The document discusses artificial intelligence and how machines learn to understand and recreate the world. It explains that machines are shifting from rule-based systems to learning-based systems using large datasets and powerful computing. The three main types of machine learning are supervised learning by being taught, unsupervised learning by observation, and reinforcement learning by exploration. Generative AI can now synthesize realistic text and data without human input.

1. Artificial Intelligence
How Machines Learn To Understand
and Recreate The World
Dr. Michael Platzer

3. Example: Progress in Image Classification
- 1’300’000 images
- 1’000 categories

5. The 3 Drivers of AI
BIG DATA BIG COMPUTE DEEP LEARNING

6. systems
that know
based on rules
systems
that learn
based on data
PARADIGM SHIFT

7. How do these Machines Learn?

8. How do these Humans Learn?

9. 1) we learn by Observation

10. 2) we learn by Exploration

11. 3) we learn by Being Taught

12. Learning by Being Taught
→ Supervised Learning
Learning by Observation
→ Unsupervised learning
Learning by Exploration
→ Reinforcement Learning

13. Supervised Learning Learning by Being Taught
dog
dog
cat
cat
cat
catdog
dog
dog
cat

14. Supervised Learning Learning by Being Taught

15. Alaskan Malamute Siberian Husky
Supervised Learning Learning by Being Taught

16. Unsupervised Learning Learning by Observation

17. Unsupervised Learning Learning by Observation

18. Unsupervised Learning Learning by Observation

19. Unsupervised Learning Learning by Observation
Sushi - Japan + Germany =

20. Unsupervised Learning Learning by Observation
Sushi - Japan + Germany =

21. Unsupervised Learning Learning by Observation

22. Reinforcement Learning Learning by Exploration

23. Reinforcement Learning Learning by Exploration

24. So, How do these machines learn?

25. y = f(x, w)
single layer layout two layer layout
multi layer architectures

26. cat: 82%
dog: 18%
This is a Cat! This is a Cat! This is a Cat! This is a Cat!
y = f(x, w)

28. Generative AI

29. Synthetic Shakespeare Synthetic Linux Source Code
Generative AI for Synthetic Text
→ no a-priori assumptions
→ no feature engineering

30. highly realistic & representative synthetic data for
testing, development, innovation & collaboration
Generative AI for Synthetic Customer Data

31. Michael Platzer
michael.platzer@mostly.ai
LinkedIn.com/in/mplatzer