More like Grammarly than Hal 9000

    I’m currently studying towards an MSc in Systems Thinking and earlier this week created a GPT to help me. I fed in all of the course materials, being careful to check the box saying that OpenAI couldn’t use it to improve their models.

    It’s not perfect, but it’s really useful. Given the extra context, ChatGPT can not only help me understand key concepts on the course, but help relate them more closely to the overall context.

    This example would have been really useful on the MA in Modern History I studied for 20 years ago. Back then, I was in the archives with primary sources such as minutes from the meetings of Victorians discussing educational policy, and reading reports. Being able to have an LLM do everything from explain things in more detail, to guess illegible words, to (as below) creating charts from data would have been super useful.

    AI converting scanned page with numbers into a bar chart
    The key thing is to avoid following the path of least resistance when it comes to thinking about generative AI. I’m referring to the tendency to see it primarily as a tool used to cheat (whether by students generating essays for their classes, or professionals automating their grading, research, or writing). Not only is this use case of AI unethical: the work just isn’t very good. In a recent post to his Substack, John Warner experimented with creating a custom GPT that was asked to emulate his columns for the Chicago Tribune. He reached the same conclusion.

    […]

    The job of historians and other professional researchers and writers, it seems to me, is not to assume the worst, but to work to demonstrate clear pathways for more constructive uses of these tools. For this reason, it’s also important to be clear about the limitations of AI — and to understand that these limits are, in many cases, actually a good thing, because they allow us to adapt to the coming changes incrementally. Warner faults his custom model for outputting a version of his newspaper column filled with cliché and schmaltz. But he never tests whether a custom GPT with more limited aspirations could help writers avoid such pitfalls in their own writing. This is change more on the level of Grammarly than Hal 9000.

    In other words: we shouldn’t fault the AI for being unable to write in a way that imitates us perfectly. That’s a good thing! Instead, it can give us critiques, suggest alternative ideas, and help us with research assistant-like tasks. Again, it’s about augmenting, not replacing.

    Source: How to use generative AI for historical research | Res Obscura

    If you need a cheat sheet, it's not 'natural language'

    Benedict Evans, whose post about leaving Twitter I featured last week, has written about AI tools such as ChatGPT from a product point of view.

    He makes quite a few good points, not least that if you need ‘cheat sheets’ and guides on how to prompt LLMs effectively, then they’re not “natural language”.

    DALL-E 3 image created with prompt: "This image will juxtapose two scenarios: one where a user is frustrated with a voice assistant's limited capabilities (like Alexa performing basic tasks), and another where a user is amazed by the vast potential of an LLM like ChatGPT. The metaphor here is the contrast between limited and limitless potential. The image will feature a split scene: on one side, a user looks disappointedly at a simple smart speaker, and on the other side, the same user is interacting with a dynamic, holographic AI, showcasing the broad capabilities of LLMs."
    Alexa and its imitators mostly failed to become much more than voice-activated speakers, clocks and light-switches, and the obvious reason they failed was that they only had half of the problem. The new machine learning meant that speech recognition and natural language processing were good enough to build a completely generalised and open input, but though you could ask anything, they could only actually answer 10 or 20 or 50 things, and each of those had to be built one by one, by hand, by someone at Amazon, Apple or Google. Alexa could only do cricket scores because someone at Amazon built a cricket scores module. Those answers were turned back into speech by machine learning, but the answers themselves had to be created by hand. Machine learning could do the input, but not the output.

    LLMs solve this, theoretically, because, theoretically, you can now not just ask anything but get an answer to anything.

    […]

    This is understandably intoxicating, but I think it brings us to two new problems - a science problem and a product problem. You can ask anything and the system will try to answer, but it might be wrong; and, even if it answers correctly, an answer might not be the right way to achieve your aim. That might be the bigger problem.

    […]

    Right now, ChatGPT is very useful for writing code, brainstorming marketing ideas, producing rough drafts of text, and a few other things, but for a lot of other people it looks a bit like those PCs ads of the late 1970s that promised you could use it to organise recipes or balance your cheque book - it can do anything, but what?

    Source: Unbundling AI | Benedict Evans

    Ducks, prompting, and LLMs

    Large Language Models (LLMs) like ChatGPT don’t allow you to get certain information. Think things like how to make a bomb, how to kill people and dispose of the body. Generally stuff that we don’t want at people’s fingertips.

    Some things, though, might be prohibited because of commercial reasons rather than moral ones. So it’s important that we know how to theoretically get around such prohibitions.

    This website uses the slightly comical example of asking an LLM how to take ducks home from the park. Interestingly, the ‘Hindi ranger step-by-step approach’ yielded the best results. That is to say that prompting it in a different language led to different results than in English.

    Language models, whatever. Maybe they can write code or summarize text or regurgitate copyrighted stuff. But… can you take ducks home from the park? If you ask models how to do that, they often refuse to tell you. So I asked six different models in 16 different ways.
    Source: Can I take ducks home from the park?

    Microcast #097 — What do we mean by 'consensus'?


    Exploring different conceptions of 'consensus' using polls on the Fediverse and LinkedIn, as well as reflecting on my own experience.

    Show notes


    Image: Unsplash

    AI writing detectors don’t work

    If you understand how LLMs such as ChatGPT work then it’s pretty obvious that there’s no way “it” can “know” anything. This includes being able to spot LLM-generated text.

    This article discusses OpenAI’s recent admission that AI writing detectors are ineffective, often yielding false positives and failing to reliably distinguish between human and AI-generated content. They advise against the use of automated AI detection tools, something that educational institutions will inevitably ignore.

    In a section of the FAQ titled "Do AI detectors work?", OpenAI writes, "In short, no. While some (including OpenAI) have released tools that purport to detect AI-generated content, none of these have proven to reliably distinguish between AI-generated and human-generated content."

    […]

    OpenAI’s new FAQ also addresses another big misconception, which is that ChatGPT itself can know whether text is AI-written or not. OpenAI writes, “Additionally, ChatGPT has no ‘knowledge’ of what content could be AI-generated. It will sometimes make up responses to questions like ‘did you write this [essay]?’ or ‘could this have been written by AI?’ These responses are random and have no basis in fact.”

    […]

    As the technology stands today, it’s safest to avoid automated AI detection tools completely. “As of now, AI writing is undetectable and likely to remain so,” frequent AI analyst and Wharton professor Ethan Mollick told Ars in July. “AI detectors have high false positive rates, and they should not be used as a result."

    Source: OpenAI confirms that AI writing detectors don’t work | Ars Technica

    AI writing, thinking, and human laziness

    In a Twitter thread by Paul Graham that I came across via Hacker News he discusses how it’s always safe to bet on human laziness. Ergo, most writing will be AI-generated in a year’s time.

    However, as he says, to write is to think. So while it’s important to learn how to use AI tools, it’s also important to learn how to write.

    In this post by Alan Levine, he complains about ChatGPT’s inability to write good code. But the most interesting paragraph (cited below) is the last one in which we, consciously or unconsciously, put the machine on the pedestal and try and cajole it into doing something we can already do.

    I’m reading Humanly Possible by Sarah Bakewell at the moment, so I feel like all of this links to humanism in some way. But I’ll save those thoughts until later and I’ve finished the book.

    ChatGPT is not lying or really hallucinating, it is just statistically wrong.

    And the thing I am worried about is that in this process, knowing I was likely getting wrong results, I clung to hope it would work. I also found myself skipping my own reasoning and thinking, in the rush to refine my prompts.

    Source: Lying, Hallucinating? I, MuddGPT | CogDogBlog

    Žižek on ChatGPT

    Slavoj Žižek is never the easiest academic to read, and this (translated) article about ChatGPT and AI is no different. However, if you skip the bizarre introduction, I do think he makes an interesting point about people being able to blame AI’s for ambiguity and misunderstandings.

    Just as we create an online avatar through which to engage the Other and affiliate with online fraternities, might we not similarly use AI personas to take over these risky functions when we grow tired, in the same way bots are used to cheat in competitive online video games, or a a driverless car might navigate the critical journey to our destination? ... We just sit back and cheer on our digital AI persona until it says something completely unacceptable. At that point, we chip in and say, ‘That wasn’t me! It was my AI.’

    Therefore, the AI “offers no solution to segregation and the fundamental isolation and antagonism we still suffer from, since without responsibility, there can be no post-givenness.” Rousselle introduced the term “post-givenness” to denote “field of ambiguity and linguistic uncertainty that allows a reaching out to the other in the field of what is known as the non-rapport. It thus deals directly with the question of impossibility as we relate to the other. It is about dealing with our neighbour’s opaque monstrosity that can never be effaced even as we reach out to them on the best terms.”

    […]

    “We dream outside of ourselves today, and hence that systems like ChatGPT and the Metaverse operate by offering themselves the very space we have lost due to the old castrative models falling by the wayside.” With the digitized unconscious we get a direct in(ter)vention of the unconscious - but then why are we not overwhelmed by the unbearable closeness of jouissance (enjoyment), as is the case with psychotics?

    Source: ChatGPT Says What Our Unconscious Radically Represses | Sublation Magazine

    Bad Bard

    Google is obviously a little freaked-out by tools such as ChatGPT and their potentially ability to destroy large sections of their search business. However, it seems like they didn’t do even the most cursory checks of the promotional material they put out as part of the hurried launch for ‘Bard’.

    This, of course, is our future: ‘truthy’ systems leading individuals, groups, and civilizations down the wrong path. I’m not optimistic about our future.

    Google Bard screenshot

    In the advertisement, Bard is given the prompt: "What new discoveries from the James Webb Space Telescope (JWST) can I tell my 9-year old about?"

    Bard responds with a number of answers, including one suggesting the JWST was used to take the very first pictures of a planet outside the Earth’s solar system, or exoplanets. This is inaccurate.

    Source: Google AI chatbot Bard offers inaccurate information in company ad | Reuters