An increasing number of our personal and business interactions are impacted by artificial intelligence (AI) and on the near horizon are increasingly sophisticated machine learning and deep learning technologies. Now is the right time to get a grasp of the core concepts and understand how these ideas and functionalities are changing our lives and impacting our businesses. In this two-hour workshop, presented in partnership with the University of Chicago, you will gain an appreciation of the history and recent acceleration of AI in the industry, get a solid understanding of the core concepts and modern building blocks for AI-driven services. Via small group exercises you will work with your peers to identify some practical applications for AI in your workplace. And you will have a chance to explore some fun questions around AI such as "Is there a master algorithm?", "Can AI build AI?" and "When will I be replaced by a robot?

2. First hour
● Brief history
● Core concepts
● Example applications
Break: 10 mins
Second hour
● Group activity
● Discussion, Open questions and Q&A

4. Source: Peter Morgan/Reuters

5. Source: Wikipedia

6. Source: Google, Amazon, Facebook, Tesla

11. Source: Chatbots Magazine, Wikipedia

14. Source: artint.info, sebastianraschka.com, Wikipedia,

15. Source: artint.info, sebastianraschka.com, Wikipedia,

16. Source: artint.info, sebastianraschka.com, Wikipedia,

17. Source: artint.info, sebastianraschka.com, Wikipedia,

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19. Source: igcseict.info

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23. Source: Peter Morgan/Reuters

24. Source: Peter Morgan/Reuters

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26. Source: mathworks.com, amazon.com
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27. Source: mathworks.com, amazon.com
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28. Source: mathworks.com, amazon.com
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29. Source: mathworks.com, amazon.com
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39. Support
Vector
Machines
Matrix
Factorization

40. Support
Vector
Machines
Matrix
Factorization

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52. Source: artint.info, sebastianraschka.com, Wikipedia,

58. Input Output

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70. Input Model Output

71. Input Model Output
Expected
Output
Loss function

72. Input Model Output
Expected
Output
Loss function

73. Input Model Output
Expected
Output
Loss function

74. Input Model Output
Expected
Output
Loss function
Trained
model

75. Input Model Output
Expected
Output
Loss function
Input
Trained
model
Output

77. Input
Trained
model
Output
Expected
Output
Loss function

78. ● Large-scale deployment
● Distributed computing
● Integration with Google
Cloud
● Faster than TensorFlow
● Easier to experiment with
nonstandard neural network
architectures
● Faster than TensorFlow
● Integration with AWS

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99. Visual Question Answering
Q: How symmetrical are the white bricks on either side of the building?
A: very
99

100. Visual Question Answering
Q: How symmetrical are the white bricks on either side of the building?
A: very
Q: How asymmetrical are the white bricks on either side of the building?
A: very
100

101. Visual Question Answering
Q: How symmetrical are the white bricks on either side of the building?
A: very
Q: How asymmetrical are the white bricks on either side of the building?
A: very
Q: How big are the white bricks on either side of the building?
A: very
101

102. Visual Question Answering
Q: How symmetrical are the white bricks on either side of the building?
A: very
Q: How asymmetrical are the white bricks on either side of the building?
A: very
Q: How big are the white bricks on either side of the building?
A: very
Q: How spherical are the white bricks on either side of the building?
A: very
102

103. Visual Question Answering
Q: How symmetrical are the white bricks on either side of the building?
A: very
Q: How asymmetrical are the white bricks on either side of the building?
A: very
Q: How big are the white bricks on either side of the building?
A: very
Q: How spherical are the white bricks on either side of the building?
A: very
Q: How fast are the bricks speaking on either side of the building?
A: very
103