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Nope. Just testifying, Mult.
Are you concerned you could be replaced on ST by a mechanical AI parrot?
Nope. Just testifying, Mult.
As a Shark Tank investor often says, what is to stop someone from creating a mechanical parrot that does the same thing only 100Xs more at a fraction of the cost?
Embarrassment?As a Shark Tank investor often says, what is to stop someone from creating a mechanical parrot that does the same thing only 100Xs more at a fraction of the cost?
AI's don't get embarrassed.
Good point.
The series linked below basically argues that the payoff for AI is so important geopolitically that energy should be deregulated, green energy transition postponed and environmental impacts disregarded.
The basic idea is that the growth in orders of magnitude (OOMs) of computation and algorithmic efficiency will compress the time it takes to do technical research by OOMs, and thus will confer decisive economic and military advantages to whoever does it best/first.
To his credit, the author does not dismiss material and political constraints, nor the problem of nonalignment, or AI gone rogue.
Introduction - SITUATIONAL AWARENESS: The Decade Ahead (situational-awareness.ai)
Last edited by Winehole23; 06-25-2024 at 05:16 PM.
Energy capacity/transmission and water are relatively inelastic constraints to the exponential growth of AI. The shifting of cost burden from utilities to customers is frankly insidious, but we're used to that in Texas.
linkBut as more and more server farms spring up, the state’s largest electric utility, Dominion Energy, has scrambled to keep pace. The industry’s peak energy usage in 2022 was almost 2.8 gigawatts, or about a fifth of the utility’s total statewide sales. That same year, Dominion told its customers in Loudoun County it could no longer guarantee it would deliver as much power as they needed, stalling the breakneck development.
Instead, data companies began to eye places like Iowa, Georgia, and nearby Prince William County — where residents like Ward warn that similar problems are on the horizon. Critics say the Digital Gateway proposal alone will require at least three gigawatts of electricity, or the equivalent of the power demand of 750,000 homes. “Where is that power coming from?” Ward asked the Board of Supervisors.
It’s a question that PJM Interconnection, the regional transmission organization that spans thirteen states and the District of Columbia, is also asking. The organization, which coordinates the movement of wholesale electricity in parts of the eastern United States, recently approved a set of $5.1 billion transmission projects, primarily to deliver more power to Virginia’s data centers.
The problem is that these costs will be distributed across the various states within the network, says David Lapp, Maryland People’s Counsel, an independent Maryland state position that advocates for Maryland’s residential utility consumers. Though the upgrades primarily benefit private companies in Virginia, they will result in rate hikes for ordinary Maryland customers, a move Lapp calls “fundamentally unfair.”
The added transmission capacity is more than what Maryland’s largest utility itself currently uses at peak times. “The scale, scope, and cost of the [Digital Gateway] projects are unprecedented,” Lapp wrote to the PJM Board of Managers before arguing to federal energy regulators that PJM had unfairly allocated costs to Maryland. The Federal Energy Regulatory Commission denied his request in May, leaving Maryland on the hook for $551 million.
This is a common scenario. In Indiana, for instance, utility regulators recently approved a new $800 million data center campus with Meta Platforms, Inc., which owns Facebook and other social media services. The secret negotiated rate for the facility’s power has been redacted from public filings, but what was included was that the infrastructure required to connect the facility to the grid will cost $82 million.
Duke Energy Indiana redacted details about the special electricity rates it provided to Meta Platforms for its new data center campus.
The Office of the Utility Consumer Counselor allowed the facility to shift those costs onto ratepayers, arguing it would bring capital investment to the area. Data centers, however, don’t create many local jobs. Nevertheless, the Indiana facility will be doubly subsidized, as it is also receiving a thirty-five-year sales tax exemption from the state.
Just as mortgage companies make money on interest, incentivizing them to sell more mortgages, utilities make money by spending on infrastructure. That’s because regulations allow these companies a return on their investments in upgrades like new transmission lines. Utilities, in other words, also profit from outsourcing data center costs to the public. In fact, Dominion’s most recent investor presentation proudly claimed “robust rate base growth,” and forecast a staggering 8,500 megawatt e in demand.
“It’s very counterintuitive,” Lapp says, that utilities “make money by spending other people’s money.”
The arrangement sends the wrong price signal to the industry, Lapp argues. If tech companies paid full freight for their energy infrastructure, they would be incentivized to find ways to use less power. Instead, many are going in the wrong direction: despite its goals to become carbon-free by 2030, Microsoft’s emissions jumped by 30 percent in 2023, thanks to its recent investments in AI.
Altogether, a new report by the Electric Power Research Ins ute found that AI could comprise roughly 9 percent of the country’s total energy demand by the end of the decade. Other estimates suggest global data center energy demand could double by 2026, while some utilities, like those in Arizona and Washington, may see as much as 10 percent load growth.
You can't say we wasn't warned, Winester, and duly so.
But commerce is a succulent beast when befriended, no matter what. No.matter.what.
Bad actors aren't a what if, they're main beneficiaries
https://x.com/emollick/status/1805731107096379430
And we was duly warned by those in the know who giggled thru the entire warning(s).
OK MT, do we listen to the AI experts or do we AI research internet on our own as Qtsa has not text-walled on this topic with a new conspiracy theory from Korea?
We do precisely what State sponsored CNN tells us to do after American Democracy has told them, Effy.OK MT, do we listen to the AI experts or do we AI research internet on our own as Qtsa has not text-walled on this topic with a new conspiracy theory from Korea?
It's like shaved pussy.
AI got his own statue. Knew it.
CommercePERIOD
AI financial crises | CEPRThese drivers of financial instability are well understood and have always been a concern, long before the advent of computers. As technology was increasingly adopted in the financial system, it brought efficiency and benefited the system, but also amplified existing channels of instability. We expect AI to do the same.
When identifying how this happens, it is useful to consider the societal risks arising from the use of AI (e.g. Weidinger et al. 2022, Bengio et al. 2023, Shevlane et al. 2023) and how these interact with financial stability. When doing so, we arrive at four channels in which the economy is vulnerable to AI:
- The misinformation channel emerges because the users of AI do not understand its limitations, but become increasingly dependent on it.
- The malicious use channel arises because the system is replete with highly resourced economic agents who want to maximise their profit and are not too concerned about the social consequences of their activities.
- The misalignment channel emerges from difficulties in ensuring that AI follows the objectives desired by its human operators.
- The oligopolistic market structure channel emanates from the business models of companies that design and run AI engines. These companies enjoy increasing returns to scale, which can prevent market entry and increase geneity and risk monoculture.
(They'll) get it straightened out if it kills us.
"On Bull ," by Harry Frankfurt might be the most influential philosophical monograph in the world right now.
In this paper, we argue against the view that when ChatGPT and the like produce false claims they are lying or even hallucinating, and in favour of the position that the activity they are engaged in is bull ting, in the Frankfurtian sense (Frankfurt, 2002, 2005). Because these programs cannot themselves be concerned with truth, and because they are designed to produce text that looks truth-apt without any actual concern for truth, it seems appropriate to call their outputs bull .ChatGPT is bull | Ethics and Information Technology (springer.com)The structure of the paper is as follows: in the first section, we outline how ChatGPT and similar LLMs operate. Next, we consider the view that when they make factual errors, they are lying or hallucinating: that is, deliberately uttering falsehoods, or blamelessly uttering them on the basis of misleading input information. We argue that neither of these ways of thinking are accurate, insofar as both lying and hallucinating require some concern with the truth of their statements, whereas LLMs are simply not designed to accurately represent the way the world is, but rather to give the impression that this is what they’re doing. This, we suggest, is very close to at least one way that Frankfurt talks about bull . We draw a distinction between two sorts of bull , which we call ‘hard’ and ‘soft’ bull , where the former requires an active attempt to deceive the reader or listener as to the nature of the enterprise, and the latter only requires a lack of concern for truth. We argue that at minimum, the outputs of LLMs like ChatGPT are soft bull : bull –that is, speech or text produced without concern for its truth–that is produced without any intent to mislead the audience about the utterer’s at ude towards truth. We also suggest, more controversially, that ChatGPT may indeed produce hard bull : if we view it as having intentions (for example, in virtue of how it is designed), then the fact that it is designed to give the impression of concern for truth qualifies it as attempting to mislead the audience about its aims, goals, or agenda. So, with the caveat that the particular kind of bull ChatGPT outputs is dependent on particular views of mind or meaning, we conclude that it is appropriate to talk about ChatGPT-generated text as bull , and flag up why it matters that – rather than thinking of its untrue claims as lies or hallucinations – we call bull on ChatGPT.
I said "Good morning, fart-face."
Damn, AI is a maga!!!!
Only pussies & assholes do AI.
Yeah those guys were interviewed on the Better Offline podcast, which spends all its time ting on AI and the rotting of big tech.
How the massive power draw of generative AI is overtaxing our grid (cnbc.com)“Everybody is worried about AI being energy intensive. We can solve that when we get off our ass and stop being such idiots about nuclear, right? That’s solvable. Water is the fundamental limiting factor to what is coming in terms of AI,” said Tom Ferguson, managing partner at Burnt Island Ventures.
Technology has a solution for that in the works but who knows if it will work
Breakthrough CRAM technology ditches von Neumann model, makes AI 1,000x more energy efficient
The global demand for AI computing has data centers consuming electricity like frat houses chug beer. But researchers from the University of Minnesota might have a wildly innovative solution to curb AI's growing thirst for power with a radical new device that promises vastly superior energy efficiency.
The researchers have designed a new "computational random-access memory" (CRAM) prototype chip that could reduce energy needs for AI applications by a mind-boggling 1,000 times or more compared to current methods. In one simulation, the CRAM tech showed an incredible 2,500x energy savings.
Traditional computing relies on the decades-old von Neumann architecture of separate processor and memory units, which requires constantly moving data back and forth in an energy-intensive process. The Minnesota team's CRAM completely upends that model by performing computations directly within the memory itself using spintronic devices called magnetic tunnel junctions (MTJs).
Rather than relying on electrical charges to store data, spintronic devices leverage the spin of electrons, offering a more efficient subs ute for traditional transistor-based chips.
"As an extremely energy-efficient digital-based in-memory computing substrate, CRAM is very flexible in that computation can be performed in any location in the memory array. Accordingly, we can reconfigure CRAM to best match the performance needs of a diverse set of AI algorithms," said Ulya Karpuzcu, a co-author on the paper published in Nature. He added that it is more energy-efficient than traditional building blocks for today's AI systems.
By eliminating those power-hungry data transfers between logic and memory, CRAM technologies like this prototype could be critical for making AI vastly more energy efficient at a time when its energy needs are exploding.
The International Energy Agency forecasted in March that global electricity consumption for AI training and applications could more than double from 460 terawatt-hours in 2022 to over 1,000 terawatt-hours by 2026 – nearly as much as all of Japan uses.
https://www.techspot.com/news/104005...del-makes.html
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