I think it’ll make a comeback eventually. LLMs will get progressively less useful as a replacement as its’ training data stales. Without refreshed data it’s going to be just as irrelevant as the years go on. Where will it get data about new programming languages or solutions to problems in new software? LLM knowledge will be stuck in 2025 unless new training material is given to it.
Your analogy simply does not hold here. If you’re having an AI train itself to play chess, then you have adversarial reinforcement learning. The AI plays itself (or another model), and reward metrics tell it how well it’s doing. Chess has the following:
A very limited set of clearly defined, rigid rules.
One single end objective: put the other king in checkmate before yours is or, if you can’t, go for a draw.
Reasonable metrics for how you’re doing and an ability to reasonably predict how you’ll be doing later.
Here’s where generative AI is different: when you’re doing adversarial training with a generative deep learning model, you want one model to be a generator and the other to be a classifier. The classifier should be given some amount of human-made material and some amount of generator-made material and try to distinguish it. The classifier’s goal is to be correct, and the generator’s goal is for the classifier to pick completely randomly (i.e. it just picks on a coin flip). As you train, you gradually get both to be very, very good at their jobs. But you have to have human-made material to train the classifier, and if the classifier doesn’t improve, then the generator never does either.
Imagine teaching a 2nd grader the difference between a horse and a zebra having never shown them either before, and you hold up pictures asking if they contain a horse or a zebra. Except the entire time you just keep holding up pictures of zebras and expecting the child to learn what a horse looks like. That’s what you’re describing for the classifier.
I think it’ll make a comeback eventually. LLMs will get progressively less useful as a replacement as its’ training data stales. Without refreshed data it’s going to be just as irrelevant as the years go on. Where will it get data about new programming languages or solutions to problems in new software? LLM knowledge will be stuck in 2025 unless new training material is given to it.
lmao. Ignorance is bliss is it.
Well. I doubt that very much. Take as an analogy the success of the chess AI which was left training itself - compared to being trained…
Your analogy simply does not hold here. If you’re having an AI train itself to play chess, then you have adversarial reinforcement learning. The AI plays itself (or another model), and reward metrics tell it how well it’s doing. Chess has the following:
Here’s where generative AI is different: when you’re doing adversarial training with a generative deep learning model, you want one model to be a generator and the other to be a classifier. The classifier should be given some amount of human-made material and some amount of generator-made material and try to distinguish it. The classifier’s goal is to be correct, and the generator’s goal is for the classifier to pick completely randomly (i.e. it just picks on a coin flip). As you train, you gradually get both to be very, very good at their jobs. But you have to have human-made material to train the classifier, and if the classifier doesn’t improve, then the generator never does either.
Imagine teaching a 2nd grader the difference between a horse and a zebra having never shown them either before, and you hold up pictures asking if they contain a horse or a zebra. Except the entire time you just keep holding up pictures of zebras and expecting the child to learn what a horse looks like. That’s what you’re describing for the classifier.