The article is less awful than the title. In short, the thesis is that ML seems only able to learn associations, rather than stronger, causal models.
It should be fairly obvious that ‘curve fitting’ is a misleading category—these models are clearly learning highly meaningful latent spaces that no prior approaches ever did. But I would agree that the actual high-level ability to make causal inferences seems to be lacking.
Where I disagree with Pearl is simply with the idea that these stronger models won't emerge through future research. It's too early to say this, after barely a decade of large-scale AI research that has been undergoing continual rapid progress. Greater generality and more powerful models are some of the most well-established goals of the field.
It should be fairly obvious that ‘curve fitting’ is a misleading category—these models are clearly learning highly meaningful latent spaces that no prior approaches ever did. But I would agree that the actual high-level ability to make causal inferences seems to be lacking.
Where I disagree with Pearl is simply with the idea that these stronger models won't emerge through future research. It's too early to say this, after barely a decade of large-scale AI research that has been undergoing continual rapid progress. Greater generality and more powerful models are some of the most well-established goals of the field.