Whatever happened to the "old school" type of ML, the kind that IIRC is called "discriminative AI"? Has generative nonsense choked it out?
Analysis
This Reddit post from r/learnmachinelearning highlights a concern about the perceived shift in focus within the machine learning community. The author questions whether the current hype surrounding generative AI models has overshadowed the importance and continued development of traditional discriminative models. They provide examples of discriminative models, such as predicting house prices or assessing heart attack risk, to illustrate their point. The post reflects a sentiment that the practical applications and established value of discriminative AI might be getting neglected amidst the excitement surrounding newer generative techniques. It raises a valid point about the need to maintain a balanced perspective and continue investing in both types of machine learning approaches.
Key Takeaways
- •Generative AI is currently a hot topic, potentially overshadowing other ML approaches.
- •Discriminative AI models still have significant practical value and applications.
- •A balanced approach is needed, continuing research and development in both generative and discriminative AI.
“I'm referring to the old kind of machine learning that for example learned to predict what house prices should be given a bunch of factors or how likely somebody is to have a heart attack in the future based on their medical history.”