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Tuesday, December 10, 2019

The Book of Why: Exploring the missing piece of artificial intelligence | Blog - TechTalks

Welcome to TechTalks’ AI book reviews, a series of posts that explore the latest literature on AI.

Ben Dickson, software engineer and the founder of TechTalks suggest, The Book of Why, written by Judea Pearl, explores why our artificial intelligence can perform complicated tasks by can't answer simple questions.

Photo: Depositphotos
In the past six decades, the field of artificial intelligence has traveled through a meandering path, passing through periods of excitement and disenchantment, and a longstanding dispute between various approaches to creating intelligence.

Today, deep learning, the current dominant AI technique, owes its success in large part to an abundance in data and compute resources. Thanks to deep learning models and their underlying technology, artificial neural networks, we have been able to tackle problems that were impossible to solve with classical AI approaches. There are now AI algorithms that can outperform humans at many complicated tasks, such as playing Go or predicting cancer.

Today, most advances in the field are associated with creating bigger neural networks and training them with more and more data. In the past few years, this approach has yielded AI models that can perform more accurately on tasks that require spatial consistency (e.g., image classification), or temporal consistency (e.g., text generation).

But the current excitement surrounding pouring more data and compute into deep learning models has blinded most research to one of the fundamental problems that AI technology still suffers from: causality.
The Book of Why: The New Science of Cause and Effect, written by award-winning computer scientist Judea Pearl and science writer Dana Mackenzie, delves into this topic...

The ladder of causation 
In The Book of Why, Pearl introduces the “ladder of causation,” a three-level model to evaluate the intelligence of living or artificial systems. While a lot of the book goes into explaining the ladder of causation with historical and practical examples, I’ll do my best to summarize it here...

The mini-Turing test 
Pearl has focused The Book of Why on what he calls “the mini-Turing test,” named after the AI evaluation experiment that computer science pioneer Alan Turing proposed in 1950. Pearl describes the mini-Turing test as such:

“How can machines (and people) represent causal knowledge in a way that would enable them to access the necessary information swiftly, answer questions correctly, and do it with ease, as a three-year-old child can?”...

The Book of Why, a much-recommended read to anyone who’s interested in an alternate view on the current state of AI, is much more than just a discussion about intelligence. It’s a look at the history of causal science and humanity’s path from observing data to developing new sciences.

Recommended Reading

The Book of Why:
The New Science of
Cause and Effect
Source: TechTalks