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AI | Now + Next
Outline
• What is AI?
• Current state of AI
• Limitations of AI Today
• AI Achievements
• AI Talent war
• Machine Learning
• Deep Learning
• AI Trends
What is AI?
Artificial Intelligence is the science and engineering of
making intelligent machine, especially intelligent computer
programs
- John Mccarthy, Father of AI
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
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
AI Achievements
Is AI == ML today?
ML is the dominant mode of AI today
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.
Machine Learning
Supervised Learning Demo
Unsupervised Learning
Reinforcement Learning
Video: https://www.youtube.com/watch?v=qy_mIEnnlF4&t=89s
Deep Learning
What have we achieved with this?
What have we achieved with this?
Yann LeCun in 1993 at Bell Lab
Video: https://www.youtube.com/watch?v=FwFduRA_L6Q
Deep Learning Timeline
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
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:
Single Layer Perceptron with Sigmoid activation function (Model Iteration 1)
A
B
Multi-Layer Perceptron with Sigmoid activation function (Model Iteration 2)
Deep Learning
Back Propagation Algorithm
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/
Computer Vision Applications
YOLO (You Only Look Once)
YOLO Video: https://www.youtube.com/watch?v=MPU2HistivI
YOLO
YOLO uses a single CNN network for both classification and
localising the object using bounding boxes.
Real-Time Autonomous Checkout
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
AI at the Edge
AI-as-a-Service
Amazon
SageMaker
Meta-learning for Deep Learning Models
Capsule Networks
CapsNet will require less training data CapsNet may be less prone to hacking attacks
NO UI IS THE NEW UI
Google Soli
EMOTIV
TEXT BRAIN VOICE
NO UI IS THE NEW UINO UI IS THE NEW UI
The AI 100
2018
DISRUPTION
EVERYWHERE
ROAD TO DATA SCIENTIST
NOT ENOUGH
THANK YOU!

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AI | Now + Next