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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.
Dr. Pravir Malik is the founder and chief technologist of QIQuantum and the Forbes Technology Council group leader for Quantum Computing. Given the rapidity with which AI is evolving and the speed ...
The financial industry has sophisticated risk models. It does not yet have causal ones. That distinction is about to matter enormously. In the decade following the 2008 financial crisis, the ...
Insurance is, at its core, a causal business. A policyholder does not file a claim because their profile resembles historical claimants. They file a claim because something caused them harm. A ...
What’s the link between ice cream sales and crime rates? Let’s say an overzealous rookie reporter finds that ice cream sales and crime rates tend to increase and decrease together. This can suggest a ...
Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...
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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