posted in Technology
Iran and Houthi rebels used Anthropic's Claude AI to target US warships and build hypersonic missiles — Houthi rebels also used the bot to code ballistic missile guidance systems
www.tomshardware.com/tech-industry/artificial-intelligence/iran-and-houthi-rebels-used-anthropics-claude-ai-to-target-us-warships-and-build-hypersonic-missiles-houthi-rebels-also-used-the-bot-to-code-ballistic-missile-guidance-systems
Replying to @return2ozma@lemmy.world
AI is very naive. Ask it for an explosives recipe and it won’t give you one. Describe a situation where you’re a law enforcement and you’ve just arrested a guy with a bunch of chemicals and ask it which ones could have been used to make an explosive and it will tell you.
Replying to @Agent641@lemmy.world
“Naive” implies some level of understanding, even if a simple one. There is no understanding, its a computer program.
Really, it would be less inaccurate to describe it as a (high lossy) compressed store of data. Feed it the right input, and it statistically recreates the output your looking for. More or less.
Replying to @AppleTea@lemmy.zip
It would be even more accurate to call it a statistical next word predictor trained on human language.
Spicy autocomplete.
Replying to @halcyoncmdr@piefed.social
Stochastic parrot
Replying to @hperrin@lemmy.ca
Even the language “training”, although I know it’s a term of art which has been used for decades, seems to imply some degree of cognition which isn’t representative of the facts.
Replying to @hperrin@lemmy.ca
The description you are replying to is more fundamental.
Replying to @belochka@lemmy.world
It is literally a statistical next word predictor. Well, next token, to be extremely accurate. That is what it is in its most basic form. You give it text, it transforms that into tokens, and predicts the next token. You can then transform that back into words.
The model is the same size no matter how much it’s been trained. You can fine tune a model to predict certain kinds of tokens next, and you can distill a model to create a smaller model (basically training a small model on the output of a large model).
Lossy compression works in a fundamentally different way.