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Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem
Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.
We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...
Alembic Technologies has raised $145 million in Series B and growth funding at a valuation 15 times higher than its previous round, betting that the next competitive advantage in artificial ...
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