17 Matching Annotations
  1. Mar 2024
    1. Next to the xz debacle where a maintainer was psyops'd into backdooring servers, this is another new attack surface: AI tools make up software packages in what they generate which get downloaded. So introducing malware is a matter of creating malicious packages named the way they are repeatedly named by AI tools.

    1. https://web.archive.org/web/20240305082302/https://aiedusimplified.substack.com/p/on-not-using-generative-ai

      This seems an interesting piece on the use of algogens. It probably does not address the issues around transparency, labor, footprint etc. But it does seem to address the search for the spot where algogens are useful in one's own workflow. Like me in [[Coding Personal Tools With GitHub Co-Pilot]]. There getting to action faster, and saving time are key. But only if you use it as intermediate step, never as a result to be used as is or as final output.

      Via [[Stephen Downes]] https://www.downes.ca/post/76336

  2. Feb 2024
    1. Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself. If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized? If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects"

      Broderick: https://www.garbageday.email/p/ai-search-doomsday-cult, Anderson: https://arxiv.org/abs/2305.17493

      AI search hides the authors of the material it presents, summarising it is abstracting away the authors. It doesn't bring readers to those authors, it just presents a summary to the searcher as end result. Take it or leave it. At the same time, if one searches for something you know about, you see those summaries are always of. Leaving you guessing how of it is when searching something you don't know about. Search should never be the endpoint, always a starting point. I think that is my main aversion against AI search tools. Despite those clamoring 'it will get better over time' I don't think it will easily because the tool nor its makers have any interest in the quality of output necessarily and definitely can't assess it. So what's next, humans factchecking AI output. Why not prevent bs at its source? Nice ref to Maggie Appleton's centipede metaphor in [[The Expanding Dark Forest and Generative AI]]

  3. Jan 2024
  4. Dec 2023
    1. "hadn’t seriously considered the future economic impact on illustrators" This sounds too much like the 'every illegal download is a misplaced sale' trope of the music industry. There are many reasons to not use algogens, or opt for different models for such generation than the most popular public facing tools. Missed income for illustrators by using them in blog posts isn't one. Like with music downloads there's a whole world of users underneath the Cosean floor. My blog or presentations will never use bought illustrations, I started making lots of digital photos for that reason way back in 2003, and have been using open Creative Commons licenses. And now may try to generate a few images, if it's not too work intensive. Not to say that outside the mentioned use case of blogs and other sites (the ones that already now are indistinguishable from generated texts and only have generating ad eyeballs as purpose), the lower end of the existing market will get eroded. I bet that at the same time there will be a growing market for clearly human made artefacts as status symbol too. The Reverse Turing effect in play. I've paid more for prints of artwork, both graphics and photos, made in the presence of the artist than one printed after their death for instance. They adorn the walls at home rather than my blog though.

  5. Nov 2023
    1. Creative Commons can be relied upon to take a generally pro-ownership and libertarian stance regarding rules and regulation

      This is bothersome seen from my perspective of both a CC advocate and European national chapter and as a CC using maker. In my experience makers using CC use CC because they want to limit the ownership current international copyright laws and treaties bestow on them, as they see them as obstacle and greedy, and generally not serving the maker but later exploiters of artefacts. Also the perspective of contributing to the common good / pool of culture is frequent, and counter libertarian angles. I need to check but I think it might also be a ways off from Lessig's original idea for CC as expressed in [[Free Culture by Lawrence Lessig]].

    2. https://web.archive.org/web/20231108095251/https://www.downes.ca/cgi-bin/page.cgi?post=75761

      [[Stephen Downes]] on CC and their answers to US copyright questions wrt generative algo's.

  6. Sep 2023
    1. https://www.filosofieinactie.nl/blog/2023/9/5/open-source-large-language-models-an-ethical-reflection (archive version not working) Follow-up wrt openness of LLMs, after the publication of the inteprovincial ethics committee on ChatGPT usage within provincial public sector in NL. At the end mentions the work by Radboud Uni I pointed them to. What are their conclusions / propositions?

  7. Aug 2023
    1. Roland Barthes (1915-1980, France, literary critic/theorist) declared the death of the author (in English in 1967 and in French a year later). An author's intentions and biography are not the means to explain definitively what the meaning of a (fictional I think) text is. [[Observator geeft betekenis 20210417124703]] dwz de lezer bepaalt.

      Barthes reduceert auteur to de scribent, die niet verder bestaat dan m.b.t. de voortbrenging van de tekst. Het werk staat geheel los van de maker. Kwam het tegen in [[Information edited by Ann Blair]] in lemma over de Reader.

      Don't disagree with the notion that readers glean meaning in layers from a text that the author not intended. But thinking about the author's intent is one of those layers. Separating the author from their work entirely is cutting yourself of from one source of potential meaning.

      In [[Generative AI detectie doe je met context 20230407085245]] I posit that seeing the author through the text is a neccesity as proof of human creation, not #algogen My point there is that there's only a scriptor and no author who's own meaning, intention and existence becomes visible in a text.

  8. May 2023
    1. This clearly does not represent all human cultures and languages and ways of being.We are taking an already dominant way of seeing the world and generating even more content reinforcing that dominance

      Amplifying dominant perspectives, a feedback loop that ignores all of humanity falling outside the original trainingset, which is impovering itself, while likely also extending the societal inequality that the data represents. Given how such early weaving errors determine the future (see fridges), I don't expect that to change even with more data in the future. The first discrepancy will not be overcome.

    2. This means they primarily represent the generalised views of a majority English-speaking, western population who have written a lot on Reddit and lived between about 1900 and 2023.Which in the grand scheme of history and geography, is an incredibly narrow slice of humanity.

      Appleton points to the inherent severely limited trainingset and hence perspective that is embedded in LLMs. Most of current human society, of history and future is excluded. This goes back to my take on data and blind faith in using it: [[Data geeft klein deel werkelijkheid slecht weer 20201219122618]] en [[Check data against reality 20201219145507]]

    3. But a language model is not a person with a fixed identity.They know nothing about the cultural context of who they’re talking to. They take on different characters depending on how you prompt them and don’t hold fixed opinions. They are not speaking from one stable social position.

      Algogens aren't fixed social entities/identities, but mirrors of the prompts

    4. A big part of this limitation is that these models only deal with language.And language is only one small part of how a human understands and processes the world.We perceive and reason and interact with the world via spatial reasoning, embodiment, sense of time, touch, taste, memory, vision, and sound. These are all pre-linguistic. And they live in an entirely separate part of the brain from language.Generating text strings is not the end-all be-all of what it means to be intelligent or human.

      Algogens are disconnected from reality. And, seems a key point, our own cognition and relation to reality is not just through language (and by extension not just through the language center in our brain): spatial awareness, embodiment, senses, time awareness are all not language. It is overly reductionist to treat intelligence or even humanity as language only.

    5. This disconnect between its superhuman intelligence and incompetence is one of the hardest things to reconcile.

      generative AI as very smart and super incompetent at the same time, which is hard to reconcile. Is this a [[Monstertheorie 20030725114320]] style cultural category challenge? Or is the basic one replacing human cognition?

    6. But there are a few key differences between content generated by models versus content made by humans.First is its connection to reality. Second, the social context they live within. And finally their potential for human relationships.

      yes, all generated content is devoid of an author context e.g. It's flat and 2D in that sense, and usually fully self contained no references to actual experiences, experiments or things outside the scope of the immediate text. As I describe https://hypothes.is/a/kpthXCuQEe2TcGOizzoJrQ

    7. Most of the tools and examples I’ve shown so far have a fairly simple architecture.They’re made by feeding a single input, or prompt, into the big black mystery box of a language model. (We call them black boxes because we don't know that much about how they reason or produce answers. It's a mystery to everyone, including their creators.)And we get a single output – an image, some text, or an article.

      generative AI currently follows the pattern of 1 input and 1 output. There's no reason to expect it will stay that way. outputs can scale : if you can generate one text supporting your viewpoint, you can generate 1000 and spread them all as original content. Using those outputs will get more clever.

    8. By now language models have been turned into lots of easy-to-use products. You don't need any understanding of models or technical skills to use them.These are some popular copywriting apps out in the world: Jasper, Copy.ai, Moonbeam

      Mentioned copy writing algogens * Jasper * Wordtune * copy.ai * quillbot * sudowrite * copysmith * moonbeam