A former Amazon executive has accused the company of telling her to violate copyright law in order to compete with other tech giants in AI, reports Business Insider.
As part of a wider lawsuit against the company, in which Viviane Ghaderi claims she was discriminated against and ultimately fired for taking maternity leave, Ghaderi says she was told to “ignore legal advice and Amazon’s own policies to get better results” when developing its large-language models.
I think I'll settle on paying for Anthropic Claude 3 via their web interface (I'll check out the API access at some point too), and use PAYG API credits via Drafts for access to GPT 4. The GPT 4 selector in the API currently redirects to gpt-4-turbo.
In #homeassistant, using #nodered to make an API call to a #llamacpp server running #mistral 7B model. I create a prompt that asks it to summarize all the data in my house from the sensors. The results are pretty impressive for such a little model. Now I get a customized rundown, Jarvis style.
Useful? Probably not. But cool as hell. :cool_skelly:
Stack Overflow with all the explanation that's needed about why #LLMs are useless for any sort of learning:
Overall, because the average rate of getting correct answers from ChatGPT and other generative AI technologies is too low, the posting of answers created by ChatGPT and other generative AI technologies is substantially harmful to the site and to users who are asking questions and looking for correct answers.
The primary problem is that while the answers which ChatGPT and other generative AI technologies produce have a high rate of being incorrect, they typically look like the answers might be good and the answers are very easy to produce.
[…]
The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with significant subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.
I am trying to build a RAG with LLAMA 3 and... getting really crazy with the strange formats I get in the response....
Not only the response, but additional text, XML tags... #Llama3#LLM#RAG
Petition to preface all #LLM output with:
“The stories and information generated by this tool are probabilistic works of word salad and input bias. Only a techbro would take anything produced here as fact.”
I find that one great use for LLMs is for something a bit like rubber duck debugging, but for any topic.
You ask the LLM for its thoughts on a topic and respond. The LLM probably has no real insights to give you, since these things necessarily live in the world of cliché, but the process can help you to clarify your thoughts.
Then you can talk with a thoughtful and intelligent person to find the real errors in your thinking.
Amazingly constructive and positive answer by Phi 3 Mini 4K instruct #LLM running locally on an iPhone 14 (using Private LLM) when asked “What happened to Avicii”
“While we respectfully acknowledge the impact of an artist's work, focusing more on their contributions rather than their untimely demise helps honor their memory positively.”
My first troublesome hallucination with a #LLM in a while: #Claude3#Opus (200k context) insisting that I can configure my existing #Yubikey#GPG keys to work with PKINIT with #Kerberos and helping me for a couple of hours to try to do so — before realising that GPG keys aren't supported for this use case. Whoops.
No real bother other than some wasted time, but a bit painful and disappointing.
They use #OpenAI, which means my GitHub OSS has almost certainly been used in training data.
They rely on OpenAI's promise to not ingest any code that is used for "context".
They specifically do not disclaim that their tool could result in me violating someone else's copyright, and they could suggest the same code to someone else, too.
Uninstall this crap, now. It's dangerous and irresponsible
Back in 2018, Dario Amodei worked at OpenAI. And looking at one of its first A.I. models, he wondered: What would happen as you fed an artificial intelligence more and more data? He and his colleagues decided to study it, and they found that the A.I. didn’t just get better with more data; it got better exponentially.
I subscribed to Anthropic’s Claude 3 LLM yesterday to get access to their Opus model so I can try it out. It’s £18 per month.
I think LLMs will be useful as research assistants. Any output will need to be fact-checked and heavily edited before publication. People will still be needed who have knowledge of the topic that #LLM is generating text about. But in areas you have existing knowledge, they are a good way to get a high level text to use as a staring point that you can hammer into shape.
The internet is in decay [due to AI]. According to my guest today, Nilay Patel, this isn’t just a blip, as the big platforms figure out how to manage this. He believes that A.I. content will break the internet as we know it.
I've been trialling GitHub Copilot recently at work and, having been generally skeptical of the golden mountains promised by AI hype guys, I have to say that it gave me a modest efficiency gain in some scenarios. I would miss not having it, much like I would miss not having autocomplete.
I'll probably write up a blog for hgrsd.nl with a few thoughts of where it was helpful for me.
i low key don't want to see a big jump in #LLM or #AI capabilities anytime soon. rn they're capable enough that my mom wants to use them, but bad enough that even she has an intuitive sense for when they're wrong
that's how you build "AIQ", the skill of using it. Lots of people toying with them, to feel out their capabilities and limitations
Im as anti-"AI" as the next person, but I think its important to keep in mind the larger strategic picture of "AI" w.r.t. #search when it comes to #DuckDuckGo - both have the problem of inaccurate information, mining the commons, etc. But Google's use of LLMs in search is specifically a bid to cut the rest of the internet out of information retrieval and treat it merely as a source of training data - replacing traditional search with #LLM search. That includes a whole ecosystem of surveillance and enclosure of information systems including assistants, chrome, android, google drive/docs/et al, and other vectors.
DuckDuckGo simply doesnt have the same market position to do that, and their system is set up as just an allegedly privacy preserving proxy. So while I think more new search engines are good and healthy, and LLM search is bad and doesnt work, I think we should keep the bigger picture in mind to avoid being reactionary, and I dont think the mere presence of LLM search is a good reason to stop using it.
VOLlama v0.1.0, an open-source, accessible chat client for OLlama
Unfortunately, many user interfaces for open source large language models are either inaccessible or annoying to use with screen readers, so I decided to make one for myself and others. Non screen reder users are welcome to use it as well.
I hope that ML UI libraries like Streamlit and Gradio will become more friendly with screen readers in the future, so making apps like this is not necessary! #LLM#AI#ML https://chigkim.github.io/VOLlama/
'Librarian Andrew Gray has made a “very surprising” discovery. He analyzed five million scientific studies published last year and detected a sudden rise in the use of certain words, such as meticulously (up 137%), intricate (117%), commendable (83%) and meticulous (59%). [...] The explanation for this rise: tens of thousands of researchers are using [...] LLMs tools to write their studies or at least “polish” them.'