You also dont need to be told that the bat is causing the sudden change in the balls direction.
Such inferences come to us humans intuitively.
But they struggle to make simple causal inferences like the ones we just saw in the baseball video above.
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is a term often used in machine learning.
The simplest example of i.i.d.
is flipping a coin or tossing a die.
domain by training the model on very large corpora of examples.
Lack of causal understanding makes it very hard to make predictions and deal with novel situations.
This is why you seeself-driving cars make weird and dangerous mistakeseven after having trained for millions of miles.
Generalizing well outside the i.i.d.
Causal models also allow humans to repurpose previously gained knowledge for new domains.
Causal learning
So, why has i.i.d.
remained the dominant form of machine learning despite its known weaknesses?
Pure observation-based approaches are scalable.
Continue the training until you reach the accuracy you require.
There are already many public datasets that provide such benchmarks, such as ImageNet, CIFAR-10, and MNIST.
But as the AI researchers observe in their paper, accurate predictions are often not sufficient to inform decision-making.
As life patterns changed, the accuracy of the models dropped.
Causal models remain robust when interventions change the statistical distributions of a problem.
Causal models also allow us to respond to situations we havent seen before and think about counterfactuals.
We dont need to drive a car off a cliff to know what will happen.
Counterfactuals play an important role in cutting down the number of training examples a machine learning model needs.
These attacks clearly constitute violations of the i.i.d.
The researchers also suggest that causality can be a possible defense against adversarial attacks.
Adversarial attacks target machine learnings sensitivity to i.i.d.
In a broad sense, causality can address machine learnings lack of generalization.
It is fair to say that much of the current practice (of solving i.i.d.
benchmark problems) and most theoretical results (about generalization in i.i.d.
configs) fail to tackle the hard open challenge of generalization across problems, the researchers write.
Two of these concepts include structural causal models and independent causal mechanisms.
Disentangling these causal variables will make AI systems more robust against unpredictable changes and interventions.
As a result, causal AI models wont need huge training datasets.
Pearl is a vocal critic of pure deep learning methods.
The paper does not, however, make any direct reference to hybrid systems.
Higher representations are crucial to causality, reasoning, and transfer learning.
At its core, i.i.d.
you’ve got the option to read the original articlehere.
