An introductory overview of current AI technology and applications, and career advice for those who are interested in AI.

1. AI Technology Overview and
Career Guidance
Kunling Geng

2. Self-intro
• B.S. (2008-2012), Shanghai Jiao Tong University
• Electrical and Computer Engineering
• PHD. (2012-2017), University of Southern California
• Biomedical Engineering, USC-Viterbi PhD fellowship recipient
• Research focuses Computational Neuroscience, AI and Neural Networks
• Work(2017-present), Decision Engines Inc.
• AI & DS Team Lead, AI Architect

3. Agenda
• Intro to AI
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Deep Reinforcement Learning (DRL)
• Generative Adversarial Networks (GAN)
• Deep Learning Challenges
• Career Advice

5. “systems that have been taught or learned how to carry out specific tasks”

6. We are still very far away from
“Artificial General Intelligence”
• General Intelligence is the type of adaptable intellect found in humans
• A flexible form of intelligence capable of learning how to carry out
vastly different tasks
• Anything from haircutting, driving, building spreadsheets

7. Sophia Scam??
In October 2017, Sophia became the
first robot to receive citizenship of any
country.
Sophia the Robot live on Jimmy Kimmel Show, 2018

8. My understanding or Current AI

10. Deep Learning History

11. What makes Deep Learning Successful now?
• Massively parallel computing with GPUs
• Appearance of large, high-quality labeled datasets
• Software platforms
• New architecture and techniques

12. Deep Learning Fundamental Architectures
• Deep Restricted Boltzmann Machine (Pioneer of deep learning)
• Convolutional Neural Networks (CNN)
• Deep Recurrent Neural Networks (LSTM, GRU)
• Deep Reinforcement Learning (DRL)
• Generative Adversarial Networks (GAN)

13. CNN and Its Applications

14. Convolutional Neural Networks (CNN)

15. CNN Applications
• Self Driving Cars (Object Detection)
• Face Detection and Recognition (Face ID)
• Medical Image Diagnosis (Image classification and localization)
• Human Gesture and Pose recognition
• Optical Character Recognition
• Natural Language Processing
• Robotic and Manufacturing

16. Object Detection
• Detect and localize multiple objects in one image

17. Object Detection Model: Faster RCNN

18. Google Tensorflow Object Detection API
https://github.com/tensorflow/models/tree/master/research/o
bject_detection

19. RNN and Its Applications

20. Recurrent Neural Networks

21. RNN Applications
• Speech Recognition (Siri, Amazon Alexa, Google
home)
• Speech Synthesis
• Neural Machine Translation
• OCR (Tesseract 4)
• Named Entity Recognition (NER)
• Document Classification
• Sentiment analysis
• Hand writing generation
• Question answering Machine
• Video caption generation

22. Deep Reinforcement Learning and Its
Applications

23. Reinforcement Systems
• Find a sequence of optimal actions to maximize the award

24. Deep Policy Networks

25. Deep Reinforcement Learning Applications
Video Games
Games
Alpha Go
Robotics

26. Generative Adversarial Network and Its
Applications

27. Generative Adversarial Networks (GANs)

28. Analogy of GAN

29. GAN Applications
Super Resolution
Fake but realistic celebrities photos
Photo/Image Editing
Style/Design Generation

30. Deep Learning Challenges

34. Challenges of Deep Learning Models
• 1. Lack of transparency
• Lack of interpretations
• very hard to debug
• 2. Required a lot of training data, especially annotated data by human
• ImageNet has 14 million images, Coco data set has more than 100, 000 images
• Transfer learning could help
• 3. Not very robust, and easy to be attacked
• Change a single pixel of image could lead to a misclassification
• 4.Very shallow
• Most of the DL models are now only good at perception levels
• Cannot deduct and infer like human
• Cannot make use of prior knowledge
• Bad at hierarchical representations of knowledge

35. AI + DS Career Guidance

36. Data Science + AI Jobs
• “Old-fashioned” Data Science Positions (Process Structured Data)
• Data Analyst
• Business Analyst
• Data Engineer
• Data Scientist (Process structured data like excel, database)
• New DS & AI Related Jobs (Process Unstructured Data)
• Deep learning / Machine Learning Data Scientist
• Computer Vision Engineer
• Natural Language Processing (NLP) Engineer
• ML, DL, CV, NLP Research Scientist

37. How to become an AI Expert?
• Foundations:
• Math, Statistics, Linear Algebra, Signal Processing, Image Processing
• Knowledge of Machine Learning and Deep Learning
• Basic skills of Linux, Bash, Docker
• Basic Web Techs such as HTML, CSS, JS, API, etc
• Algorithm, OOP, system design
• Tools:
• Python/C, C++ (not recommend R)
• Keras + Tensorflow / Pytorch
• Advanced Projects and Skills:
• Domain Expert in Computer Vision / NLP / Speech Recognition / Speech Syntheses / OCR / Video Analysis /…
• Research skills, reading and writing papers, presentation, etc.
• Advanced Projects that showed your ability to complete an AI project from end to end
• Advanced Projects showed your capability in research, problem solving, or innovations

38. Recommended Learning Resources for Beginner
• Andrew Ng, Machine Learning and Deep Learning Courses on Coursera
• Feifei Li, Stanford CS231n, focus on computer vision
• Chris Manning, Stanford CS224n, focus on NLP
• Book: Hands-On Machine Learning with Scikit-Learn and TensorFlow
• Code Examples: https://github.com/keras-team/keras/tree/master/examples

39. Recommended Projects for Beginners
• 1. Image Classification
• Crawl data from google, build a classifier from scratch
• 2. Object Detection
• Collect and annotate the data by yourself
• Use Tensorflow object detection api to fine-tune the model
• 3. Document Classification
• Collect documents with different categories
• Build a text classifier using NLP models
• 4. Pick a problem that you are really intrigued and want to solve
• Data collection, annotation,
• model selection and training
• Build a demo, show your results

40. Q&A
https://www.linkedin.com/in
/kunling-geng-37229463/
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