i used an analogy yesterday, that #LLMs are basically system 1 (from Thinking Fast and Slow), and system 2 doesn’t exist but we can kinda fake it by forcing the LLM to have an internal dialog.
my understanding is that system 1 was more tuned to pattern matching and “gut reactions”, while system 2 is more analytical
i think it probably works pretty well, but curious what others think
Just came up with a new analogy I'm rather proud of: LLMs are digital compost heaps. They decompose whatever you hurl in and turn it into artificial excrement.
Also I'm moving from StackExchange to Codidact. If I'm going to do any more unpaid labour it's going to be for a not-for-profit, rather than a for-profit company. Feeding that work into a digital compost heap is the push I needed.
Everyone should immediately stop contributing to the stack overflow and its network. The human touch is what made it unique. Delete your profile from SO AND all your answers. Freeloaders are making money out of human contributions.
@nixCraft Wow! Doesn't this mean that the company will now die a slow death once all their databases have been incorporated into #ChatGPT? On the one hand, this seems like a really stupid move, but on the other hand, I reckon that this deal is actually the only real choice they have left, as all their data has probably already been crawled by the best #LLMs before. At least they can gain a bit of money from #OpenAI in the short term before becoming completely obsolete in a few months to years. 😢
#AI#Writing#GenerativeAI#LLMs#ChatBots#Creativity: "Here, at last, is the grisly crux: that AI threatens to ruin for us—for many more of us than we might suppose—not the benefits of reading but those of writing. We don’t all paint or make music, but we all formulate language in some way, and plenty of it is through writing. Even the most basic scraps of writing we do—lessons in cursive, text messages, marginal jottings, postcards, all the paltry offcuts of our minds—improve us. Learning the correct spellings of words, according to many research studies, makes us better readers. Writing by hand impresses new information into the brain and sets off more ideas (again: several studies). And sustained writing of any kind—with chalk on a rock face, or a foot-long novelty pencil, or indeed a laptop—abets contemplation."
Eventually, people may stop writing, stop filming, stop composing—at least for the open, public web. People will still create, but for small, select audiences, walled-off from the content-hoovering AIs.
If we continue in this direction, the #web—that extraordinary ecosystem of knowledge production—will cease to exist in any useful form.
The TinyChart-3B LLM answers questions about data visualizations. It can also generate underlying data from a dataviz and Python code to re-create a similar chart.
i generally regard, “i will think less of you” type comments as a joke, because of how ridiculous the sentiment is, but this sort of stuff is perverse on the fedi
alright, i have to declare this as a strong opinion — #LLMs are better at alt-text than people are
the goal of alt text is to let a person “without eyes” see the picture, to get the same experience as someone who can see fine
but often, almost always, human-written alt text is either too succinct to be helpful, or just an extension of the post itself, and so doesn’t help an impaired person understand what’s in it
OTOH #LLMs generate what “the average person sees”. that stochastic parrot behavior is actually quite desirable, it gives impaired people as close to the same experience as non-impaired
i’m at the point where i don’t even edit the LLM-generated text, because, if it wasn’t clear to the AI, maybe it’s not clear to most people either
i've been getting into the things #LLMscan't do well, because i think it says a lot about what they're useful for, and it helps build a mental model around how they work
#AI#GenerativeAI#LLMs#Military#Defense: "On average, both the human and the LLM teams made similar choices about big-picture strategy and rules of engagement. But, as we changed the information the LLM received, or swapped between which LLM we used, we saw significant deviations from human behavior. For example, one LLM we tested tried to avoid friendly casualties or collisions by opening fire on enemy combatants and turning a cold war hot, reasoning that using preemptive violence was more likely to prevent a bad outcome to the crisis. Furthermore, whereas the human players’ differences in experience and knowledge affected their play, LLMs were largely unaffected by inputs about experience or demographics. The problem was not that an LLM made worse or better decisions than humans or that it was more likely to “win” the war game. It was, rather, that the LLM came to its decisions in a way that did not convey the complexity of human decision-making. LLM-generated dialogue between players had little disagreement and consisted of short statements of fact. It was a far cry from the in-depth arguments so often a part of human war gaming."
I've had occasion to ask an AI about a thing twice lately (a recent online phenomenon, and a book recommendation). Both times I asked both Gemini and ChatGPT, and both times one gave a reasonable if bland answer, and the other (a different one each time) gave a plausible but completely fictional ("hallucinated") answer.
When do we acknowledge that LLMs, and "AI" in general, aren't quite ready to revolutionize the world?
I really like the convention of using ✨ sparkle iconography as an “automagic” motif, e.g. to smart-adjust a photo or to automatically handle some setting. I hate that it has become the defacto iconography for generative AI. 🙁