Answers.Body: <p>It's hard nowadays to draw a distinct line between the two with the advent of conditional generation and normalizing flow</p>
  <p>Usually, we say that is generative if you try to model the joint probability <span class="math-container">$p(x,y)$</span>, whereas discriminative models try to model the marginal <span class="math-container">$p(y|x)$</span></p>
  <p>However, generative model aims at sampling from such learned distribution, where usually instead discriminative models are only interested in some statistics of it (such as the mode)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-06-19T07:29:00.897Z
Answers.Text: I would lead with and emphasize *sampling*, a lot of sequence generators are predictive models of distribution, plus a separate sampling routine
Answers.UserDisplayName: Neil Slater
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Answers.CreationDate: 2024-06-19T10:13:57.843Z
Answers.Text: @NeilSlater yup, though they start from the hypothesis of modelling $p(x)$ and they factor it as $\prod p(x_i | x_{j<i})$, and so it falls back to the usual classification problem
Answers.UserDisplayName: Alberto
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Answers.CreationDate: 2024-06-18T22:17:49.737Z
Answers.LastActivityDate: 2024-06-18T22:17:49.737Z
Answers.UserDisplayName: Alberto
Body: <p>How does one tell if a given model is generative AI or predictive AI?</p>
  <p>Do generative models have more outputs than inputs and <em>vice versa</em> for predictive models?</p>
  
Tags: <terminology><definitions><generative-model><prediction><predictive-model>
Title: How to tell if a model is generative vs. predictive?
UserDisplayName: Geremia
ThreadText: # How to tell if a model is generative vs. predictive?
  
  (Asked by: username_0 on 2024-06-18)
  
  How does one tell if a given model is generative AI or predictive AI?
  
  Do generative models have more outputs than inputs and *vice versa* for predictive models?
  
  **Tags:** terminology, definitions, generative-model, prediction, predictive-model
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-06-18. Score: 2
  
  It's hard nowadays to draw a distinct line between the two with the advent of conditional generation and normalizing flow
  
  Usually, we say that is generative if you try to model the joint probability $p(x,y)$, whereas discriminative models try to model the marginal $p(y|x)$
  
  However, generative model aims at sampling from such learned distribution, where usually instead discriminative models are only interested in some statistics of it (such as the mode)
  
  > **Comments:**
  > **username_2**: I would lead with and emphasize *sampling*, a lot of sequence generators are predictive models of distribution, plus a separate sampling routine  
  > **username_1**: @NeilSlater yup, though they start from the hypothesis of modelling $p(x)$ and they factor it as $\prod p(x_i | x_{j<i})$, and so it falls back to the usual classification problem  
  
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