Presentation on current status and near future trend in AI. This talk also gives a sneak peek into how Machine Learning and Deep Learning works.

1. AI | Now + Next

2. Outline
• What is AI?
• Current state of AI
• Limitations of AI Today
• AI Achievements
• AI Talent war
• Machine Learning
• Deep Learning
• AI Trends

4. What is AI?
Artificial Intelligence is the science and engineering of
making intelligent machine, especially intelligent computer
programs
- John Mccarthy, Father of AI

5. Current State of AI
Strong AI
• Computers thinking at a level that
meets or surpasses people
• Computers engaging in abstract
reasoning & thinking
Weak AI
• Computers solve problems by
detecting useful patterns
• Pattern-based AI is an Extremely
powerful tool
• Has been used to automate many
processes today - Driving, language
translation

6. Limitations on AI Today
• Many things still beyond the realm of AI
• No thinking computers
• No Abstract Reasoning
• Often AI systems Have Accuracy Limits
• Many things difficult to capture in data
• Sometimes Hard to interpret Systems

7. AI Achievements

9. Is AI == ML today?
ML is the dominant mode of AI today

10. AI Talent War
There are around 300,000
qualified AI researchers. But
demand is in millions
Demand for data scientists will
surpass demand for engineers.
According to IBM, demand for data scientists will
increase to 2.7 million by 2020.

11. Machine Learning

12. Supervised Learning Demo

13. Unsupervised Learning

14. Reinforcement Learning

15. Video: https://www.youtube.com/watch?v=qy_mIEnnlF4&t=89s

17. Deep Learning

18. What have we achieved with this?

19. What have we achieved with this?

20. Yann LeCun in 1993 at Bell Lab
Video: https://www.youtube.com/watch?v=FwFduRA_L6Q

21. Deep Learning Timeline

22. GPU vs CPU
Ref: http://www.nvidia.com/object/what-is-gpu-computing.html
Compared
to CPUs
20x
speedups
are typical
GPUs also excel at floating-point vector operations
because neurons are nothing more than vector
multiplication and addition. All of these characteristics
make neural networks on GPUs what's

23. Single Layer Perceptron (Model Iteration 0)
Problems:
• The model outputs a real number whose value correlates with the concept of likelihood (higher values
imply a greater probability the image represents stairs) but there’s no basis to interpret the values as
probabilities, especially since they can be outside the range [0, 1].
• The model can’t capture the non-linear relationship between the variables and the target. To see this,
consider the following hypothetical scenarios:

24. Single Layer Perceptron with Sigmoid activation function (Model Iteration 1)
A
B

25. Multi-Layer Perceptron with Sigmoid activation function (Model Iteration 2)
Deep Learning

26. Back Propagation Algorithm

27. Deep Learning Architectures
Ref: https://www.ibm.com/developerworks/library/cc-machine-learning-deep-learning-architectures/index.html Ref: http://www.asimovinstitute.org/neural-network-zoo/

28. Computer Vision Applications

29. YOLO (You Only Look Once)
YOLO Video: https://www.youtube.com/watch?v=MPU2HistivI

30. YOLO
YOLO uses a single CNN network for both classification and
localising the object using bounding boxes.

31. Real-Time Autonomous Checkout

32. AI Trends
Taking AI to the edge
Decentralization and
Democratization
AI-as-a-Service Meta-learning solutions Quantum Neural
Blockchain AI
Capsule Networks
Amazon, Google,
Microsoft dominate
enterprise AI
AI is coming to clinical
diagnostics
DIY AI Automation
Blue & White colored
jobs
No UI is the New UI
Chatbots, Voice
enabled systems

33. AI at the Edge

34. AI-as-a-Service
Amazon
SageMaker

35. Meta-learning for Deep Learning Models

36. Capsule Networks
CapsNet will require less training data CapsNet may be less prone to hacking attacks

37. NO UI IS THE NEW UI
Google Soli
EMOTIV
TEXT BRAIN VOICE
NO UI IS THE NEW UINO UI IS THE NEW UI

39. The AI 100
2018

40. DISRUPTION
EVERYWHERE

42. ROAD TO DATA SCIENTIST
NOT ENOUGH

43. THANK YOU!