@simon Hi Simon. On a loosely related topic, Bridgyfed is working now and you have fans over in Bluesky who would happily follow you there if you just hit follow on @bsky.brid.gy@bsky.brid.gy. I know you already have an account there, but this might be easier than cross-posting?
I've seen "critical AI" people saying generous things about the "carefully tailored and curated" datasets that were used to train statistical ML models five or ten years ago — in order to cast the monstrosity of GenAI into sharp relief.
This is quite different from what I was hearing about statistical ML in those quarters five years ago. Which gives me an idea for how GenAI will eventually gain broad acceptance!
@budgibson@tedunderwoodillinois Just wait for the next round of technical advances, at which point we'll start hearing things like:
"At least ChatGPT-era AI was modest and well integrated with human needs. These new AI Agents, on the other hand, raise frightening questions of legal liability."
I’m skeptical of “declining standards” stories. My theory is that smartphones/soc media have given young people a direct access to the world that my generation never had. They see peers becoming influencers, boasting about internships, &c. School is not the only place to put aspiration. + https://www.harvardmagazine.com/node/85660
@WenyiShang@tedunderwoodillinois Interesting! Yes, it was a somewhat monastic experience, especially because small colleges in the US are often in remote locations.
But I also really think students today are better informed.
@TedUnderwood Hi, I hope you’re well. I was wondering if you have any thoughts on detecting AI-generated text—is there any method for doing that that’s better than reading tea leaves? Could provide an interesting bonus angle to something else we’ve been working on.
@samuel_wade My understanding is that there's no silver bullet here. It's likely going to be possible to train a model that can detect a particular generative model in a particular social context. But generalizing that is very difficult, and solutions are fragile to any change in the system. So in general people recommend not putting much hope in this, I think.
It’s fine for the bridge to Bluesky to be opt-in. But however it’s constructed, I am looking forward to sharing some of the fine content here in a space where algorithmic discovery is easy and uncontroversial.
It was imo unfortunate for the cause of open social media that it got paired with a finical dogmatism inclined to throw roadblocks in the way of basic operations like searching content.
@ideaferace Yes, faceted and local has advantages and is okay with me.
But personally, I prefer a space where algorithmic discovery (and search) are just default norms. I’m glad I’ll be able to base myself there and connect to (parts of) the multiverse.
The new "memory" feature of ChatGPT is yet another example of OpenAI building a whole new feature on top of some relatively straight-forward prompt engineering. They've given ChatGPT a new tool called "bio" which can be used to persist little pieces of information about the user.
@simon It's so eerie that they're building this core function in natural language. I would have imagined it as a separate model trained to recognize snippets that ought to be persisted.
Draw Jam, on Youtube, made an interesting counter argument to the oft given argument from AI art users that #AIArt is making the creation art more accessible.
That's complete crap.
As he points out, you can get a complete free art education from the free art tutorials (some by your favorite artists) that already exist on Youtube and the web in general.
You just have to sit down and put the work and practice in.
@seinecle I don’t actually have first hand experience on this yet though I want to soon.
My understanding is that memory (VRAM or Apples unified memory) matters a lot. Also people sacrifice some quality by quantizing models to make them smaller.
Computer-assisted studies of fiction tend to count words and trace themes. It's harder to measure the things that really keep readers turning pages: mystery, suspense, and surprise.
If we got this method to work, the payoff might be that we could measure the creation of mystery, and distant-read things like the history of the cliffhanger. Here's a toy example with Conan Doyle's Hound of the Baskervilles.
I find that uncertainty is higher at the end of serial installments than at other chapter breaks. (The "tune in next week" effect.)
@Virginicus Well, you can compare it to the divergence within a book in the earlier figure. The variance between books’ mean difficulties is comparable to the variance between the hardest/easiest passages in any single book.
@Virginicus yes, although the range between outliers in Hound may also be partly just having more data points — I had to do four passes on it versus one in the other illustration