Answers.Body: <p>What you described end-to-end training is mainly due to <a href="https://en.wikipedia.org/wiki/Feature_learning" rel="nofollow noreferrer">feature learning</a> with modern deep learning architectures.</p>
  <blockquote>
  <p>In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data... The hierarchical architecture of the biological neural system inspires deep learning architectures for feature learning by stacking multiple layers of learning nodes... The input at the bottom layer is raw data, and the output of the final, highest layer is the final low-dimensional feature or representation.</p>
  </blockquote>
  <p>So in the sense of end-to-end deep feature learning, you're right that the focus on classification and regression as classical discriminative ML models cannot cover AI well at all compared to modern deep generative models, as discussed in yesterday's <a href="https://ai.stackexchange.com/questions/46238/machine-learning-vs-deep-learning-in-context-of-generative-ai-vs-discrimative-a/">post</a>. And of course these (deep) feature learning 'black boxes' will play a more and more important role in AGI such as <a href="https://arxiv.org/abs/2206.06488" rel="nofollow noreferrer">multimodal learning with transformer</a> which can be seen as a step further towards AGI.</p>
  
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Answers.UserDisplayName: cinch
Body: <p>I just think that pure classification and regression problems cannot describe artificial intelligence well at all.</p>
  <p>Artificial neural networks are trained in an end-to-end manner, and the trained model is a black box. Why don’t people use the same idea to study general artificial intelligence? That is, using an end-to-end approach to study general artificial intelligence instead of splitting a large problem into small problems (classification, regression, and others).</p>
  
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Comments.CreationDate: 2024-07-16T12:51:04.420Z
Comments.Text: You're asking 2 distinct questions here. The question in the title and the question "Why don't people end-to-end training in AGI?". These questions are quite different. Can you please remove one of the questions and ask the other in a separate post? If they are really related or the same question, can you clarify that?
Comments.UserDisplayName: nbro
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Comments.CreationDate: 2024-07-16T21:49:44.757Z
Comments.Text: @nbro In my opinion, classification and regression are about breaking down big problems (AGI) into small problems to solve, just like how people used to use manual feature extraction for image classification. Breaking down big problems into small problems to solve and end-to-end are opposites, so I think they can be discussed together.
Comments.UserDisplayName: huang
Tags: <agi>
Title: Why popular AI research focus on classification and regression?
UserDisplayName: huang
ThreadText: # Why popular AI research focus on classification and regression?
  
  (Asked by: username_0 on 2024-07-16)
  
  I just think that pure classification and regression problems cannot describe artificial intelligence well at all.
  
  Artificial neural networks are trained in an end-to-end manner, and the trained model is a black box. Why don’t people use the same idea to study general artificial intelligence? That is, using an end-to-end approach to study general artificial intelligence instead of splitting a large problem into small problems (classification, regression, and others).
  
  **Tags:** agi
  
  > **Comments:**
  > **username_1**: You're asking 2 distinct questions here. The question in the title and the question "Why don't people end-to-end training in AGI?". These questions are quite different. Can you please remove one of the questions and ask the other in a separate post? If they are really related or the same question, can you clarify that?  
  > **username_0**: @username_1 In my opinion, classification and regression are about breaking down big problems (AGI) into small problems to solve, just like how people used to use manual feature extraction for image classification. Breaking down big problems into small problems to solve and end-to-end are opposites, so I think they can be discussed together.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-07-16. Score: 1
  
  What you described end-to-end training is mainly due to [feature learning](https://en.wikipedia.org/wiki/Feature_learning) with modern deep learning architectures.
  
  > In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data... The hierarchical architecture of the biological neural system inspires deep learning architectures for feature learning by stacking multiple layers of learning nodes... The input at the bottom layer is raw data, and the output of the final, highest layer is the final low-dimensional feature or representation.
  
  So in the sense of end-to-end deep feature learning, you're right that the focus on classification and regression as classical discriminative ML models cannot cover AI well at all compared to modern deep generative models, as discussed in yesterday's [post](https://ai.stackexchange.com/questions/46238/machine-learning-vs-deep-learning-in-context-of-generative-ai-vs-discrimative-a/). And of course these (deep) feature learning 'black boxes' will play a more and more important role in AGI such as [multimodal learning with transformer](https://arxiv.org/abs/2206.06488) which can be seen as a step further towards AGI.
  
  
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