How is knowledge engineering fundamentally different than machine learning?

Question 1: How is knowledge engineering fundamentally different than machine learning?

Question 2: Describe how Convolutional Neural Network work to overcome the difficulties that you mentioned in part A? Please make sure your description includes a discussion of the process of convolution and how it is represented in the architecture of a typical CNN.

Question 3:

Part A
What is deep learning and how would you distinguish it from “shallow” learning? How would you distinguish deep learning from “traditional” Artificial Neural Networks (ANN)?

Part B
Please give an example of situation where deep learning may not be appropriate and a situation where deep learning can be applied successfully? Based on the two examples you gave, can you describe broadly what makes a situation ideal for a deep learning-based solution?

Question 4:

Part A
How does human mind gets so much out of so little, so quickly, so flexibly, and on such little? What is the implication of this observation on developing cognitive systems?

Part B
Please describe Polanyi’s Paradox and Moravec’s Paradox. Discuss the implications of these two paradoxes on the development of cognitive systems.
Please justify your answers on parts A and B. You may want to use examples to contextually frame your argument.

Question 5:

Part A
What is the relationship between Intelligence and Cognition? Please define each and then discuss why understanding of this distinction is fundamental to the development of cognitive systems.
Part B
Cognitive systems have evolved substantially during the past couple of decades. What do you think has fueled this evolution and where do you think the next step in this evolutionary ladder will be? Look into your crystal ball, what do you see?

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