The document provides an introduction to a course on designing artificial intelligence. It discusses the history and recent successes of AI, including deep learning techniques. It notes challenges like bias in data and the need for more diversity in AI development. The course will focus on current machine learning techniques and how AI could help underserved groups. Students will research AI technologies, propose solutions, and develop new ways for non-experts to understand AI through prototypes and reflection.

1. Designing A.I.
Week 1: Introduction
January 25, 2017
David Young
dyoung@newschool.edu
https://courses.newschool.edu/courses/PSDS5330

2. - Introduction
- This Semester
- Homework

3. A little (of my) background…
1980s AI Boom
Lisp Machines
Expert Systems
Boston’s Route 128
AI Winter

4. But recently AI started
having successes...
IBM’s Watson

5. Google DeepMind’s
AlphaGo

6. Companies are
restructuring around AI

7. No longer “The Uber of …”
It’s “... with A.I.”

8. What changed?
- Massive amounts of data
- Cheap parallel processing
- Machine learning / neural network techniques

9. A.I. is the collective term for technologies that include:
Pattern recognition
Natural language processing
Image recognition
Hypothesis generation
Google’s frames this as:
- Machine intelligence (thinking)
- Natural language processing (listening)
- Machine perception (seeing)

10. Statistics and Data Analysis
Pattern Recognition
Neural Networks and Deep Learning
Learning Clusters & Recommendation Systems
Reinforcement Learning

11. Neural networks
Learning types:
Supervised
Unsupervised
Semi-supervised
Reinforcement

12. Deep Learning
Involving multiple layers of learning systems
which are tasked with discovering increasingly
abstract or “high-level” patterns.
(This approach is often referred to as
hierarchical feature learning.)

13. There are platforms available for use
today...

14. And applications from the practical to the
fantastical...

16. We’re at the start of a
new era

17. Maybe even more radical?

18. Bank of America Merrill Lynch predicted that by 2025 the
“annual creative disruption impact” from AI could amount
to $14 trillion-33 trillion, including a $9 trillion reduction in
employment costs thanks to AI-enabled automation of
knowledge work; cost reductions of $8 trillion in
manufacturing and health care; and $2 trillion in
efficiency gains from the deployment of self-driving cars
and drones.
The McKinsey Global Institute, a think-tank, says AI is
contributing to a transformation of society “happening ten
times faster and at 300 times the scale, or roughly 3,000
times the impact” of the Industrial Revolution.

19. There’s a lot of hype.
But things are moving fast.
We need to engage now.

20. But with speed come problems…
- Bias in learning data
- Questionable learning techniques

22. Ethical / Privacy issues

23. Where do these problems come from?
- Monoculture
- Lack of diversity
- Tech barriers

24. “We can bemoan or welcome the digital revolution, the
coming of self-driving cars, social change or the mass
movement of peoples, but we can’t stop any of it. What
we can do is try to make these changes work for the
betterment of our lives and our planet.”

25. “I am personally not worried about an AI
apocalypse, as I consider that a
completely made-up fear.
I am concerned about the lack of diversity
in the AI research community and in
computer science more generally.”
Jeff Dean, Google Brain Project Lead

26. “What’s important is to find the people who
want to use AI for good—communities and
leaders—and figure out how to help them
use it.”
“This year, Artificial Intelligence will become
more than just a computer science problem.
Everybody needs to understand how A.I.
behaves.”
“I think we can do a lot better in making AI
easier to understand for social scientists and
other non-computer science folks.”

27. “For AI to be successful, its not just engineers and
computers scientists talking to each other, it involves policy
design, art, psychology, philosophy. There is something
amazing about imaging that confluence of conversations.”
Genevieve Bell, Intel
Speaking at #AINow

28. There is progress…
But we need more voices.

29. “Technical mastery should not be a requirement for
participating in building our collective future.”

30. Outsiders have engaged before...

31. Embrace the newness
and strangeness of it all.

32. Cultural imagination can inspire
technology’s roadmap

33. We need to find new languages,
metaphors, and intuitions.
We need new ways for non-technical
stakeholders to engage.

34. This might be the only way we will be able
to understand the technology.

35. - Introduction
- This Semester
- Homework

36. This is not about artificial
general intelligences (AGI).
And it is not about future
fictions.

37. We're engaging with current and near-term A.I.
Focusing on Machine Learning

38. This is a workshop / studio.
We're embracing our outsider perspectives.
There are no answers (yet).

39. Technology
Survey the current
landscape of A.I.
technologies and the
surrounding questions of
social bias.
Solutions
Explore how A.I. could be
applied in service to the
aspirations of an underserved,
alternative, or creative
community.
Bridging
Develop new strategies,
methods and languages
to facilitate the intuition
and understanding of A.I.
by non-technical
stakeholders.
Our approach:

40. Technology (& Language Mapping)

41. “This year’s competition will focus on three underserved
groups in New York City: Youth (13-18), Seniors (65+),
and Immigrants.”
www.bigapps.nyc & www.civichalllabs.org
Solutions: Communities

42. Bridging
Start with human-centered design.
But is it out of date?
The human may no longer be the center.

43. What process should we use?

44. 4D?
Can be very slow
No chance to iterate

45. Agile?
Hard to develop a vision
Can be too rigid

46. Build Measure Learn?
(Lean Startup)

47. This term…
Two Sprints
Research
SynthesisIdeation
Design
“Learn"
Iterate

48. Technology SolutionsBridging
Learning
Language
Needs &
opportunities
Bridging &
Methods Concept
Prototype & test

49. Technology SolutionsBridging
Learning
Language
Needs &
opportunities
Bridging &
Methods Concept
Prototype & test
After mid-term, we examine and reflect.
Second phase projects can focus on solution refinement,
methodological tools for bridging, or technical roadmaps.

50. Groups
Pairs
Teams

52. This is a studio.
Making is important.
Prototyping not programming.

53. Embrace our collective diversity, perspectives,
methods, and languages.
To learn from and inspire one another……..

54. Tools & Resources
- Slack
- Graph Commons
- Google Drive
- Medium https://medium.com/designing-ai-spring2017
www.designing.ai
#designingai

55. Designing A.I.