Video game support developer Keywords Studios tried to create a game solely using artificial intelligence but failed because the technology was "unable to replace talent".
Unless you specify that you want a talented output. A lot of people don’t realize that you need to tell AIs what kind of output you want them to give you, if you don’t then they’ll default to something average. That’s the cause of a lot of disappointment with tools like ChatGPT.
Unironically yes, sometimes. A lot of the best works which its training samples are based on cites the original poster’s qualifications, and this filters into the model where asking for the right qualifications directly can influence it to rely more on high quality input samples when generating its response.
But it’s still not perfect, obviously. It doesn’t make it stop hallucinating.
Yeah, you still need to give an AI’s output an editing and review pass, especially if factual accuracy is important. But though some may mock the term “prompt engineering” there really are a bunch of tactics you can use when talking to an AI to get it to do a much better job. The most amusing one I’ve come across is that some AIs will produce better results if you offer to tip them $100 for a good output, even though there’s no way to physically fulfill such a promise. The theory is that the AI’s training data tended to have better stuff associated with situations where people paid for it, so when you tell the AI you’re willing to pay it’ll effectively go “ah, the user is expecting good quality.”
You shouldn’t have to worry about the really quirky stuff like that unless you’re an AI power-user, but a simple request for high-quality output can go a long way. Assuming you want high quality output. You could also ask an AI for a “cheesy low-quality high-school essay riddled with malapropisms” on a subject, for example, and that would be a different sort of deviation from “average.”
Absolutely, it’s one of the first curious things you discover when using them, such as stable diffusion “masterpiece” or the famous system prompt leaks from proprietary llms
It makes sense in how it works but in proprietary use it is mostly handled for you
Finding the right words and amount is a hilarious exercise that provides pretty good insight in the attention mechanics
Consider the “let’s work step by step”
This proved a revolutionary way to system the coders as they then will structure the output better, there’s then more research that happened around why this is so amazingly effective at making the model proof check itself
Predictions are obviously closely related to the action part of our brains as well, so it makes sense that it would help when you think about it
ai automates the behavior of an average agent, not a talented one
Unless you specify that you want a talented output. A lot of people don’t realize that you need to tell AIs what kind of output you want them to give you, if you don’t then they’ll default to something average. That’s the cause of a lot of disappointment with tools like ChatGPT.
Ahhh so the secret to using ChatGPT successfully is to tell it to give you good output?
Like “make sure the code actually works” and “don’t repeat yourself like a fucking idiot” and “don’t hallucinate false information”!
Unironically yes, sometimes. A lot of the best works which its training samples are based on cites the original poster’s qualifications, and this filters into the model where asking for the right qualifications directly can influence it to rely more on high quality input samples when generating its response.
But it’s still not perfect, obviously. It doesn’t make it stop hallucinating.
Yeah, you still need to give an AI’s output an editing and review pass, especially if factual accuracy is important. But though some may mock the term “prompt engineering” there really are a bunch of tactics you can use when talking to an AI to get it to do a much better job. The most amusing one I’ve come across is that some AIs will produce better results if you offer to tip them $100 for a good output, even though there’s no way to physically fulfill such a promise. The theory is that the AI’s training data tended to have better stuff associated with situations where people paid for it, so when you tell the AI you’re willing to pay it’ll effectively go “ah, the user is expecting good quality.”
You shouldn’t have to worry about the really quirky stuff like that unless you’re an AI power-user, but a simple request for high-quality output can go a long way. Assuming you want high quality output. You could also ask an AI for a “cheesy low-quality high-school essay riddled with malapropisms” on a subject, for example, and that would be a different sort of deviation from “average.”
Absolutely, it’s one of the first curious things you discover when using them, such as stable diffusion “masterpiece” or the famous system prompt leaks from proprietary llms
It makes sense in how it works but in proprietary use it is mostly handled for you
Finding the right words and amount is a hilarious exercise that provides pretty good insight in the attention mechanics
Consider the “let’s work step by step”
This proved a revolutionary way to system the coders as they then will structure the output better, there’s then more research that happened around why this is so amazingly effective at making the model proof check itself
Predictions are obviously closely related to the action part of our brains as well, so it makes sense that it would help when you think about it
So I need to praise it and call it a good boy?
Interestingly enough, I’ve started adding something similar to my AI prompts because a Lemmy user had it in theirs and I liked their images.