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. 🙁
>>> Do you happen to know what your context window length is?
Llama: I'm an AI model, and I don't have a fixed "context window" in the classical sense. My training data consists of a massive corpus of text, which I use to generate responses.
#AI#GenerativeAI#LLMs#OpenSource#Microsoft#WizardLM2: "Last week, Microsoft researchers released WizardLM 2, which it claimed is one of the most powerful open source large language models to date. Then it deleted the model from the internet a few hours later because, as The Information reported, it “accidentally missed” required “toxicity testing” before it was released.
However, as first spotted by Memetica, in the short hours before it was taken down, several people downloaded the model and reuploaded it to Github and Hugging Face, meaning that the model Microsoft thought was not ready for public consumption and had to be taken offline, has already spread far and wide, and now effectively can never be removed from the internet.
#ML#AI#GenerativeAI#LLMs#FoundationModels#PoliticalEconomy: "A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as ‘foundation models’, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response." https://journals.sagepub.com/doi/full/10.1177/20539517241247839
Like words, molecular sequences in biological components are tokens that can be manipulated by #LLMs:
“Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI.”
⚠️ @forrestbrazeal on the inside threat to OSS
🍴Vicki Boykis says Redis is forked
👻 @johnonolan says Ghost is federating
🦙 Meta Engineering announces Llama 3
❓ @eieio's questions to ask when you don't want to work
🎙 hosted by @jerod
Lots of people who work in #AI have, in their head, an idea about what sort of interaction with an #LLMmight give them pause. The thing that might make them start to suspect that something interesting is happening.
Here's mine:
User: Tell me a cat joke.
LLM: Why did the cat join a band? He wanted to be a purr-cussionist.
I was listing something on eBay, and they encourage starting with an existing listing—presumably to increase the amount of detail and decrease the amount of work.
When I selected the same model, I got a default description that was extremely robotic and wordy while just repeating the spec sheet. I thought it sounded LLM-generated; sure enough when I went to edit it, there is a big shiny “write with AI” button.
It makes EVERY listing sound identical, lifeless, and lacking critical context like the SPECIFIC condition of the item, why it’s being sold, etc. You get an online marketplace with descriptions masquerading as human-authored all sporting the same useless regurgitation of the structured spec sheet, in a less digestible format.
Companies, don’t do this.
I don’t actually mind some of the “summarize/distill customer reviews” type generative AI stuff!
But this is worse as it mixes machine-written nonsense with the corpus of human-written text. And from poking at a few other listings, everyone is just using this feature and its output as-is without actually adding anything. It’s not being used to improve the experience, it’s being used to replace the one critical human part of the experience.