In this presentation, I show what and how you can build AI products in short, mid and long term.

1. Artificial Intelligence:
Case studies
Rudradeb Mitra
https://www.linkedin.com/in/mitrar/

2. My objective
Make you guys build something in AI

3. Two kinds of algorithms
• Deep learning (supervised or unsupervised) with Neural
networks
• Reinforcement learning

4. Deep learning with NN

5. What can you build?
• Short term - Things you can build from a month to 6
months using existing tools. Mostly feature.
• Mid term - Things you can build within 1-2 years. Can use
some existing frameworks but you also need to add an
app.
• Long term - Solving some visionary major problems.

6. Short term
• Chat bots - Language analysis, sentiment analysis..
✓ pick up a sector, a customer and build their bot
Trained Neural Network
Interface /API
You

7. How to build a simple chatbot

8. How to build a simple chatbot
User Says "From where can I buy burgers near Times Square in
Manhattan".

9. Short term
Use existing tools and available softwares.
• Chat bots - Language analysis, sentiment analysis..
✓ pick up a sector, a customer and build their bot
• Image recognition
✓ used by e-commerce (search, advance AR)

10. Reinventing shopping
experience
Google cloud vision API

11. Mid term
Your Data
Interface /API
Neural Network
Existing Data

14. Mid term
• Reduce road accidents by driver analysis
✓ Insurance companies
• Predict customer buying behavior
✓ Retail
• Predictive maintenance
✓ Industrial IoT

15. Identifying risky drivers

17. Classification with NN
Driving score
Mobile
usage
score

18. Predicting customer buying
behaviors

19. vdts
product_id user
_id
eval_s
et
order_num
ber
order_do
w
order_hour_of_
day
days_since_prior_or
der2539329 1 prior 1 2 8
2398795 1 prior 2 5 7 15
473747 1 prior 3 7 12 20
22544786 1 prior 4 1 7 21
4215438 1 prior 5 3 15 28
2295261 1 prior 6 2 7 19
2295261 1 prior 7 6 20 20
2550362 1 prior 8 5 14 14
1187899 1 prior 9 2 16 0
2168274 1 prior 10 2 8 30
1501582 1 train 11 1 11 10
Instacart Data - 2M order data

20. 2539329
2398785
473747
2550368
1187899
(Product ID)
Predicted product ID
• Our orders follow some pattern
Existing approach

21. Order time

22. Time 2Time 1 Time 3
LSTM (Recurrent Neural
Netowrk)

23. Time 1 Time 2
LSTM - Memory
TensorFlow library for LSTM

24. Predictive maintenance -
Industrial IoT

25. Long term Visionary
problems
• Approach to AI - Intuition building
• Future algorithms?

26. Reinforcement learning:
Building intuition

28. • AlphaGo was able to build
intuition.
• Train with NN and then let it
play with itself.
• Learning through
Reinforcement learning.
Intuition in machines

29. Future AI algorithms
• How can we assure that AI/AS are accountable?
• How can we ensure that AI/AS are transparent?
• How can we extend the benefits and minimize the risks of
AI/AS technology being misused?

30. – Eliezer Yudkowsky
“By far the greatest danger of Artificial Intelligence
is that people conclude too early that they
understand it.”