20171221 Invited talk for team colleagues in tea time. Basic understanding of AI and ML sharing, along with some personal experiences.

1. Just small talk about AI
and new opportunity in IC design industry

2. Puppy or Muffin?

3. ML progress in Image Recognition

4. What is AI (or ML)?
A part of the field of “predictive statistics”

5. When someone is talking about using AI in a
project…
• Breaking the project down into the components drawn above, and
then building the right model
• Gathering training examples, which is often the hardest part of the task;
• Choosing the basic shape of the model
• Which is what things like “neural networks”, “decision trees”, and so on; these are basic
kinds of model which are good for different problems;
• Running the training;
• And then, most importantly, figuring out what’s broken and adjusting it.

6. The extent of intelligence depends on…
• The capacity and efficiency of the model,
• The amount and relevance of the experience data,
• The efficacy of algorithm for learning, explanation, prediction, and
inference,
• The speed and efficiency of processors executing the algorithms.

7. e.g. Six images to train your Grey Cat Detector
Overfitting: What the model actually learned was “grey cats are cat-shaped things which sit on carpets”

8. What AI is good at and bad at
• [No need to use AI] Clear goals in a predictable environment
• Camera to spot the wheels of cars in an assembly line.
• [Harder] Clear goals in an unpredictable environment
• Get from location A to location B safely and at a reasonable speed. (self-driving cars)
• [Harder] Indirect goals in a predictable environment
• Playing games, like Go.
• [Harder] Indirect goals in an unpredictable environment
• Winning at board games (like planning your financial portfolio).
• [Can’t be solved by AI at all] Undefined goals
• Writing a good novel.
We already made major inroads here

9. What do real world hard questions look like?
Kabir Alli’s (in)famous results
The Passenger and the Pedestrian Polite fictions

10. Let’s see some real cases
What kind of job AI can do today?
(No matter what “they” do it’s good enough or not)

11. Play Go better than humans

12. GO TO DMC

13. Write sci-fi screenplay

14. Absolutely nail Super Mario

15. Fly a drone

16. Machine Learns To Code
pay tribute to Michael Learns To Rock

17. Write software unit tests

18. But watch out!

20. 結語
• 現下看來，還是抱緊 5G 大腿比較實在。 ;-P
• AI could be
• Meaningless marketing jargon
• A new empty buzzword
• Be sure what are you going to do with AI

21. New opportunity in IC design
industry
Case study – GRAPHCORE

23. Chief investigator of NCTU CGI
……

24. GRAPHCORE
https://www.graphcore.ai

25. Two founders catch my eyes…
Nigel Toon (CEO) and Simon Knowles (CTO), who are semiconductor industry veterans,
with their biggest success being Icera, which sold to NVIDIA for $435 million.

47. Who invests GRAPHCORE?
• Only ones are well known by me named here:
• Samsung
• Dell
• Demis Hassabis, co-founder of DeepMind (i.e. the boss of Aja Huang)
• Zoubin Ghahramani, Chief Scientist at UBER

48. APPENDIX

49. Relationship
Artificial
Intelligence
Machine
Learning
Deep Learning
Reinforcement
Deep Learning

50. Parents why bother?
Of course 這裡在意的是從中訓練思考與邏輯的過程

51. Programmers why bother?

52. How did TTPCom talents impact the IT
industry?
？
Silicon IP
AJAR
Protocol stack

53. My TTPCom journey

54. The end of the story