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Using AI to Understand Complex Causation - DZone AI

#artificialintelligence

Whenever something serious happens, we usually try and determine cause and effect. What was it that caused this thing to unfold the way it did? Whilst the theory is nice, we often employ some rather dubious explanations to try and explain the series of events. There have been attempts in the past to generate mathematical models for general causality, but they haven't been particularly effective, especially for more complex problems. A new study from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India, has attempted to use AI to do a better job.


Using AI to Understand Complex Causation - DZone AI

#artificialintelligence

Whenever something serious happens, we usually try and determine cause and effect. What was it that caused this thing to unfold the way it did? There have been attempts in the past to generate mathematical models for general causality, but they haven't been particularly effective, especially for more complex problems. A new study from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India, has attempted to use AI to do a better job. The model creates significant opportunities to analyze complex phenomena in areas such as economics, disease outbreaks, climate change and conservation," the researchers say.


new-math-untangles-the-mysterious-nature-of-causality-consciousness

WIRED

Using the mathematical language of information theory, Hoel and his collaborators claim to show that new causes--things that produce effects--can emerge at macroscopic scales. They say coarse-grained macroscopic states of a physical system (such as the psychological state of a brain) can have more causal power over the system's future than a more detailed, fine-grained description of the system possibly could. Just as codes reduce noise (and thus uncertainty) in transmitted data--Claude Shannon's 1948 insight that formed the bedrock of information theory--Hoel claims that macro states also reduce noise and uncertainty in a system's causal structure, strengthening causal relationships and making the system's behavior more deterministic. With Albantakis and Tononi, Hoel formalized a measure of causal power called "effective information," which indicates how effectively a particular state influences the future state of a system.


Discovering Causal Relations by Experimentation: Causal Trees

AAAI Conferences

Generally, the less background knowledge needed, the better; the robot should be able to start 92 MAICS-97 out with the "mind of an infant" and learn everything it needs.


Causation in a Nutshell

#artificialintelligence

Knowing the who, what, when, where, etc., is vital in marketing. Predictive analytics can also be useful for many organizations. However, also knowing the why helps us better understand the who, what, when, where, and so on, and the ways they are tied together. It also helps us predict them more accurately. Knowing the why increases their value to marketers and increases the value of marketing.