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!”
AI has the same problem that I saw ten year ago at IBM. And remember that IBM has been at this AI game for a very long time. Much longer than OpenAI or any of the new kids on the block. All of the shit we’re seeing today? Anyone who worked on or near Watson saw or experienced the same problems long ago.
We wonder often if what is created by AI has any value, and at what cost to artists and creators. These are important considerations. But we need to also wonder what AI is taking from what has already been created.
Robin takes a fair and balanced look at the ethics of using large language models.
LLMs are good at transforming text into less text
Laurie is really onto something with this:
This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.
Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.
A shame that this must-read book won’t be out in time for Christmas—’twould make a great stocking filler for a lot of people I know.
A smart, incisive look at the technologies sold as artificial intelligence, the drawbacks and pitfalls of technology sold under this banner, and why it’s crucial to recognize the many ways in which AI hype covers for a small set of power-hungry actors at work and in the world.
I listened to a day of talks on AI at UX Brighton, and I came away disappointed by what wasn’t mentioned.
Using generative large-language model tools? Sleeping well at night?
I object.
Thinking about priorities at UX Brighton.
Pest control for your website.