Search results for tag #ml
Meta Garbage Collection: Using OCaml's GC to GC Rust https://lobste.rs/s/p3z0zw #ml #rust
https://soteria-tools.com/blog/meta-garbage-collection
Generative AI Is an engineering disaster. A shockingly inefficient trillion-dollar project…
Paywalled article:
https://www.theatlantic.com/technology/2026/07/generative-ai-engineering-disaster/687901/
#tech #technology #BigTech #IT #AI #ArtificialIntelligence #LLM #LLMs #ML #MachineLearning #GenAI #generativeAI #AIAgent #AISlop #FuckAI #Fuck_AI #enshittification #microslop #microsoft #copilot #meta #google #NVIDIA #amazon #gemini #OpenAI #ChatGPT #anthropic #claude
Alien intelligence is what we are building with #ML https://www.theguardian.com/books/2026/jun/21/m-john-harrison-if-we-met-a-real-alien-wed-have-no-clue-what-they-thought
At BIML we have not changed our mind about the Anthropic fable/mythos export control disaster. We talk about the whole ironic thing in some detail here. Neither anthropic nor the US government are right.
Some time ago, I decided to add my thoughts about "AI" to my site…
#tech #technology #BigTech #IT #AI #ArtificialIntelligence #LLM #LLMs #ML #MachineLearning #GenAI #generativeAI #AIAgent #AISlop #FuckAI #Fuck_AI #enshittification #microslop #microsoft #copilot #meta #google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
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Generative Al is based on massive theft from creatives, without consent, credit or compensation. Using gen-Al is asking a chatbot to spit out the combined efforts of ripped-off creatives. It is industrializing and devaluing human expression, artistry and craftsmanship. Creatives are losing their jobs and motivation because tech corporations unscrupulously absorb and exploit their work.
Tech corporations are building more and more huge Al data centers, consuming lots of internet bandwidth, energy, water and other valuable resources, increasing scarcity, prices and emissions, degrading the already fragile environment.
When you're using an LLM like a chatbot or image generator, every bit of data you submit helps build a personal profile of you and your relatives, contributing to the power and reach of corporations and governments, and decreasing your privacy and security.
Generative Al enables fake images, videos, text and speech that are widely used for abuse, deception, cybercrime, misinformation and propaganda, polluting safety, justice, science, news reporting and much more.
Al use outsources your thinking and decision-making. Challenging your brain is essential to maintain mental agility, and to avoid Al dependency. Increasingly leaving tasks up to chatbots and gen-Al will result in a decline of skills, problem-solving capacity, independence and self-confidence.
#tech #technology #BigTech #IT #AI #ArtificialIntelligence #LLM #LLMs #ML #MachineLearning #GenAI #generativeAI #AIAgent #AISlop #FuckAI #Fuck_AI #enshittification #microslop #microsoft #copilot #meta #google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
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Two-panel comic, showing a slick businessman in the first frame, grinningly pointing to a product promotion image, while saying "With AI image generation, we don't have to pay artists to advertise our product."
In the second panel, a potential customer approaches the image, and says "Hmm, AI… Must be a cheap scammy product if they couldn't afford an artist."
New Silver Bullet episodes focus on the emerging field of #ML security ( #MLsec for short). And feature:
@gadi https://berryvilleiml.com/2026/03/02/silver-bullet-security-podcast-154-gadi-evron/
Giovanni Vigna https://berryvilleiml.com/2026/04/01/silver-bullet-security-podcast-155-giovanni-vigna/
@philvenables https://berryvilleiml.com/2026/05/01/silver-bullet-security-podcast-156-phil-venables/
AI/ML Security
<https://openssf.org/groups/ai-ml-security/>
＂This working group is situated at the intersection between security and artificial intelligence (AI). We explore the security risks associated with Large Language Models (LLMs), Generative AI (GenAI), and other forms of artificial intelligence and machine learning (ML), and their impact on open source projects, maintainers, their security, communities, and adopters. Furthermore, we explore using AI and ML to strengthen the security of other open source projects.
This group in collaborative research and peer organization engagement to explore topics related to AI and security. This includes security for AI development (e.g., supply chain security) but also using AI for security. We are covering risks posed to individuals and organizations by improperly trained models, data poisoning, privacy and secret leakage, prompt injection, licensing, adversarial attacks, and any other similar risks.
This group leverages prior art in the AI/ML space,draws upon both security and AI/ML experts, and pursues collaboration with other communities (such as the CNCF’s AI WG, LFAI & Data, AI Alliance, MLCommons, and many others) who are also seeking to research the risks presented by AL/ML to OSS in order to provide guidance, tooling, techniques, and capabilities to support open source projects and their adopters in securely integrating, using, detecting and defending against LLMs. …＂
Legislators in the US state of Maine have voted through a moratorium on building large data centres, becoming the first US state to do so. The measure will become law if not vetoed by Democratic Governor, Janet Mills.
https://www.rte.ie/news/world/2026/0415/1568275-maine-data-centres/
The slopification of the world continues…
#tech #technology #BigTech #IT #AI #ArtificialIntelligence #LLM #LLMs #ML #MachineLearning #GenAI #generativeAI #AIAgent #AISlop #FuckAI #Fuck_AI #enshittification #microslop #microsoft #copilot #meta #google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
From 'BuddhaBot' to $1.99 chats with AI Jesus, the faith-based tech boom is here
https://apnews.com/article/religious-chatbots-ai-technology-jesus-buddhabot-e1ed4832b25a23ee85292da681a0ec37?utm_source=flipboard&utm_medium=activitypubPosted into Top Stories @top-stories-AssociatedPress
Decipher covered the mythos controversy with a nice bottom line (provided by BIML)...fix the dang software.
#MLsec #swsec #appsec #ML #AI #mythos
https://youtu.be/uCyvQ_ubXo8?si=9GF7QfOmyDYVJBVO&t=428
https://decipher.sc/2026/04/10/anthropics-claude-mythos-is-just-the-beginning/
I've recently summed up my thoughts on generative "AI" on my homepage. Here's a screenshot of that section.
#tech #technology #BigTech #IT #AI #ArtificialIntelligence #LLM #LLMs #ML #MachineLearning #GenAI #generativeAI #AIAgent #AISlop #FuckAI #Fuck_AI #enshittification #microslop #microsoft #copilot #meta #google #NVIDIA #gemini #OpenAI #ChatGPT #anthropic #claude
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My thoughts on generative "AI"
I'm glad generative artificial "intelligence" was not a thing yet during the vast majority of my career. A number of realizations arose while exploring generative Large Language Models…
Generative AI is based on massive theft from creatives, without consent, credit or compensation. Using gen-AI is asking a chatbot to spit out the combined efforts of ripped-off creatives. It is industrializing and devaluing human expression, artistry and craftsmanship. Creatives are losing their jobs and motivation because tech corporations unscrupulously absorb and exploit their work. If you appreciate art, support the artists, not the thieves of their labor.
Tech corporations are building more and more huge data centers for AI processing, consuming lots of internet bandwidth, energy, water and more, increasing scarcity, prices and emissions, degrading the already fragile environment.
Unless you're using a fully local AI configuration, every bit of data you submit contributes to the power and reach of corporations and governments, decreasing your privacy and security.
Generative AI enables deepfakes that are widely used for abuse, deception, cybercrime, misinformation and propaganda, polluting justice, science advancement and news report credibility.
More text doesn't fit in this Alt text, but everything can be read over at https://metinseven.nl
The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners.From The reanimation of pseudoscience in machine learning and its ethical repercussions here: https://www.cell.com/patterns/fulltext/S2666-3899(24)00160-0. It's open access.
In other words ML--which includes generative AI--is smuggling long-disgraced pseudoscientific ideas back into "respectable" science, and rejuvenating the harms such ideas cause.
#AI #GenAI #GenerativeAI #LLMs #MachineLearning #ML #AIEthics #science #pseudoscience #JunkScience #eugenics #physiognomy
Some of the pathological beliefs we attribute to techbros were already present in this view of statistics that started forming over a century ago. Our writing is just data; the real, important object is the “hypothetical infinite population” reflected in a large language model, which at base is a random variable. Stable Diffusion, the image generator, is called that because it is based on latent diffusion models, which are a way of representing complicated distribution functions--the hypothetical infinite populations--of things like digital images. Your art is just data; it’s the latent diffusion model that’s the real deal. The entities that are able to identify the distribution functions (in this case tech companies) are the ones who should be rewarded, not the data generators (you and me).
So much of the dysfunction in today’s machine learning and AI points to how problematic it is to give statistical methods a privileged place that they don’t merit. We really ought to be calling out Fisher for his trickery and seeing it as such.
#AI #GenAI #GenerativeAI #LLM #StableDiffusion #statistics #StatisticalMethods #DiffusionModels #MachineLearning #ML
We later expanded this work and landed it as a chapter in a 2008 book Multiobjective Problem Solving from Nature, which is downloadable from https://link.springer.com/book/10.1007/978-3-540-72964-8 . You'll see the chapter starting on page 357 of that PDF (p 361 in the PDF's pagination). We applied a technique from the theory of coevolutionary algorithms to examine small instances of the game of Nim, and were able to make several interesting statements about that game. Arthur Samuel's original papers on checkers were about learning by self-play, a particularly simple form of coevolutionary algorithm, as I argue in the introductory chapter of my PhD dissertation. Our technique is applicable to Samuel's work and any other work in that class--in other words, it's squarely "machine learning" in the sense Samuel meant the term.
Whatever you may think of this particular work of mine, it's bad news when a field forgets and rejects its own historical origins and throws away the early fruitful lines of work that led to its own birth. #GenerativeAI threatens to have a similar wilting effect on artificial intelligence and possibly on computer science more generally. The marketplace of ideas is monopolizing, the ecosystem of ideas collapsing. Not good.
#MachineLearning #ML #AI #ComputerScience #Coevolution #CoevoutionaryAlgorithm #checkers #Nim #BoardGames
https://mastodon.world/@Mer__edith/113197090927589168
Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI
With the growing attention and investment in recent AI approaches such as large language models, the narrative that the larger the AI system the more valuable, powerful and interesting it is is increasingly seen as common sense. But what is this assumption based on, and how are we measuring value, power, and performance? And what are the collateral consequences of this race to ever-increasing scale? Here, we scrutinize the current scaling trends and trade-offs across multiple axes and refute two common assumptions underlying the 'bigger-is-better' AI paradigm: 1) that improved performance is a product of increased scale, and 2) that all interesting problems addressed by AI require large-scale models. Rather, we argue that this approach is not only fragile scientifically, but comes with undesirable consequences. First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint. Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate. Finally, it exacerbates a concentration of power, which centralizes decision-making in the hands of a few actors while threatening to disempower others in the context of shaping both AI research and its applications throughout society.Currently this is on #arXiv which, if you've read any of my critiques, is a dubious source. I'd love to see this article appear in a peer-reviewed or otherwise vetted venue, given the importance of its subject.
I've heard through the grapevine that US federal grantmaking agencies like the #NSF (National Science Foundation) are also consolidating around generative AI. This trend is evident if you follow directorates like CISE (Computer and Information Science and Engineering). A friend told me there are several NSF programs that tacitly demand LLMs of some form be used in project proposals, even when doing so is not obviously appropriate. A friend of a friend, who is a university professor, has said "if you're not doing LLMs you're not doing machine learning".
This is an absolutely devastating mindset. While it might be true at a certain cynical, pragmatic level, it's clearly indefensible at an intellectual, scholarly, scientific, and research level. Willingly throwing away the diversity of your own discipline is bizarre, foolish, and dangerous.
If you accept the viewpoint of ecological rationality, data (about people) is not nearly as useful for predictive purposes as it's made out to be. There is a "less is more" phenomenon in many applications, especially those that claim to predict behaviors or outcomes of some kind. See also this talk: https://www.cs.princeton.edu/news/how-recognize-ai-snake-oil .
There is a "less is more" effect with food, too. People need a baseline amount of food to maintain health, but having significantly more food than that doesn't confer significantly more health. Also food can spoil if one hoards it.
If data is a liquid, it's more like milk than oil.
What you're seeing here is that for most categories, there is a linear increase in the number of submissions to the category year-over-year up until the end of the data series in 2021. Computer science is dramatically different: its increase looks exponential, and it looks like its rate of increase may have accelerated circa 2017. The chart on the right, which is the same data shown proportional instead of as raw counts, suggests computer science might be "eating" mathematics starting around 2017.
2017 is around when generative AI papers started to appear in large quantities. There was a significant advance in machine learning published around 2018 but known before then that made deep learning significantly more effective. Tech companies were already pushing this technology. #OpenAI (the #GPT / #ChatGPT maker) was founded in 2015; GPT-2 was released in early 2019. arXiv's charts don't show this, but I suspect these factors play a role in the seeming phase shift in their CS submissions in 2017.
We don't know what 2022 and 2023 would look like on a chart like this but I expect the exponential increase will have continued and possibly accelerated.
In any case, this trend is extremely concerning. The exponential increase in number of submissions to what is supposed to be an academic pre-print service is not reasonable. There hasn't been an exponential increase in the number of computer scientists, nor in research funding, nor in research labs, nor in the output-per-person of each scientist. Furthermore, these new submissions threaten to completely swamp all other material: before long computer science submissions will dwarf those of all over fields combined; since this chart stops at 2021 they may have already! arXiv's graphs do not break down the CS submissions by subtopic, but I suspect they are in the machine learning/generative AI/LLM space and that submissions on these topics dwarf the other subdisciplines of computer science. Finally, to the extent that arXiv has quality controls in place for its archive, these can't possibly keep up with an exponentially-increasing rate of submissions. They will eventually fail if they haven't already (as I suggested in a previous post I think there are signs that their standards are slipping; perhaps that started circa 2017 and that's partly why the rate of submissions accelerated then?).
#arXiv #science #publishing #AI #ML #CS #computerscience
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