AI Slop is Flooding the Scientific Community
Scientific journals are dealing with a flood of AI slop and with the release of Prism, the fear is that it's only getting worse.
https://www.freezenet.ca/ai-slop-is-flooding-the-scientific-community/
#News #Technology #academia #AI #journals #OpenAI #paper #research #science
AI Slop is Flooding the Scientific Community
Scientific journals are dealing with a flood of AI slop and with the release of Prism, the fear is that it's only getting worse.
https://www.freezenet.ca/ai-slop-is-flooding-the-scientific-community/
#News #Technology #academia #AI #journals #OpenAI #paper #research #science
Sembla que #OpenAi no es pugui permetre perdre clients...estan molt pesats pregant-me que no marxi.
The AI Con authors Emily M. Bender (Professor of Linguistics, University of Washington) and Alex Hanna (Director of Research, Distributed AI Research Institute) break down a #NYT opinion piece that exemplifies hype laundering: how AI industry narratives get legitimized through respected voices in prestigious outlets.
The article is "Stop Worrying, and Let A.I. Help Save Your Life" by Dr. Robert Wachter, chair of the Department of Medicine at #UCSF, published in the New York Times on January 19, 2026.
Dr. Wachter admits he's replacing professional medical consultations with colleagues—what physicians call "curbside consults"- with ChatGPT queries. He claims AI's input is "virtually always useful," though he admits it's sometimes "just plain wrong." Emily responds: "People who really should know better have fallen for this." Alex notes the absurdity: "This seems like really a weird kind of approach to medical practice... Maybe someone who is concerned about their different medical conditions and had no place to turn, but someone at UCSF. I've been to UCSF. That's very alarming."
Wachter provides zero peer-reviewed studies, no outcome data, no comparative metrics. Just personal anecdotes claiming the tools work. Emily points out he's demonstrating "no evidence-based practice of checking like how well does this work and also how does it impact the work of physicians when they're using it."
The accountability problem is central. Alex observes that with a human colleague, "you would actually know it's coming from them and there's some accountability if they give you some just wild advice." With LLMs? No one is responsible when the answer is wrong. Emily: "The point isn't that the answers are unreliable. Is that there's no accountability for the answers."
She also raises automation bias concerns: "If you review the output, are you also reviewing the things that you didn't get to because it didn't come out as output?" The system's omissions may be as dangerous as its errors.
Wachter is against "overly restrict[ing] A.I. tools" by "setting an impossibly high bar." Classic regulatory capture language. Emily: "I want all medical devices to be tightly regulated."
https://www.twitch.tv/videos/2687163982 (just the video stream https://d2vi6trrdongqn.cloudfront.net/bc9948ab01f26c79a170_dair_institute_317110603102_1770049612/720p60/index-dvr.m3u8)
https://www.nytimes.com/2026/01/19/opinion/ai-health-medical-care.html
#ChatGPT ( #OpenAI) is #Trump's biggest donor, and #ICE uses ChatGPT. It's time to quit. http://quitgpt.org/
Twitch
The AI Con authors Emily M. Bender (Professor of Linguistics, University of Washington) and Alex Hanna (Director of Research, Distributed AI Research Institute) break down a #NYT opinion piece that exemplifies hype laundering: how AI industry narratives get legitimized through respected voices in prestigious outlets.
The article is "Stop Worrying, and Let A.I. Help Save Your Life" by Dr. Robert Wachter, chair of the Department of Medicine at #UCSF, published in the New York Times on January 19, 2026.
Dr. Wachter admits he's replacing professional medical consultations with colleagues—what physicians call "curbside consults"- with ChatGPT queries. He claims AI's input is "virtually always useful," though he admits it's sometimes "just plain wrong." Emily responds: "People who really should know better have fallen for this." Alex notes the absurdity: "This seems like really a weird kind of approach to medical practice... Maybe someone who is concerned about their different medical conditions and had no place to turn, but someone at UCSF. I've been to UCSF. That's very alarming."
Wachter provides zero peer-reviewed studies, no outcome data, no comparative metrics. Just personal anecdotes claiming the tools work. Emily points out he's demonstrating "no evidence-based practice of checking like how well does this work and also how does it impact the work of physicians when they're using it."
The accountability problem is central. Alex observes that with a human colleague, "you would actually know it's coming from them and there's some accountability if they give you some just wild advice." With LLMs? No one is responsible when the answer is wrong. Emily: "The point isn't that the answers are unreliable. Is that there's no accountability for the answers."
She also raises automation bias concerns: "If you review the output, are you also reviewing the things that you didn't get to because it didn't come out as output?" The system's omissions may be as dangerous as its errors.
Wachter is against "overly restrict[ing] A.I. tools" by "setting an impossibly high bar." Classic regulatory capture language. Emily: "I want all medical devices to be tightly regulated."
https://www.twitch.tv/videos/2687163982 (just the video stream https://d2vi6trrdongqn.cloudfront.net/bc9948ab01f26c79a170_dair_institute_317110603102_1770049612/720p60/index-dvr.m3u8)
https://www.nytimes.com/2026/01/19/opinion/ai-health-medical-care.html
#ChatGPT ( #OpenAI) is #Trump's biggest donor, and #ICE uses ChatGPT. It's time to quit. http://quitgpt.org/
Twitch
A book recommendation: Parmy Olson's Supremacy: AI, ChatGPT, and the Race That Will Change the World. I highly recommend it to anyone interested in AI. The book tells the origin stories behind the familiar names: how ChatGPT came to be, how DeepMind ended up under Google, how Anthropic and Claude were born as a reaction to OpenAI's shift in direction, and how GitHub Copilot and Gemini entered the picture.
What I found especially fascinating is the massive amount of research behind OpenAI and how open and idealistic the starting points were for everything until business took over. Musk, Altman, Hassabis and the other key figures have been on completely different pages: some care deeply about ethics and safety, some couldn't care less. At the same time, everyone acknowledges the risks and talks about them openly, yet they all act differently.
I've been tinkering with chatbots since the MegaHAL days and as a developer I use all of these tools often, so reading the behind-the-scenes story was captivating. Won the Financial Times Business Book of the Year 2024, and deservedly so.
PS. If you're planning to guilt trip me about using AI or being interested in it, I'll just block you.
#AI #Claude #ChatGPT #OpenAI #Anthropic #Books #Bookstodon #BookSuggestions
Anthropic is spending millions of dollars to air commercials during Sunday night’s National Football League championship game to slam rival OpenAI for its plan to sell ads on its ChatGPT chatbot. https://www.japantimes.co.jp/business/2026/02/08/tech/anthropic-super-bowl-ads-openai-chatgpt/?utm_medium=Social&utm_source=mastodon #business #tech #anthropic #ai #superbowl #advertising #openai #chatgpt
"Microsoft has used OpenAI’s billions in inference spend to cover up the collapse of the growth of the Intelligent Cloud segment. OpenAI’s inference spend now represents around 10% of Azure’s revenue.
Microsoft, as I discussed a few weeks ago, is in a bind. It keeps buying GPUs, all while waiting for the GPUs it already has to start generating revenue, and every time a new GPU comes online, its depreciation balloons. Capex for GPUs began in seriousness in Q1 FY2023 following October’s shipments of NVIDIA’s H100 GPUs, with reports saying that Microsoft bought 150,000 H100s in 2023 (around $4 billion at $27,000 each) and 485,000 H100s in 2024 ($13 billion). These GPUs are yet to provide much meaningful revenue, let alone any kind of profit, with reports suggesting (based on Oracle leaks) that the gross margins of H100s are around 26% and A100s (an older generation launched in 2020) are 9%, for which the technical term is “dogshit.” Somewhere within that pile of capex also lies orders for H200 GPUs, and as of 2024, likely NVIDIA’s B100 (and maybe B200) Blackwell GPUs too.
You may also notice that those GPU expenses are only some portion of Microsoft’s capex, and the reason is because Microsoft spends billions on finance leases and construction costs. What this means in practical terms is that some of this money is going to GPUs that are obsolete in 6 years, some of it’s going to paying somebody else to lease physical space, and some of it is going into building a bunch of data centers that are only useful for putting GPUs in.
And none of this bullshit is really helping the bottom line! Microsoft’s More Personal Computing segment — including Windows, Xbox, Microsoft 365 Consumer, and Bing — has become an increasingly-smaller part of revenue, representing in the latest quarter a mere 17.64% of Microsoft’s revenue in FY26 so far, down from 30.25% a mere four years ago."
https://www.wheresyoured.at/premium-the-haters-guide-to-microsoft/
#Microsoft #AI #GenerativeAI #BigTech #OpenAI #IntelligentCloud
"Microsoft has used OpenAI’s billions in inference spend to cover up the collapse of the growth of the Intelligent Cloud segment. OpenAI’s inference spend now represents around 10% of Azure’s revenue.
Microsoft, as I discussed a few weeks ago, is in a bind. It keeps buying GPUs, all while waiting for the GPUs it already has to start generating revenue, and every time a new GPU comes online, its depreciation balloons. Capex for GPUs began in seriousness in Q1 FY2023 following October’s shipments of NVIDIA’s H100 GPUs, with reports saying that Microsoft bought 150,000 H100s in 2023 (around $4 billion at $27,000 each) and 485,000 H100s in 2024 ($13 billion). These GPUs are yet to provide much meaningful revenue, let alone any kind of profit, with reports suggesting (based on Oracle leaks) that the gross margins of H100s are around 26% and A100s (an older generation launched in 2020) are 9%, for which the technical term is “dogshit.” Somewhere within that pile of capex also lies orders for H200 GPUs, and as of 2024, likely NVIDIA’s B100 (and maybe B200) Blackwell GPUs too.
You may also notice that those GPU expenses are only some portion of Microsoft’s capex, and the reason is because Microsoft spends billions on finance leases and construction costs. What this means in practical terms is that some of this money is going to GPUs that are obsolete in 6 years, some of it’s going to paying somebody else to lease physical space, and some of it is going into building a bunch of data centers that are only useful for putting GPUs in.
And none of this bullshit is really helping the bottom line! Microsoft’s More Personal Computing segment — including Windows, Xbox, Microsoft 365 Consumer, and Bing — has become an increasingly-smaller part of revenue, representing in the latest quarter a mere 17.64% of Microsoft’s revenue in FY26 so far, down from 30.25% a mere four years ago."
https://www.wheresyoured.at/premium-the-haters-guide-to-microsoft/
#Microsoft #AI #GenerativeAI #BigTech #OpenAI #IntelligentCloud
OpenAI demonstriert mit GPT-5 die Steuerung eines physischen Biolabors bei Ginkgo Bioworks. Das Modell optimierte Parameter für die zellfreie Proteinsynthese über 36.000 autonome Experimente. Im Gegensatz zur Bayes-Optimierung nutzte die KI biologisches Vorwissen, um den Suchraum effizienter zu navigieren. Die Produktionskosten sanken dabei um 40 Prozent. #OpenAI #GPT5 #GinkgoBioworks
https://www.all-ai.de/news/beitrage2026/gpt5-labor-biologie
OpenAI demonstriert mit GPT-5 die Steuerung eines physischen Biolabors bei Ginkgo Bioworks. Das Modell optimierte Parameter für die zellfreie Proteinsynthese über 36.000 autonome Experimente. Im Gegensatz zur Bayes-Optimierung nutzte die KI biologisches Vorwissen, um den Suchraum effizienter zu navigieren. Die Produktionskosten sanken dabei um 40 Prozent. #OpenAI #GPT5 #GinkgoBioworks
https://www.all-ai.de/news/beitrage2026/gpt5-labor-biologie
Understanding Neural Network, Visually…
https://visualrambling.space/neural-network
#tech #technology #science #math #MachineLearning #ML #BigTech #AI #ArtificialIntelligence #LLM #LLMs #MachineLearning #GenAI #generativeAI #Meta #Google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
Understanding Neural Network, Visually…
https://visualrambling.space/neural-network
#tech #technology #science #math #MachineLearning #ML #BigTech #AI #ArtificialIntelligence #LLM #LLMs #MachineLearning #GenAI #generativeAI #Meta #Google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
⚠️ Nvidia won't release new gaming GPU for 'first year in three decades' due to RAM shortage — and it's also slashing RTX 50 production
｢ AI ｣
