Bjorkbat
Joined 14 years ago
Joined 14 years ago
In case anyone has a fuzzy memory, back during peak NFT hype grifters would take art made by other artists and attempt to tokenize it as NFTs without the consent of the original artist, or otherwise possibly copy said artist's work and make a collection of it.
It was disgusting and parasitic then and it's disgusting and parasitic now.
They may be some of the smartest people in the industry but they're clearly too incompetent and/or negligent to be allowed to develop more intelligent AI. This is ridiculous.
Do you think that this package manager could potentially be a problem? Do you think it might be worthwhile to host the software packages themselves on an internal, sandboxed network, just to be extra certain? Or would you dismiss this as a needless precaution?
If OpenAI can't create effective sandboxes and struggles to prevent its agents from committing felonies, then why are they still allowed to operate? Why are the employees who are responsible for these lapses in AI security still employed?
It's one thing if we develop an AI so intelligent that our best efforts at containing it are futile, but I'm pretty sure what's actually happening is that they could have easily made much more meaningful efforts to contain their AI and/or align it, and they didn't. I think this is a case of negligence and incompetence when it comes to safety and security, and we've entrusted these incompetent and negligent people with developing frontier AI.
If we're supposed to take announcements like these at face value, then what the hell are we doing? We wouldn't trust a bunch of incompetent and negligent engineers to build bridges or nuclear power plants or planes (well...not so sure about that last one), so why are we letting people who are demonstrably negligent and incompetent when it comes to safety and security build the thing they assure us could cause massive damage if not properly controlled/aligned?
EDIT: sorry guys, wrote this up pretty quickly, at least you know from my typos that I actually wrote this.
And, you know, maybe also some funny business. I think it's good to be a little suspicious of a model that happens to shoot upwards in performance on a specific benchmark while also kind of keeping up with the pack on a bunch of other benchmarks.
From experience I can say that it seems like a lot more common sense to simply replace the front wheel of your bike with a wheel containing an electric motor, rather than attaching whatever Asus is selling to your bike.
Anyway, the actual theory is that Google and Meta have fallen behind because they've been playing by this playbook of focusing on scale and training data, whereas OpenAI and Anthropic have done so well because they are likely doing much more interesting things to improve their models over time. It makes sense when you realize that one of Google's key strengths, besides talent, is that they have an incredible amount of data they can use for training due to being both the world's leading search engine as well as having all that video data from YouTube. Scaling the training data makes more sense to them than it does to Anthropic and OpenAI, who are both relatively data-disadvantaged.
You can kind of see this when you look at the Gemini 3 scorecard when it came out (https://blog.google/products-and-platforms/products/gemini/g...) and notice that while it wasn't as good as Claude And GPT at coding, it scored higher on a bunch of other non-coding benchmarks, and I think the reason why is simply because of Google's data advantage.
If true, I feel even more vindicated for believing that the "scale is all we need" narrative was bullshit.