Is there a fundamental difference between how a program like chatgp learns and how a human learns?
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With “machine learning” you’re asking the machine to make a prediction if something will happen based on certain conditions.
So say you have a bunch of statistics from bunch of basketball games. You want to see how well your model takes in a bunch of information about games in the past (who played, how many points they scored, rebounds, turnovers, etc) and whether or not it can accurately predict which team won those games. If it’s very good at predicting the outcomes of those games you could apply that to when it predicts which team will win in the future.
So first you take a portion of those games with the win-loss result and use that to “train” the model. It understands that when all 5 starters score 10+ points that team wins most of the time. Then you give the model the rest of the data without that win-loss result information and ask it to guess whether or not the team won. Then you compare its predictions with reality. If its a good match then, great. If not then you either need to improve the model or maybe find more data that can help refine things. If you have the box scores of a 100 games you give the model 20 of those games to train and then test on the remaining 80.
This cycle of training and testing your data is how the model “learns” to be more accurate when making a prediction. You give it some information, ask it to make a prediction based on what it knows, and then see how well it does.
ChatGPT is advanced since its doing more than statistical modeling but the idea is similar, it’s looking at a ton of information and trying to make a prediction of what you want not only because of what you asked but what everyone who has ever asked a similar question before has gotten for an answer.
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