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.
Absolutely unbelievable but here we are. #Slack by default using messages, files etc for building and training #LLM models, enabled by default and opting out requires a manual email from the workspace owner.
When you choose to use an #ML#LLM foundation model, you accept the risk management decisions made by the vendor without your input. Wonder what they are? Read this #MLsec paper from #IEEE computer.
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.
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
If you pick up one of the #Nvidia Orin boards, definitely get an SSD to go along with it. While it can run off an SD card, you’re going to run out of space quickly, and you’ll see a performance hit on complex tasks (like running a local #LLM). #EdgeAI#ai
I just finished a productive Copilot session on a complex programming task. I came up with much of the algorithms, and wrote a lot of the code, and had to guide it a lot throughout, but credit where due, Copilot did make small but meaningful contributions along the way.
Overall, not a pair programmer but someone useful to talk to when WFH alone on complex tasks.
Enough for Copilot to earn a ✋🏽. And I like how it responded to that. It has got that part down. 😉
I guess making money somewhat honestly by having customers that actually pay for a service with at least some guarantees of privacy and safety is not as lucrative as having an open platform network where people are tricked into giving out all their data while they are spied upon for whatever reasons.
#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. 🤔
Someone actually built a Chrome extension to "Hide annoying Google AI Overviews". LOL. Right now it only has 2,000 users, but I wouldn't be surprised if it reaches millions in a few months when SGE (Search Generative Experience) will be rolled out globally. https://chromewebstore.google.com/detail/hide-google-ai-overviews/neibhohkbmfjninidnaoacabkjonbahn Duckduckgo is also releasing similar tech using AI .
@nixCraft it would be better to prevent the LLM results from being generated altogether. That would not just save us from the #LLM crap but also prevent the #CO2 emissions being created at all (maybe).
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.
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.