Py paris2017 / promises and perils in artificial intelligence, by Andreas Muller

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The document discusses both the promises and perils of artificial intelligence. It notes that machine learning has made significant progress in areas like image recognition and natural language processing by learning from large datasets, but still struggles with tasks requiring complex reasoning or knowledge. Key challenges mentioned include issues of interpretability, understandability, and bias in machine learning models. The author advocates for optimism in the field's potential while also acknowledging its current limitations.

Promises and Perils
in Artificial Intelligence
and why you shouldn’t trust anyone that uses the phrase Artificial Intelligence
Andreas Müller
Columbia University, scikit-learn

What is Data Science?
“The practice of, and methods for, reporting,
inference, and decision making based on data.”

What is (supervised) Machine Learning?
“Extracting information from data to make
predictions on new observations.”

Observe the past – predict for the future
Given examples: generalize the pattern
Generalization

Hope Hype
Deep Learning
Big data
Machine Learning
AI
Counting
Statistics
Data Visualization
Data Science
Bots

Classifying with many examples Works in the real world,
sometimes superhuman
Generalizing to new concepts
using few examples
“works” in papers
Producing text or complex objects “works” in papers
Reasoning with world-knowledge no-one knows

Where does Python
(and scikit-learn fit in)?

amueller.github.io
@amuellerml
@amueller
t3kcit@gmail.com
https://github.com/amueller/talks_odt/

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