How it feels to get an AI email from a friend
My reaction to this surprised me: I was repelled
I imagine this is what it feels like when you’re on a phone call with someone and towards the end of the call you hear a distinct flushing sound.
Link tags: chat
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AI is not like you and me
AI is the most anthropomorphized technology in history, starting with the name—intelligence—and plenty of other words thrown around the field: learning, neural, vision, attention, bias, hallucination. These references only make sense to us because they are hallmarks of being human.
But ascribing human qualities to AI is not serving us well. Anthropomorphizing statistical models leads to confusion about what AI does well, what it does poorly, what form it should take, and our agency over all of the above.
There is something kind of pathological going on here. One of the most exciting advances in computer science ever achieved, with so many promising uses, and we can’t think beyond the most obvious, least useful application? What, because we want to see ourselves in this technology?
Meanwhile, we are under-investing in more precise, high-value applications of LLMs that treat generative A.I. models not as people but as tools.
Anthropomorphizing AI not only misleads, but suggests we are on equal footing with, even subservient to, this technology, and there’s nothing we can do about it.
Benjamin Parry~ Writing ~ Marking the homework of a twelve year old ~ @benjaminparry
Don’t get me wrong, there are some features under the mislabeled bracket of AI that have made a huge impact and improvement to my process. Audio transcription has been an absolute game-changer to research analysis, reimbursing me hours of time to focus on the deep thinking work. This is a perfect example of a problem seeking a solution, not the other way around. The latest wave of features feel a lot like because we can rather than we should, because.
A Coder Considers the Waning Days of the Craft | The New Yorker
GPT-4 is impressive, but a layperson can’t wield it the way a programmer can. I still feel secure in my profession. In fact, I feel somewhat more secure than before. As software gets easier to make, it’ll proliferate; programmers will be tasked with its design, its configuration, and its maintenance. And though I’ve always found the fiddly parts of programming the most calming, and the most essential, I’m not especially good at them. I’ve failed many classic coding interview tests of the kind you find at Big Tech companies. The thing I’m relatively good at is knowing what’s worth building, what users like, how to communicate both technically and humanely. A friend of mine has called this A.I. moment “the revenge of the so-so programmer.” As coding per se begins to matter less, maybe softer skills will shine.
Robots.txt - Jim Nielsen’s Blog
I realized why I hadn’t yet added any rules to my
robots.txt: I have zero faith in it.
Squish Meets Structure: Designing with Language Models
The slides and transcript from a great talk by Maggie Appleton, including this perfect description of the vibes we get from large language models:
It feels like they’re either geniuses playing dumb or dumb machines playing genius, but we don’t know which.
Making Large Language Models work for you
Another great talk from Simon that explains large language models in a hype-free way.
Documentation for GPTBot - OpenAI API
Now that the horse has bolted—and ransacked the web—you can shut the barn door:
To disallow GPTBot to access your site you can add the GPTBot to your site’s robots.txt:
User-agent: GPTBot Disallow: /
Catching up on the weird world of LLMs
This is a really clear, practical, level-headed explanatory talk from Simon. You can read the transcript or watch the video.
The LLMentalist Effect: how chat-based Large Language Models replicate the mechanisms of a psychic’s con
Taken together, these flaws make LLMs look less like an information technology and more like a modern mechanisation of the psychic hotline.
Delegating your decision-making, ranking, assessment, strategising, analysis, or any other form of reasoning to a chatbot becomes the functional equivalent to phoning a psychic for advice.
Imagine Google or a major tech company trying to fix their search engine by adding a psychic hotline to their front page? That’s what they’re doing with Bard.
In new AI hype frenzy, tech is applying the label to everything now
Today’s AI promoters are trying to have it both ways: They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks. But they also define AI so broadly as to include almost any large-scale, statistically-driven computer program.
Under this definition, everything from the Google search engine to the iPhone’s face-recognition unlocking tool to the Facebook newsfeed algorithm is already “AI-driven” — and has been for years.
Will GPT models choke on their own exhaust? | Light Blue Touchpaper
There’s a general consensus that large language models are going to get better and better. But what if this as good as it gets …before the snake eats its own tail?
The tails of the original content distribution disappear. Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions. We call this effect model collapse.
Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale.
Language Model Sketchbook, or Why I Hate Chatbots
Chat is rarely a suitable interface for most tasks. Here, Maggie Appleton explores and prototypes some alternatives.
Today’s AI is unreasonable - Anil Dash
Today’s highly-hyped generative AI systems (most famously OpenAI) are designed to generate bullshit by design. To be clear, bullshit can sometimes be useful, and even accidentally correct, but that doesn’t keep it from being bullshit. Worse, these systems are not meant to generate consistent bullshit — you can get different bullshit answers from the same prompts. You can put garbage in and get… bullshit out, but the same quality bullshit that you get from non-garbage inputs! And enthusiasts are current mistaking the fact that the bullshit is consistently wrapped in the same envelope as meaning that the bullshit inside is consistent, laundering the unreasonable-ness into appearing reasonable.
“Artificial Intelligence & Humanity,” an article by Dan Mall
AI is great anything quantity-related and bad and anything quality-related.
Sensible thinking from Dan here, that mirrors what we’re thinking at Clearleft.
In other words, it leans heavily on averages; the closer the training data matches an average, the higher degree of confidence that the result is more “correct,” or at least desirable.
The problem is that this is the polar opposite of what we consider creativity to be. Creativity isn’t about averages. It’s about the outliers, sometimes the one thing that’s different than all the rest.
ChatGPT is not ‘artificial intelligence.’ It’s theft. | America Magazine
But in calling these programs “artificial intelligence” we grant them a claim to authorship that is simply untrue. Each of those tokens used by programs like ChatGPT—the “language” in their “large language model”—represents a tiny, tiny piece of material that someone else created. And those authors are not credited for it, paid for it or asked permission for its use. In a sense, these machine-learning bots are actually the most advanced form of a chop shop: They steal material from creators (that is, they use it without permission), cut that material into parts so small that no one can trace them and then repurpose them to form new products.
Why Chatbots Are Not the Future by Amelia Wattenberger
Of course, users can learn over time what prompts work well and which don’t, but the burden to learn what works still lies with every single user. When it could instead be baked into the interface.
LukeW | Ask LukeW: New Ways into Web Content
I like how Luke is using a large language model to make a chat interface for his own content.
This is the exact opposite of how grifters are selling the benefits of machine learning (“Generate copious amounts of new content instantly!”) and instead builds on over twenty years of thoughtful human-made writing.
Welcome to the Artificial Intelligence Incident Database
The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.
We need to tell people ChatGPT will lie to them, not debate linguistics
There’s a time for linguistics, and there’s a time for grabbing the general public by the shoulders and shouting “It lies! The computer lies to you! Don’t trust anything it says!”