An #LLM with all the smoke and mirrors of statistical and analytic processing of the best of them, but based solely on the complete corpus of Shakespeare’s writings.
Ask any question and its reply will be in his words.
I'd been writing a post for #weblogpomo2024 talking about some of the more comical fuck-ups all of these #ai and #llm have been spewing. And now I'm fucking furious.
Note: content warning for depression, self-harm, and suicide
There's an economic curse on Large Language Models — the crappiest ones will be the most widely used ones.
The highest-quality models are exponentially more expensive to run, and currently are too slow for instant answers or processing large amounts of data.
Only the older/smaller/cut-down models are cheap enough to run at scale, so the biggest deployments are also the sloppiest ones.
I have an #AI article writing tool that makes about 20 different API calls. Most of them are for generation but several of them use the #LLM for reasoning tasks. For example matching keywords to the article headings it would be most appropriate to write about them under, then returning a JSON.
I'm only a hobbyist but I'd say a couple of the prompts are pretty complex.
i’m very excited about the interpretability work that #anthropic has been doing with #LLMs.
in this paper, they used classical machine learning algorithms to discover concepts. if a concept like “golden gate bridge” is present in the text, then they discover the associated pattern of neuron activations.
this means that you can monitor LLM responses for concepts and behaviors, like “illicit behavior” or “fart jokes”
so now we have a way to interpret and query #LLM responses in a structured format, as well as a control mechanism for driving LLM behavior
this is great news
Bruce Schneier wrote that prompt injection boils down to the fact that data and code pass through the same channel. with this interpretability work, we’re seeing the beginnings of a control channel separated from the data channel — you can control LLM behavior in a way that you can’t override via the data channel
Neue Folge im #TheDinerPodcast: Gesellschaft und KI: Wir sind völlig undvorbereitet auf die #Krise in der wir schon stecken. Im Gespräch mit @peterpur über generative #KI, Gesetzgebung und #Lobbying, was wir in der Schule lernen sollten, Lügen in Krisen, #Ästhetik und die stete Frage danach, was wir als #Gesellschaft überhaupt wollen. #AI#AIAct
Llama.cpp now supports the distributed inference, meaning you can use multiple computers to speed up the response time! Network is the main bottleneck, so all machines need to be hard wired, not connected through wifi. ##LLm#AI#MLhttps://github.com/ggerganov/llama.cpp/tree/master/examples/rpc
At PyCon Italia 2024 Ines Montani is presenting her talk "The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs" 🐍
Tl;Dr AI suggested adding glue to pizza to make the cheese stick. Sourced from 11 year old reddit post.
These are all good fun to mock until someone actually gets hurt taking these responses literally.
I'm torn. I've thrown my share of shade at #LLM s and the rush to shove "AI" into everything, and even what they do well, one can argue if it's worth the cost.
vocês estão usando #llm? para quem basta um gpt-3, qual a melhor solução, no sentido ético e confiabilidade? estava usando o aria do opera, mas descobri que tem um mundo de opções por aí? mistral, por exemplo, é confiável?
Reading about Ubuntu and nvidia’s LLM development collaboration, it seems like none of the features will be forced on end users via software updates. It seems like an opt-in situation, for which I’m thankful. As Microsoft and other companies are going about LLM integration wrong. Forcing users to test unsafe software is a horrible strategy.