how a.i. actually works

Back in July of 2020, I published a group post entitled “Philosophers on GPT-3.” At the time, most readers of Daily Nous had not heard of GPT-3 and had no idea what a large language model (LLM) is. How times have changed. Over the past few months, with the release of OpenAI’s ChatGPT and Bing’s AI Chatbot “Sydney” (which we learned a few hours after this post originally went up has “secretly” been running GPT-4) (as well as Meta’s Galactica—pulled after 3 days—and Google’s Bard—currently available only to a small number of people), talk of LLMs has exploded. It seemed like a good time for a follow-up to that original post, one in which philosophers could get together to explore the various issues and questions raised by these next-generation large language models. Here it is. As with the previous post on GPT-3, this edition of Philosophers On was put together by guest editor by Annette Zimmermann. I am very grateful to her for all of the work she put into developing and editing this post. Philosophers On is an occasional series of group posts on issues of current interest, with the aim of showing what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. The contributions that the authors make to these posts are not fully worked out position papers, but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are: Abeba Birhane (Senior Fellow in Trustworthy AI at Mozilla Foundation & Adjunct Lecturer, School of Computer Science and Statistics at Trinity College Dublin, Ireland), Atoosa Kasirzadeh (Chancellor’s Fellow and tenure-track assistant professor in Philosophy & Director of Research at the Centre for Technomoral Futures, University of Edinburgh), Fintan Mallory (Postdoctoral Fellow in Philosophy, University of Oslo), Regina Rini (Associate Professor of Philosophy & Canada Research Chair in Philosophy of Moral and Social Cognition), Eric Schwitzgebel (Professor of Philosophy, University of California, Riverside), Luke Stark (Assistant Professor of Information & Media Studies, Western University), Karina Vold (Assistant Professor of Philosophy, University of Toronto & Associate Fellow, Leverhulme Centre for the Future of Intelligence, University of Cambridge), and Annette Zimmermann (Assistant..

. Quando un sistema di AI sbaglia, la prima reazione è spesso la sorpresa. Come può una macchina così potente confondersi?  La risposta è meno intuitiva di quanto sembri: sbaglia esattamente per lo stesso motivo per cui funziona. Perché ragiona in termini di probabilità. Un'approssimazione del mondo Ogni sistema di AI, che classifichi immagini, formuli…

archived 12 May 2026 16:04:55 UTC

1. Introduction

What's behind the feverish AI discourse? Who thinks "AI is fake"? Is "the left" wrong to dismiss AI? Is that even what's happening? What's really going on with AI in 2026.

We share a responsibility to create and use empowering metaphors rather than misleading language, write Emily M. Bender and Nanna Inie.

L’ha creata l’attivista Antoinette Torres Soler, ma è davvero etica?

This article explains the complexities of language models for readers to grasp their limitations and societal impact.

If you are a user of LLM systems that use tools (you can call them “AI agents” if you like) it is critically important that you understand the risk of …

Despite what tech CEOs might say, large language models are not smart in any recognizably human sense of the word.

pemThe idea of an all-knowing computer program comes from science fiction and should stay there. Despite the seductive fluency of ChatGPT and other language models, they remain unsuitable as sources of knowledge. We must fight against the instinct to trust a human-sounding machine, argue Emily M. Bender & Chirag Shah. /em/ppem /em/ppDecades of science fiction have taught us that a key feature of a high-tech future is computer systems that give us instant access to seemingly limitless collections of knowledge through an interface that takes the form of a friendly (or sometimes sinisterly detached) voice. The early promise of the World Wide Web was that it might be the start of that collection of knowledge. With Meta’s a href="https://galactica.org/"Galactica/a, OpenAI’s a href="https://openai.com/blog/chatgpt/"ChatGPT/a and earlier this year a href="https://www.youtube.com/watch?v=_xLgXIhebxA"LaMDA/a from Google, it seems like the friendly language interface is just around the corner, too./pp span class="article-content-box" a href="https://iai.tv/articles/why-ai-must-learn-to-forget-auid-2302" class="iai-related-in-article click_on_suggestion_link--gtm-track" span class="iai-card" span class="iai-card--image" style="display: block;" img src="/assets/Uploads/_resampled/FillWyI0MDAiLCIyNzUiXQ/Why-AI-must-learn-to-forget.webp" alt="Why AI must learn to forget"/span span class="iai-card--content" style="display: block;" span class="iai-card--title" style="display: block;"SUGGESTED READING/span span class="iai-card--heading" style="display: block;"Why AI must learn to forget/span spanBy Ali Boyle/span /span /span /a /span /ppHowever, we must not mistake a convenient plot device—a means to ensure that characters always have the information the writer needs them to have—for a roadmap to how technology could and should be created in the real world. In fact, large language models like Galactica, ChatGPT and LaMDA are not fit for purpose as information access systems, in two fundamental and independent ways./ppFirst, what they are designed to do is to create coherent-seeming text. They do this by being cleverly built to take in vast quantities of training data and model the ways in which words co-occur across all of that text. The result is systems that can produce text that is very compelling when we as humans make sense of it. But the systems do not have any understanding of what they are producing, any communicative intent, any model of the world, or any ability to be accountable for the truth of what they are saying. This is why, in 2021, one of us (Bender) and her co-authors referred to them as a href="https://dl.acm.org/doi/abs/10.1145/3442188.3445922"stochastic parrots/a./pp class="article-plus-content--header" style="text-align: center;"___/pp class="article-plus-content--header" style="text-align: center;"Information seeking is more than simply getting answers as quickly as possible/pp class="article-plus-content--header" style="text-align: center;"___/ppSecond, the fantasy idea of an all-knowing computer rests on a fundamentally flawed notion of how knowledge works. There will never be an all-inclusive fully correct set of information that represents everything we could need to know. And even if you might hope that could come to pass, it should be very clear that today’s World Wide Web isn’t it. When people seek information, we might think we have a question and we are looking for the answer, but more often than not, we benefit more from engaging in sense-making: refining our question, looking at possible answers, understanding the sources those answers come from and what perspectives they represent, etc. Consider the difference between the queries: “What is 70 degrees Fahrenheit in Celcius?” and “Given current COVID conditions and my own risk factors, what precautions should I be taking?”/ppInformation seeking is more than simply getting answers as quickly as possible. Sure, many of our questions call for simple, fact-based responses, but others require some investigation. For those situations, it is important that we get to see the relevant sources and the provenance of information. While this requires more effort on the user end, there are important cognitive and affective processes that happen during that process that allow us to better understand our own needs and the context, as well as provide a better assessment of the information being sought and collected before we use it. We wrote about these issues in our a href="http://chiragshah.org/papers/Shah_Bender_Situating_Search_CHIIR2022.pdf"Situating Search paper/a./pp class="article-plus-content--header" style="text-align: center;"___/pp class="article-plus-content--header" style="text-align: center;"It is urgent that we recognize that an overlay of apparent fluency does not, despite appearances, entail accuracy, informational value, or trustworthiness/pp class="article-plus-content--header" style="text-align: center;"___/ppChatGPT and other conversational systems that provide direct answers to one’s questions have two fundamental issues in this regard. First, these systems are generating answers directly, which skips the step of showing the users sources from where one could look for answers. Second, these systems are providing responses in conversational natural language, something we otherwise only experience with other humans: Over both evolutionary time and every individual’s lived experience, natural language to-and-fro has always been with fellow human beings. As we encounter synthetic language output, it is very difficult not to extend trust in the same way as we would with a human. We argue that systems need to be very carefully designed so as not to abuse this trust. /pp span class="article-content-box" a href="https://iai.tv/articles/the-artist-is-dead-ai-killed-them-auid-2275" class="iai-related-in-article click_on_suggestion_link--gtm-track" span class="iai-card" span class="iai-card--image" style="display: block;" img src="/assets/Uploads/_resampled/FillWyI0MDAiLCIyNzUiXQ/AI-art-and-creativity.webp" alt="AI art and creativity"/span span class="iai-card--content" style="display: block;" span class="iai-card--title" style="display: block;"SUGGESTED READING/span span class="iai-card--heading" style="display: block;"The artist is dead, AI killed them/span spanBy Henry Shevlin/span /span /span /a /span /ppSince the release of ChatGPT, we have seen widespread, breathless reports of what people have been able to use it to do and we are very concerned about how this technology is presented to the public. Even with non-conversational search engines, we know that is common to place undue trust in the results: if the search system places something at the top of the list, we tend to believe it is a good or true or representative result and if it doesn’t find something, it is tempting to believe it does not exist. But, as Safiya Noble warns us in emAlgorithms of Oppression/em, these platforms aren’t neutral reflections of either the world as it is or how people talk about the world, but rather shaped by various corporate interests. It is urgent that we as a public learn to conceptualize the workings of information access systems and, in this moment especially, that we recognize that an overlay of apparent fluency does not, despite appearances, entail accuracy, informational value, or trustworthiness./p