This document discusses the intersection of art, science, and programmable intelligence in the current era. It introduces Eliza and Cleverbot as early examples of AI assistants and compares their abilities to stay on topic in conversations. It then covers concepts in machine learning like supervised versus unsupervised learning and examples of algorithms like linear regression, decision trees, and logistic regression. It concludes by asking about views on machine learning in the programmable age.

1. Art & Science in the Programmable
Age
Catherine Williams
Chief Data Scientist
#ANEventsWeek

2. PROGRAMMABLE
PRO GRAMMAT IC INTE LLIGE NCE

3. Eliza, the Psychotherapist
Eliza, what
do you think
about Brexit? We were
discussing you,
not me.

4. Eliza, the Psychotherapist
What about your
political
situation?
Why do you ask?

5. Eliza, the Psychotherapist
Because I want
your opinion.
What if you
never got my
opinion?

6. Eliza, the Psychotherapist
Can you please
stay on-topic?
Perhaps you would
like to be able to
please stay on
topic?

7. Cleverbot, the AI with some intelligence
Cleverbot, what
do you think
about Brexit?
I think about
dogs, what
about you?

8. Cleverbot, the AI with some intelligence
What about the
political
situation?
What do you think
about Atlas
Shrugged?

9. Cleverbot, the AI with some intelligence
Can you please
stay on-topic? I don't think you can
complain about that
considering how many
times you've changed
the subject without
reason.

11. PRO GRAMMAT IC AU TOMATION REAL-TIME
D EC I S I O NI NG

12. Expressiveness
ex·pres·sive·ness
noun
For a language, the breadth of ideas that can be represented
and communicated in that language.
For algorithms, the breadth of data feeding in, so that it can
be potentially sensitive to very small differences in input.
Source: Wikipedia

13. MACHINE LEARNING
Supervised or Unsupervised

14. SUPERVISED LEARNING
# of Bedrooms
Home Price (£)

15. SUPERVISED LEARNING
# of Bedrooms
Home Price (£)

16. SUPERVISED LEARNING: Linear Regression
# of Bedrooms
Home Price (£)

17. SUPERVISED LEARNING: Linear Regression
# of Bedrooms
Home Price (£)

18. SUPERVISED LEARNING: Decision Tree

19. SUPERVISED LEARNING: Logistic Regression
1
0.5
0

20. DEEP LEARNING
INPUT
LAYER
HIDDEN
LAYER 1
OUTPUT
LAYER
HIDDEN
LAYER 2

21. UNSUPERVISED
LEARNING

22. UNSUPERVISED
LEARNING

23. UNSUPERVISED
LEARNING

24. S C IE NC E ART
PROGRAMMABLE

25. S C IE NC E
ART
PROGRAMMABLE

26. What do you think
about machine
learning in the era
of the
programmable age?
I think that in the
proper controlled
environments handled
by competent people
it can be quite
advantageous.