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..

On the surface, ChatGPT might seem like a tool that can come in useful for an array of work tasks. But before you ask the chatbot to summarize important memos or check your work for errors, it's worth remembering that anything you share with ChatGPT could be used to train the system and perhaps even pop up in its responses to other users. That's something several Samsung employees probably should have been aware of before they reportedly shared confidential information with the chatbot. Soon after Samsung's semiconductor division started allowing engineers to use ChatGPT, workers leaked secret info to it on at least three occasions, according to The Economist Korea (as spotted by Mashable). One employee reportedly asked the chatbot to check sensitive database source code for errors, another solicited code optimization and a third fed a recorded meeting into ChatGPT and asked it to generate minutes.

Over the past few months, AI chatbots like ChatGPT have captured the world's attention due to their ability to converse in a human-like way on just about any subject. But they come with a serious drawback: They can present convincing false information easily, making them unreliable sources of factual information and potential sources of defamation. Why do AI chatbots make things up, and will we ever be able to fully trust their output? We asked several experts and dug into how these AI models work to find the answers. AI chatbots such as OpenAI's ChatGPT rely on a type of AI called a « large language model » (LLM) to generate their responses.

By virtue of their medical training, doctors have a wealth of knowledge, experience, wisdom and judgment. Yet even the greatest of human brains can't remember or interpret a tiny fraction of the information now available on human health and disease. Just a few years ago, most medical decisions were based entirely on the knowledge in the head of the doctor at the time the decision was made. Today that is beginning to change, thanks to the rapid development of artificial intelligence. The evolution that brought the world ChatGPT and similar large language models is making AI one of the most quickly adopted technologies in history, promising profound changes for the way we live and work.

The tech world's hottest new toy may find itself in legal hot water as AI's tendency to invent news articles and events comes up against defamation laws. Can an AI model like ChatGPT even commit libel? Like so much surrounding the technology, it's unknown and unprecedented -- but upcoming legal challenges may change that. Defamation is broadly defined as publishing or saying damaging and untrue statements about someone. It's complex and nuanced legal territory that also differs widely across jurisdictions: a libel case in the U.S. is very different from one in the U.K., or in Australia -- the venue for today's drama.

The past few weeks have seen major AI announcements by Microsoft, OpenAI, Google, and other organizations. Tech companies are scrambling to solidify their position in the fast-expanding market for large language models (LLM) and generative AI. And as big tech continues to pour more money into the field, competition is gradually becoming polarized between Microsoft and Google. So far, Microsoft has proven to be craftier and more capable in getting LLMs generative machine learning models to work in its products. But the race is not over, and we might yet see Google (or some other company) take the lead.

Samsung employees have accidentally leaked confidential information while using ChatGPT, a language model that is designed to assist users in generating text. Specifically, the semiconductor division of Samsung allowed its engineers to use ChatGPT to check source code. However, according to a report by The Economist Korea, there have been at least three instances where employees unintentionally shared sensitive information with ChatGPT. Also read: You won't believe what NASA's Webb Space Telescope captured on Uranus! In one instance, an employee pasted confidential source code into the chat to check for errors. In another instance, an employee shared code with ChatGPT and requested optimization.

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Let me exemplify a picture. What if you stumble upon a collection of 2000 GPT-4 prompts, categorized for every niche, query, and objection? I bet life would be a hell lotta easier! That day, while researching longer hours to complete punishing tasks, I thought the same. Fortunately, those intuitive thoughts were solution-oriented and not the other way around.

We all know ChatGPT gets stuff wrong. While that can be amusing, it's less funny if ChatGPT is mistakenly identifies you as a criminal. And it's less funny still if you were in fact the person who originally uncovered the crime in question. Indeed, you might find it so unfunny, you decide to sue for defamation. Which is exactly what Brian Hood, a Melbourne Australia-based politician is doing.