started listening The Tucker Carlson Show - Sam Altman on God, Elon Musk and the Mysterious Death of His Former Employee
absolutely epic episode
#LLM #AI
llm
I spent 15 mins vibe coding with Google Gemini and made my first chrome extension showing train arrivals at Kendall/MIT......
And I still don't know anything about JavaScript... All I know is cars with the number greater or equal than 1900 are new ones...
#LLM #AI #VibeCoding #MBTA #Google #GoogleGemini
Edited 5d ago
TECH BROs v. THE OCEAN:
CRYPTO: let's boil the oceans to create fake money for criminals. We'll fleece the rubes and make miillions. LOL you own that JPG now. Sure you do.
LLMs: let's boil the oceans to create pure garbage out of people's intellectual property. We'll steal from everyone and make millions.
THE OCEANS: brb making some hurricanes
Edited 1y ago
On parle pas mal de cette dame qui est tombé dans le piège d'un faux brad pit.
Mais on parle peu de toutes ces personnes qui sont tombés dans le piège de faux investissement avec les #nft .
Et on parle encore moins de toutes ces entreprises qui tombent dans le piège de ces fausses solutions que sont les #LLM
Beware of academic "papers" about LLMs, especially on arXiv. The signal-to-noise ratio is alarmingly low, especially relative to other subdisciplines of computer science: https://wandering.shop/@xgranade/114055293092825449
#AI #GenAI #LLM #AcademicPublishing #arXiv #ComputerScience
#AI #GenAI #LLM #AcademicPublishing #arXiv #ComputerScience
Speaking of widespread low-quality scientific publication and the need to take care with words: https://retractionwatch.com/2025/02/10/vegetative-electron-microscopy-fingerprint-paper-mill/
#AI #GenerativeAI #LLM #AISlop #InformationOilSpill #AcademicPublishing #ScientificPublishing #PaperMill #PeerReview
The phrase was so strange it would have stood out even to a non-scientist. Yet “vegetative electron microscopy” had already made it past reviewers and editors at several journals when a Russian chemist and scientific sleuth noticed the odd wording in a now-retracted paper in Springer Nature’s Environmental Science and Pollution Research.
Today, a Google Scholar search turns up nearly two dozen articles that refer to “vegetative electron microscopy” or “vegetative electron microscope,” including a paper from 2024 whose senior author is an editor at Elsevier, Retraction Watch has learned. The publisher told us it was “content” with the wording.
#AI #GenerativeAI #LLM #AISlop #InformationOilSpill #AcademicPublishing #ScientificPublishing #PaperMill #PeerReview
My lab's using an LLM in an experiment for the first time. It's interesting to see how that's going.
For one thing, we (roughly a dozen AI experts) struggle to understand whether this thing is doing what we want. It's just such an ambiguous interface! We send it some text and a picture and get text back, but what is it doing? We're forced to run side experiments just to validate this one component. That makes me uncomfortable, and wonder why folks who aren't AI researchers would do such a thing.
Worse, my lab mate keeps doing more prompt engineering, data pre-processing, and restricting the LLM's vocabulary to make it work. That's a lot of effort the LLM was meant to take care of which is becoming our problem instead.
It feels like he's incrementally developing a domain specific language for this project, and all the LLM is doing is translating between English into this DSL! If that's the case, then there's no point in using an LLM, but it's hard to tell when we've crossed that line.
You are not doing science research if you stuff an LLM into the critical path of your experiments. You are, instead, producing science-shaped artifacts with the same peripheral relationship to science that LLM text output has to truth.
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.
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.
#lispygopherclimate #lisp #lambdamoo #programming #podcast #live https://communitymedia.video/w/5vAGot7LujjpFQ5Mzz6bFM
0UTC Wednesdays Weekly
@kentpitman #climateCrisis
Breaking The Complexity Barrier (Again) (Again) vs #LLM #AI .
(+ Reliably #jailbreaking LLM AI)
Capitalization and lisp
Following Terry's lead, My #McCLIM, programs and the #racket people
Talkin' 'bout my generation #gopher
telnet lambda.moo.mud.org 8888
co guest
@join screwtape
"yo<RET>
:wave
#unix_surrealism
@hairylarry @nosrednayduj @mdhughes et al.!
Edited 217d ago
In-person Certified #Scrum Developer training in Seoul, South #Korea this summer (16-17 Jul)!
Also: 18 Jul "Essential TDD w/ #AI / #LLM Assist" workshop. ( https://agile-korea.com/tdd-ai/ ) An additional 10% off for both.
https://www.scrumalliance.org/courses-events/search/coursedetail?id=202504181
Edited 217d ago
I read this with interest…
"Billionaires think that they're the smartest people who've ever lived, because they're the wealthiest people who've ever lived. If they were wrong about anything, then why would they have been so financially successful? […] They believe that everything can be quantified, like a person's IQ, and that money is a good measure of how much someone is worth."
#tech #technology #BigTech #billionaires #TaxTheRich #capitalism #AI #LLM #LLMs #ML
On the limits of LLMs (Large Language models) and LRMs (Large Reasoning Models). The TL;DR: "Our findings reveal fundamental limitations in current models: despite sophisticated self-reflection mechanisms, these models fail to develop generalizable reasoning capabilities beyond certain complexity thresholds." Meaning: accuracy collapse.
Interesting paper from Apple. https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
Edited 157d ago
Did you know? #Fedify provides #documentation optimized for LLMs through the llms.txt standard.
Available endpoints:
https://fedify.dev/llms.txt — Core documentation overview
https://fedify.dev/llms-full.txt — Complete documentation dump
Useful for training #AI assistants on #ActivityPub/#fediverse development, building documentation chatbots, or #LLM-powered dev tools.
Senators Demand Transparency on Canceled Veterans Affairs Contracts
—
Following a ProPublica investigation into how DOGE had developed an error-prone AI tool to determine which VA contracts should be killed, a trio of lawmakers said the Trump administration continues to “stonewall” their requests for details.
#News #DOGE #Veterans #VA #AI #ArtificialIntelligence #LLM #Technology #Government
AI is bad compression. Every time you run training material through it, you get a lossy summary of that material back, along with some noise.
You quickly run out of *quality* training material and start dog-fooding the output back in. Then you end up with lossy summaries of lossy summaries, and eventually all your pizza sauce recipes are dog food.
How's that AI coding going for you? Ah... I see.
Wired: McDonald’s AI Hiring Bot Exposed Millions of Applicants' Data to Hackers Using the Password ‘123456’
"... Carroll and Curry, hackers with a long track record of independent security testing, discovered that simple web-based vulnerabilities—including guessing one laughably weak password—allowed them to access a Paradox.ai account and query the company's databases that held every McHire user's chats with Olivia. The data appears to include as many as 64 million records, including applicants' names, email addresses, and phone numbers...."
https://www.wired.com/story/mcdonalds-ai-hiring-chat-bot-paradoxai/
How often do you use #AI #LLM tools like: ChatGPT, Claude, Gemini, DeepSeek or Grok?
Options: (choose one)
Edited 147d ago
Unassailable proof that the internet is 99% cat pictures:
My profile pic is AI generated slop from the prompt: "A picture of some random AI slop. Surprise me."
The LLM used is one of those that was trained on screen scrapings of the entire webiverse.
The picture is, of course, by the iron laws of the internet, a cute kitten.
…the term hallucinations is subtly misleading. It suggests that the bad behavior is an aberration, a bug, when it’s actually a feature of the probabilistic pattern-matching mechanics of neural networks.
—Karen Hao, Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
#ai #llms #llm #hallucinations
'Hallucinations are the core feature of LLMs. We just call it “hallucinations” when they do something we don’t want, and “intelligence” in the cases where it’s useful to us.' - Birgitta Böckeler
https://martinfowler.com/articles/exploring-gen-ai/i-still-care-about-the-code.html
On reflection, I think the big mistake is the conflation of #AI with #LLM and #MachineLearning.
There are genuine exciting advances in ML with applications all over the place, in science, (not least in my own research group looking at high resolution regional climate downscaling), health diagnostics, defence etc. But these are not the AIs that journalists are talking about, nor that are really related the LLMs.
They're still good uses of GPUs and will probably produce economic benefits, but probably not the multi- trillion ones the pundits seem to be expecting
https://fediscience.org/@Ruth_Mottram/114896256761569397
Ruth_Mottram - My main problem with @edzitron.com 's piece on the #AIbubble is that I agree with so much of it.
I'm now wondering if I've missed something about #LLMs? The numbers and implications for stock markets are terrifyingly huge!
Running Deepseek R1 671b fully locally on a $2000 EPYC server. Idle wattage is just 60w while with 260w under load.
This setup runs a 671B model in Q4 quantization at 3-4 TPS, running a Q8 would need something beefier. To run a 671B model in the original Q8 at 6-8 TPS you'd need a dual socket EPYC server motherboard with 768GB of RAM.
The idea that LLMs use an inordinate amount of power to run is very much outdated at this point.
https://digitalspaceport.com/how-to-run-deepseek-r1-671b-fully-locally-on-2000-epyc-rig/
Edited 102d ago
It suddenly struck me the other day that generative AI used in government and media has a very Memoirs Found in a Bathtub quality about it. One of the premises of Lem's (amazing) novel is that paper disintegrates for unknown reasons, leaving very few hard records of anything. The book is a mock diary of a person who lived through the disorienting chaos that ensued.
#AI #GenAI #GenerativeAI #LLM #tech #dev #StanislawLem #fiction #SciFi #ScienceFiction #cybernetics
#AI #GenAI #GenerativeAI #LLM #tech #dev #StanislawLem #fiction #SciFi #ScienceFiction #cybernetics
Edited 131d ago