Given how many #writing contests, anthologies and magazines are currently struggling with a flood of #AI / #LLM -generated spam, have you heard about anyone trying to fight the problem by asking specifically for stories which AI cannot easily write?
Even the best models I've tried cannot easily use #solarpunk themes, symbols and structures - they always come out unnatural.
Are there any specific limitations, formats that can work similarly?
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" 🐍
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
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?
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.
FreeCodeCamp released today a new course for fine tuning LLM models. The course, by Krish Naik, focuses on different tuning methods such as QLORA, LORA, and Quantization using different models such as Llama2, Gradient, and Google Gemma model.
In my mind, the people most likely to use "AI" for things are the ones who sort of know what they want, but don't know how to get it.
So you ask for code to do something, and the LLM spits out something glommed together from Stack Overflow posts or Reddit. How do you know it does what you wanted? How do you debug it if it doesn't work?
If these actually worked, I'd love to select a hunk of code, and have something spit out basic unit tests, or a reasonable documentation outline. Or even check for logic or security errors. How about figuring out how to upgrade my code to eliminate out-of-date libraries?
My fantasy LLMs that actually do something useful are also not trained on data stolen from the Internet. And they don't use enough electricity to power a country, or evaporate a big city's water supply.
“Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer.”
My use case for LLMs is to see if it turns up any subtopic of interest that I haven’t included in an article I’m writing on a topic.
If it does, then I can research that subtopic to see if I should include it in the article. Which I then write myself. The LLM is a search assistant.
I can also see value in them as research assistants and guides for learning about new topics. With the proviso that nothing an LLM produces should be taken at face value.
The new book by Salman Khan, of Khan Academy fame, will be of interest to anyone interested in how chatbots will influence education. There is definitely a place for them as personalised learning tutors. Especially for learners who would have zero chance of getting a human tutor for one-to-one learning. #LLM#Education https://www.penguin.co.uk/books/460644/brave-new-words-by-khan-salman/9780241680964