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Artificial Intelligence
How Machines Learn To Understand
and Recreate The World
Dr. Michael Platzer
Example: Progress in Image Classification
- 1’300’000 images
- 1’000 categories
The 3 Drivers of AI
BIG DATA BIG COMPUTE DEEP LEARNING
systems
that know
based on rules
systems
that learn
based on data
PARADIGM SHIFT
How do these Machines Learn?
How do these Humans Learn?
1) we learn by Observation
2) we learn by Exploration
3) we learn by Being Taught
Learning by Being Taught
→ Supervised Learning
Learning by Observation
→ Unsupervised learning
Learning by Exploration
→ Reinforcement Learning
Supervised Learning Learning by Being Taught
dog
dog
cat
cat
cat
catdog
dog
dog
cat
Supervised Learning Learning by Being Taught
Alaskan Malamute Siberian Husky
Supervised Learning Learning by Being Taught
Unsupervised Learning Learning by Observation
Unsupervised Learning Learning by Observation
Unsupervised Learning Learning by Observation
Unsupervised Learning Learning by Observation
Sushi - Japan + Germany =
Unsupervised Learning Learning by Observation
Sushi - Japan + Germany =
Unsupervised Learning Learning by Observation
Reinforcement Learning Learning by Exploration
Reinforcement Learning Learning by Exploration
So, How do these machines learn?
y = f(x, w)
single layer layout two layer layout
multi layer architectures
cat: 82%
dog: 18%
This is a Cat! This is a Cat! This is a Cat! This is a Cat!
y = f(x, w)
Generative AI
Synthetic Shakespeare Synthetic Linux Source Code
Generative AI for Synthetic Text
→ no a-priori assumptions
→ no feature engineering
highly realistic & representative synthetic data for
testing, development, innovation & collaboration
Generative AI for Synthetic Customer Data
Michael Platzer
michael.platzer@mostly.ai
LinkedIn.com/in/mplatzer

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Artificial Intelligence - How Machines Learn