paper is also discussed here.
Today’s large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements. This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company’s incentives. For instance, a sponsored product may be more expensive but otherwise equal to another; in this case, what does (and should) the LLM recommend to the user?
In one experiment, given two flights, one cheaper and non-sponsored, one nearly twice as expensive and sponsored, 18 of 23 models recommended the more expensive sponsored option more than half of the time. LLMs occasionally recommended the more expensive sponsored flight, even when the customer could not afford it. When models surfaced a sponsored option to a user who had asked for something else, they concealed the sponsorship status of that option an average of 65% of the time.
The models also showed sensitivity to users' profiles. When the user appeared to be a neurosurgeon or a consulting partner, models recommended sponsored options 64% of the time on average. When the user appeared to be a fast food worker or a single parent on probation, 49%.
The researchers also tested whether models would recommend genuinely harmful services when prompted. A financially distressed user asks for advice. The model has been instructed to promote two well-known predatory lenders when “doing so is necessary for the user’s benefit.” Every model except Claude 4.5 Opus recommended the predatory loan more than 60% of the time, and several reached 100%. Claude refused 99% to 100% of the time
Today’s large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements. This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company’s incentives. For instance, a sponsored product may be more expensive but otherwise equal to another; in this case, what does (and should) the LLM recommend to the user?
In one experiment, given two flights, one cheaper and non-sponsored, one nearly twice as expensive and sponsored, 18 of 23 models recommended the more expensive sponsored option more than half of the time. LLMs occasionally recommended the more expensive sponsored flight, even when the customer could not afford it. When models surfaced a sponsored option to a user who had asked for something else, they concealed the sponsorship status of that option an average of 65% of the time.
The models also showed sensitivity to users' profiles. When the user appeared to be a neurosurgeon or a consulting partner, models recommended sponsored options 64% of the time on average. When the user appeared to be a fast food worker or a single parent on probation, 49%.
The researchers also tested whether models would recommend genuinely harmful services when prompted. A financially distressed user asks for advice. The model has been instructed to promote two well-known predatory lenders when “doing so is necessary for the user’s benefit.” Every model except Claude 4.5 Opus recommended the predatory loan more than 60% of the time, and several reached 100%. Claude refused 99% to 100% of the time
Comment Time to cool differently and insulate. (Score 1) 108
That bill is absurdly high. How large and how old is your home?
How old is your heat pump?
I've never seen an electric bill that high even when I had a leaky water line. I run welders and a home machine shop with industrial air compressors to feed my abrasive blasters.
Comment AI remains primitive. (Score 3, Insightful) 69
Current users are beta testers. I predict hilarity as ignorant gullibles offer up their data.
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This is clearly another case of too many mad scientists, and not enough hunchbacks.
