Introductory talk on AI, given at Emerald Hub, co-working space, located at the Phnom Penh International University (PPIU) on 3 May 2017 to a tech audience. Note: Title based on talk from Andrew Ng with the same name. Source material where relevant referenced in speaker notes. Originally prepared by Andries, CEO of Slash, for an internal Slash team talk.

1. AI Talks:
Artificial Intelligence is the new Electricity
Last updated: 3 May 2017
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2. AI
Last updated: 3 May 2017
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3. Product Foundry.
For innovation teams and ambitious
startups/SMEs. Focus: deep tech.
Venture Builder.
Serial entrepreneurs. Slash Ventures is
where we build our own lean projects.
Offices in Singapore and Phnom Penh
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4. JUST CURIOUS ‘AI’ GEEK
WHY ARE YOU HERE?
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5. PART 1
What is AI, why it matters
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9. From Artificial Power
To
Artificial Intelligence
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12. • Klaus Schwab, is the founder
and CEO of the World
Economic Forum at Davos
• Survey of 800 executives and
experts surveyed at WEF
• Capture deep shifts occurring
in society as a result of new
software and services.
Encourage everyone to think
about the impact of these
changes on our society.
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13. SURVEY
Take 8min
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14. SURVEY RESULTS
(turn the page)
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15. What is Artificial Intelligence?
“the science of engineering and making intelligent
machines”
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16. WEAK AI
- Narrow tasks
- Very powerful tool
STRONG AI
- General intelligence
- Outperform
humans on all tasks
2 types of AI
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17. WEAK AI
- Narrow tasks
- Very powerful tool
STRONG AI
- General intelligence
- Outperform
humans on all tasks
FOCUS OF THIS TALK
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18. WAVE 1
WAVE 2
WAVE 3
3 WAVES OF AI
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19. WAVE 1: EXPERT SYSTEMS
+ Expert systems are rule-based
- Very narrow, no learning capability, poor in real-world
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20. Limits of expert systems: DARPA car challenge
2004: 0 finish 2005: 5 finish
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21. WAVE 2: MACHINE LEARNING
95% of ‘hyped’ AI applications as of 2017
A (Input)
Examples:
Email
Image
Audio
English
Text
B (Response)
Spam (0/1)
Object (cats, dogs etc)
Text (speech recognition)
Khmer
Audio (A, B etc)
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22. Example of data clustering
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23. Example of data clustering
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24. WAVE 2: MACHINE LEARNING
+ Probabilistic, statistical learning techniques
+ Good at supervised learning, classifying and predicting
- Needs lots of data to learn
- Limited ability to understand context and reason
- Statistically impressive, individually unreliable
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25. WAVE 3: (FUTURE) CONTEXTUAL ADAPTATION
+ AI construct explanatory models
+ Seed AI require much less training data
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26. Example: cat image recognition
This is a cat because …
It has fur, whiskers and claws
It has features:
Explanation
Model
Learning
Process
Decision
Explanation
Training
Data
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27. Example: handwriting
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28. EXPERT SYSTEMS
3 WAVES OF AI
MACHINE LEARNING
CONTEXTUAL
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29. From AI-assisted Humans
To Human-assisted AI’s
Will Humans evolve into
supervisors of AI’s or bots?

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31. YR MILESTONE
2023 $1000 for Human Brain capability
2049 $1000 for Human Race capability
2059 1c for Human Race capability
SUPERINTELLIGENCE?
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32. PART 2
Getting started to apply AI
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33. What’s your goal?
Why do you want to learn AI?
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34. “People hire a product or
service to get a job done”
- Clayton Christensen
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35. Job: “listen to music”
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36. Next 10,000 startups …
“AI for X”
‘X’ = your favorite digital brands J
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37. Exciting times to build an AI Startup
Just 2 small problems …
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The reality from the
trenches: AI is difficult
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39. 2 challenges to start with AI
Access to Data Access to Talent
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41. All Tech Giants got the AI memo too …
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42. “AI would be the ultimate version of
Google” – Larry Page [2000]
“We will move from a ‘Mobile First’ to
an ‘AI First’ world” – Larry Page [2016]
… and are betting the farm on AI
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43. And they have all the data in the world
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44. How to compete as small company?
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45. HORIZONTAL
Tech giants have a formidable advantage when it comes to
building broad horizontal products (image/video/voice
recognition, language translation) and infrastructure (AI Cloud)
VERTICAL
However, they’re not going to tackle every single vertical problem
VS
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46. Tech Giants more focused on
Consumers
For Startups, plenty of niche
B2B opportunities with data
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52. Do you need a PhD in Machine
Learning?
No. But it helps J
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53. Example 2015
PALANTIR job ad:
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Lots of AI tools to get you started
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55. So many….
• AWS Machine Learning (Amazon)
• TensorFlow (Google)
• Caffe2 (Facebook)
• Sonnet (Google/DeepMind)
• Torch (research)
Most popular AI/Machine Learning tools?
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56. Half the battle in AI deployment
is expectation management and social
engineering
Not technology.
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57. • Be clear on why?
• Online courses (Coursera, Udacity ...)
• Read books & articles, watch videos
• Attend meetups and AI events
• Real-world AI competitions (Kaggle)
• Set AI goals (Mark Zuckerberg’s ‘Jarvis AI’)
• Have patience, it’s a journey!
Learning AI?
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58. Most AI engineers started as
“newbies” and “wannabes”.
So are we. Baby steps.
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59. QUESTIONS?
Andries De Vos
CEO & Co-Founder
andries@slash.us.com
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