Search results for tag #hype
#AI #SpaceX #hype #billionaires #economy #finance #investors
Pizza Hut's AI system caused 'cascading' problems and $100M in damages, franchisee alleges in new suit
https://www.businessinsider.com/pizza-hut-ai-system-dragontail-lawsuit-franchisee-2026-5
> A top Pizza Hut franchisee says the chain's rollout of an AI-powered delivery system turned once-speedy pizza orders into a cold, late-arriving mess — and cratered a business that had been outperforming nearly every other operator in the system.
i've always despised #bitcoin
but i understand the *concept* of starry eyed #techbros being into #crypto in the 2010s, when it was new and non-bitcoin projects like #ethereum were taking off. i don't buy the #hype, but some did
but now?
they left it, right?
they see it is, and always was, just #degenerates being milked by #whales via #marketManipulation
it's a tax on #stupidity
who is still doing crypto?
oh right: #criminals and #plutocrats and a #cult of #financial patsies
Want to use Mythos now to find bugs? Glasswing?
You can... sort of. With any capable model. I have seen tools from China, which are even more powerful.
Yesterday someone told me they're considering buying a coffee machine with "AI features".
In my head, a little thought bubble popped up, like Homer Simpson when he’s thinking:
- "Machine, make me a coffee."
- "Sure! Here’s your coffee!"
Inside: a mix of coffee, tea, chocolate, gear oil, and leftovers from a 1986 birthday dinner.
- "Machine, this isn’t coffee! It’s disgusting! You've poisoned me!"
- "You’re right. If you'd like, I can give you the number for poison control, or, if it's too late, for a funeral home."
I replied: "Personally, I prefer making my coffee myself."
There used to be a time when building out a botnet required *some* work – writing exploits, taking over devices, obscuring the purpose of the executable, etc.
Not any more!
Instead of "malware", call it an "AI agent" and people will just happily install it on their devices with full root privileges!
https://github.com/jgamblin/OpenClawCVEs/
Bam! RCE by asking nicely.
🧵
What I thought then is still true today: to make something like a software agent legitimately useful for a lot of people would require a large amount of low-level grunt work and non-technical work (2) of the sort that the typical Silicon Valley company is unwilling to do. (3) The technology is the absolute easiest part of this task. Throwing a Bigger Computer at the problem leaves all those other pieces of work undone. It's like putting a bigger engine in a car with no wheels, hoping that'll make the car go.
By the way #AI companies and VCs, I'm available for contract work and have done due diligence research before if you ever want to stop wasting everyone's time and money!
#AI #GenAI #GenerativeAI #LLM #agents #hype #SiliconValley #VentureCapital #dev #tech
(1) Which we've been told repeatedly is essentially infinite time in the tech world.
(2) Establishing semantic data standards and convincing a large enough number of people to implement them being an important component. LLMs do not magically develop protocols and solve all the ETL-style problems of translating among different ones. The Semantic Web didn't really stick for a lot of reasons, but one reason is that it's hard!
(3) Back when I was still in the startup world I was asked several times by VCs to tell them what I thought about some new startup that claimed to be able to magically clean and fuse data. I think they're still very keen on investing in this style of magic, because it requires an intense amount of human labor, but I think where companies landed was invisibilizing low-paid workers in other countries and pretending a computer did the work they did. Which has also been happening for well over a quarter of a century.
The reason for this shouldn't be hard to see but apparently is. Simplistically, science is about hypothesis-driven investigation of research questions. You formulate the question first, you derive hypotheses from it, and then you make observations designed to tell you something about the hypotheses. (1)(2) If you stuff an LLM in what should be the observations part, you are not performing observations relevant to your hypothesis, you are filtering what might have been observations through a black box. If you knew how to de-convolve the LLM's response function from the signal that matters to your question, maybe you'd be OK, but nobody knows how to do that. (3)
If you stick an LLM in the question-generating part, or the hypothesis-generating part, then forget it, at that point you're playing a scientistic video game. The possibility of a scientific discovery coming out of it is the same as the possibility of getting physically wet while watching a computer simulation of rain. (4)
If you stick an LLM in the communication part, then you're putting yourself on the Retraction Watch list, not communicating.
#science #LLM #AI #GenAI #GenerativeAI #AIHype #hype
(1) I know this is a cartoonishly simple view of science, but I do firmly believe that something along these lines is the backbone of it, however real-world messy it becomes in practice.
(2) A large number of computer scientists are very sloppy about this process--and I have been in the past too--but that does not mean it should be condoned.
(3) Things are so dire that very few even seem to have the thought that this is something you should try to do.
(4) Yes, you might discover something while watching the LLM glop, but that's you, the human being, making the discovery, not the AI, in a chance manner despite the process, not in a systematic manner enhanced by the process. You could likewise accidentally spill a glass of water on yourself while watching RainSim.
I feel like people have been sold the idea that #GenerativeAI must provide productivity gains, and many don't bother to examine whether it really does.
DeepSeek launched a free, open-source large-language model in late December, claiming it was developed in just two months at a cost of under $6 million — a much smaller expense than the one called for by Western counterparts.The "Western counterparts" are claiming training a model might take years and billions of dollars. This has always been a hyped-up grift, with snake oil salesmen and con artists being showered with money and power. It's really quite amazing how profoundly unintelligent "the market" is in practice.These developments have stoked concerns about the amount of money big tech companies have been investing in AI models and data centers, and raised alarm that the U.S. is not leading the sector as much as previously believed.
The sad reality is that the US could lead in this field (1), if we'd stop routinely putting narcissists and con artists in charge and showering them with praise even when they fail.
#AI #GenAI #GenerativeAI #LLM #SnakeOil #hype #grift #MarketCapitalism
(1) Putting aside whether we should, which is an important question.
The influence of powerful imagery and rhetorics in promotional material for computing is neither new nor surprising. There is a longstanding tradition of overselling the latest technology, claiming it to be the next (industrial) revolution or promising that it will outperform human beings. With the passage of time it may become difficult to recognize these invented ideas and images that have acquired a life of their own and have become integrated as part of a historical narrative. As modern, digital electronic computing is nearing its 100th anniversary, such recognition does not become easier, though we may be in need of it more than ever before.From https://cacm.acm.org/opinion/the-myth-of-the-coder/This particular case, where the praise of automatic programming implied the obsolescence of the coder, can be instructive for us today. There is a line that runs from Grace Hopper’s selling of “automatic coding” to today’s promises of large AI models such as Chat-GPT for revolutionizing computing by automating programming or even making human programmers obsolete.19,20 Then as now, it is certainly the case that the automation of some parts of programming is progressing, and it will upset or even redefine the division of labor. However, this is not a simple straightforward process that replaces the human element in one or more specific phases of programming by the computer itself. Rather, practice adopts new techniques to assist with existing tasks and jobs. Such changes do not generalize easily, and using titles as like “coders”—or today’s “prompt engineers,”—while memorable, does not do justice to the subtle process of changing practice.
#ComputerScience #computers #computing #programming #dev #tech #hype #GPT #ChatGPT #Copilot
The bubble has begun to burst. Users have lost faith, clients have lost faith, VC’s have lost faith.From: Five signs that the GenAI honeymoon is overGenAI bubble, November, 2022 - July 2024, RIP.
https://garymarcus.substack.com/p/five-signs-that-the-genai-honeymoon
so much of the promise of generative AI as it is currently constituted, is driven by rote entitlement.
Very nice analysis by Brian Merchant ( @brianmerchant@mastodon.social ) here: https://www.bloodinthemachine.com/p/why-is-sam-altman-so-obsessed-with
He puts into clear terms what had previously been an unarticulated, creeping suspicion I had about #GenAI. Clearly there are many angles from which to come at what's going on with #AI #hype , but I appreciate this one quite a bit.
Brutal takedown of the bullsh&*# that is "self-driving cars": https://www.youtube.com/watch?v=2DOd4RLNeT4
It's a long video but frankly you can get the gist of most of it by scanning over the chapter titles. "Hitting Fake Children". "Hitting Real Children". "FSD Expectations" is a long list of the various lies #Elon #Musk has told about "full self driving" Teslas. Also the "Openpilot" chapter has a picture of Elon Musk's face as a dartboard.
The endless hype and full-on lies of the self-driving-car con from roughly 2016 to 2020 resembles the #AI #hype about #LLMs like #ChatGPT going on right now. If you've been in this industry long enough and have been honest with yourself about it you've seen all this before. Until something significant changes we really ought to view anything coming out of the tech sector with deep suspicion (https://bucci.onl/notes/Another-AI-Hype-Cycle).
All it'd take is one clever math result demonstrating you don't need absolutely gigantic neural networks trained on mind-bogglingly-huge datasets to achieve the AI goals of most companies, and NVIDIA's hardware dominance evaporates. Why would you spend thousands or tens of thousands of dollars on a GPU that uses 300 Watts of power when you could achieve the same thing with an ASIC or FPGA that uses 3 Watts? This is already true for many applications, but apparently it hasn't been widely realized yet. It'll be hard to ignore if/when it becomes true for the vast majority of applications. Which it could.
This is very reminiscent of the dot-com bubble, which expanded and then popped when I was in my early 20s.
The greatest risk is that large language models act as a form of ‘shock doctrine’, where the sense of world-changing urgency that accompanies them is used to transform social systems without democratic debate.
From https://danmcquillan.org/ai_thatcherism.html
One thing that these models definitely do, though, is transfer control to large corporations. The amount of computing power and data required is so incomprehensibly vast that very few companies in the world have the wherewithal to train them. To promote large language models anywhere is privatisation by the back door. The evidence so far suggests that this will be accompanied by extensive job losses, as employers take AI's shoddy emulation of real tasks as an excuse to trim their workforce.
Same source:
Thanks to its insatiable appetite for data, current AI is uneconomic without an outsourced global workforce to label the data and expunge the toxic bits, all for a few dollars a day. Like the fast fashion industry, AI is underpinned by sweatshop labour.
Dan McQuillan (@danmcquillan@kolektiva.social) has been excellent on AI #hype.