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Causal inference is an important and active area of artificial intelligence research today. Indeed, no less than Turing award winner Yoshio Bengio lists causal reasoning as a top priority, as does his co-Turing award winner Yann LeCun, who writes that “Lots of people in ML/DL know that causal inference is an important way to improve […]
An Alternate Formulation for Causal Inference Research Read More »
Most conversations between decision makers and data professionals begin back-to-front If you’re a decision maker who wants to make the best use of data and other information resources, such as artificial intelligence (AI), to support your decisions, chances are that conversations between you and your data analysts is happening back-to-front. Data analysts should encourage us
Guest Post: The Backwards Conversation (part 1 of a series) Read More »
Forbes reports that “most AI projects fail”; Techrepublic puts the number at 85%. How do we reconcile this reality with claims of AI as representing an “existential threat” such as those from Elon Musk and futurism.com? Often called AGI (Artificial General Intelligence), the idea is a general-purpose, human-like intelligence like Hal from 2001 or the
If AI Doesn’t Work, then How Could it Kill Us? Read More »
In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word “decision” to mean a lot of things. Let’s tease out some distinctions: Decision TypeNameQuestion answeredPrimary information SourceTypical success criterionTypical methodAML classification“Decisions That”: “What is this picture?” “What disease does this person have?” “Is this a cat?”DataTrue positive, true negativeSupervised learningBML regression“Decision
Understanding what we mean by “Decision” in ML/AI/DI Read More »
