Cover Page

Advances in Information Systems Set

coordinated by
Camille Rosenthal-Sabroux

Volume 8

Artificial Intelligence and Big Data

The Birth of a New Intelligence

Fernando Iafrate

Wiley Logo

List of Figures

  1. Figure 1. Identity resolution
  2. Figure I.1. “Digital assimilation”
  3. Figure I.2. The traces we leave on the Internet (whether voluntarily or not) form our Digital Identity
  4. Figure I.3. Number of connected devices per person by 2020
  5. Figure 1.1. Diagram showing the transformation of information into knowledge
  6. Figure 1.2. Business Intelligence evolution cycle
  7. Figure 1.3. The Hadoop MapReduce process
  8. Figure 2.1. Volume of activity per minute on the Internet
  9. Figure 2.2. Some key figures concerning connected devices
  10. Figure 2.3. Supervised learning
  11. Figure 2.4. Supervised learning
  12. Figure 2.5. Enhanced supervised learning
  13. Figure 2.6. Unsupervised learning
  14. Figure 2.7. Neural networks
  15. Figure 2.8. Example of facial recognition
  16. Figure 3.1. The artificial neuron and the mathematical model of a biological neuron
  17. Figure 3.2. X1 and X2 are the input data, W1 and W2 are the relative weights (which will be used as weighting) for the confidence (performance) of these inputs, allowing the output to choose between the X1 or X2 data. It is very clear that W (the weight) will be the determining element of the decision. Being able to adapt it in retro-propagation will make the system self-learning
  18. Figure 3.3. Example of facial recognition
  19. Figure 3.4. Big Data and variety of data
  20. Figure 4.1. Markess 2016 public study
  21. Figure 4.2. What is CXM?
  22. Figure 4.3. How does the autonomous car work?
  23. Figure 4.4. Connected medicine
  24. Figure 4.5. A smart assistant in a smart home
  25. Figure 4.6. In this example of facial recognition, the layers are hierarchized. They start at the top layer and the tasks get increasingly complex
  26. Figure 4.7. The same technique can be used for augmented reality (perception of the environment), placing it on-board a self-driving vehicle to provide information to the automatic control of the vehicle
  27. Figure 4.8. Recommendations are integrated into the customer path through the right channel. Customer contact channels tend to multiply rather than replace each other, forcing companies to adapt their communications to each channel (content format, interaction, language, etc.). The customer wishes to choose their channel and be able to change it depending on the circumstances (time of day, location, subject of interest, expected results, etc.)
  28. Figure 4.9. Collaborative filtering, step by step. In this example, we can see that the closest “neighbor” in terms of preferences is not interested in videos, which will inform the recommendation engine about the (possible) preferences of the Internet user (in this case, do not recommend videos). If the user is interested in video products, models (based on self-learning) will take this into account when browsing, and their profile will be “boosted” by this information
  29. Figure 4.10. Mapping of start-ups in the world of Artificial Intelligence