The document provides an introduction to artificial intelligence (AI) with the following key points: 1. It defines intelligence as the capacity for logic, understanding, learning, problem solving, and more. AI is defined as computer systems that can perform tasks requiring human intelligence like visual perception and decision-making. 2. There are different types of AI like machine learning, deep learning, supervised learning, unsupervised learning, and reinforcement learning. Machine learning allows systems to learn from data rather than through explicit programming. 3. Deep learning is a type of machine learning inspired by the brain that uses neural networks to learn representations of data. Supervised learning uses labeled input-output data to learn general rules while unsupervised learning

1. Introduction to Artificial Intelligence (AI)
Santosh Kumar
@kumsantosh
https://www.linkedin.com/in/kumsantosh/
https://www.facebook.com/instructscience/

2. Top 10 Strategic Technology Trends for 2017

3. What’s in for You ?

4. Intelligent Apps and intelligent Things:
Intelligence Anywhere and Everywhere

5. Digital Technology Platforms:
Conversational AI-Powered Platforms

6. Future Trends In The Gartner Hype Cycle
For Emerging Technologies, 2017

7. Agenda
1 Evolution
2 Artificial intelligence
3 Machine Learning
4 Deep Learning
5 Q & A
Agenda

8. Cosmic evolution
Evolution of life on the Earth

9. The Cambrian Explosion

10. Anatomy and Function of the Brain

11. Definition of Intelligence and AI
– Intelligence: Intelligence has been defined
in many different ways including as one's
capacity for logic, understanding, self-
awareness, learning, emotional knowledge,
planning, creativity, and problem solving.
– Artificial Intelligence: The theory and
development of computer systems able to
perform tasks normally requiring human
intelligence, such as visual perception,
speech recognition, decision-making, and
translation between languages

12. Types of AI

13. What is AI?
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14. Machine learning model

15. What changed?

16. Tribes of Machine Learning

17. Inspired by our brain

18. How does it mimic brain?

19. Types of machine learning

20. Deep Learning - Introduction

21. Supervised learning
• Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to
learn the mapping function from the input to the output.
• Y = f(X)
• the correct classes of the training data are known
• Examples – Email labelling spam/not-spam, face or character detection.
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-analytics-artificial-intelligence-science-fiction-e-discovery-truth

22. How does it actually work?

23. Unsupervised Learning
• Unsupervised learning is where you only have input data (X) and no corresponding output variables.
Algorithms are left to their own devises to discover and present the interesting structure in the data.
• the correct classes of the training data are not known
• Examples – eCommerce websites recommendations, friends suggestions on FB, etc.
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-analytics-artificial-intelligence-science-fiction-e-discovery-truth

24. Reinforcement Learning
• Allows the machine or software agent to learn its behavior based on feedback
from the environment.
• This behavior can be learnt once and for all, or keep on adapting as time goes by.
• Examples - Robots
Credit: http://us.hudson.com/legal/blog/postid/513/predictive-analytics-artificial-intelligence-science-fiction-e-discovery-truth

25. Key takeaways

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