Has anyone written about how textual generative AI feels strangely close to toxic masculinity in some respects? The absolute confidence in everything stated, the lack of understanding of the consequences of getting that confidence wrong for important questions, the semi-gaslighty feeling when it “corrects” itself when you call it out on something. It so often feels like talking to someone one would despise and avoid in “real life.” I’m curious if anyone did some writing on this.
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 How about figuring out how to upgrade my code to eliminate out-of-date libraries?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.
So, I know generative AI is supposed to be just the most incorrect thing ever, but I want you to compare two descriptions. "A rock on a beach under a dark sky." And: The image shows a close-up view of a rocky, cratered surface, likely a planet or moon, with a small, irregularly shaped moon or asteroid in the foreground. The larger surface appears to be Mars, given its reddish-brown color and texture. The smaller object, which is gray and heavily cratered, is likely one of Mars' moons, possibly Phobos or Deimos. The background fades into the darkness of space. The first one is supposed to be the pure best thing that isn't AI. Right? Like, it's what we've been using for the past like 5 years. And yes, it's probably improved over those years. This is Apple's image description. It's, in my opinion, the best, most clear, and sounds like the ALT-text that it's made from, which people made BTW, and the images it was made with, which had to come from somewhere, were of very high quality, unlike Facebook and Google which just plopped anything and everything into theirs. The second was from Be My Eyes. Now, which one was more correct? Obviously, Be My Eyes. Granted, it's not always going to be, but goodness just because some image classification tech is old, doesn't mean it's better. And just because Google and Facebook call their image description bullshit AI, doesn't mean it's a large language model. Because at this point in time, Google TalkBack does not use Gemini, but uses the same thing VoiceOver has. And Facebook uses that too, just a classifier. Now, should sighted people be describing their pictures? Of course. Always. With care. And having their stupid bots use something better than "picture of cats." Because even a dumb image classifier can tell me that, and probably a bit more, lol. Cats sleeping on a blanket. Cats drinking water from a bowl. Stuff like that. But for something quick, easy, and that doesn't rely on other people, shoot yeah I'll put it through Be My Eyes. #accessibility#AI#LLM#BeMyEyes#blind
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
OpenAI seems to be in a bubble where they think they can do what they want without consequences. They had been trying to get Scarlet Johansson to agree to voice a ChatGPT bot for a year. She said no. They used a very similar voice anyway. Cue lawyers and weaselly backtracking. A must-read from Casey Newton. #OpenAI#LLMhttps://www.platformer.news/open-ai-scarlett-johansson-her-voice-sam-altman/
#OpenAI doing the thing they are building tools to facilitate everyone to do (imitate the likeness & creative output of real people)... is exactly the signal we should all be recognising it to be.
We've all been told that our ability to grind out work-value is what we exist for... but even that poor measure of our worth is fair game to those big enough to steal it.
So… Big Tech is allowed to blatantly steal the work, styles and therewith the job opportunities of thousands of artists and writers without being reprimanded, but it takes similarity to the voice of a famous actor to spark public outrage about AI. 🤔
It’s going to be released in-dev and few users will likely be able to even run Recall on their PC. However, this is overstepping boundaries between Microsoft and Users. A clever malicious user is going to find a way to use this against normal users. Given how slapdick LLM security is at the moment; I’d switch to Linux in a heartbeat before being subjected to Recall. 🤬
Just realized that Microsoft Recall is totally going to be sold as an enterprise product for spying on employees. MS has been looking high and low for a way to sell enterprises on AI subscriptions and this is it. They cracked the code. Fuck.
How would anyone trust the products these people put worth? They aren’t working on making LLMs more accurate (spoiler alert: they can’t, by design), they’re working to make them more appealing to companies targeting unsuspecting consumers. By any means necessary.