Frequently asked questions

Here is a small recap of what we covered in the chapter:

Q: What is a results pyramid?

A: The results we get (man or machine) are an outcome of our experiences (data), beliefs (models), and actions. If we need to change the results, we need different (better) sets of data, models, and actions. 

Q: How is this paradigm applicable to AI and Big Data?

A: In order to improve our lives, we need intelligent systems. With the advent of Big Data, there has been a boost to the theory of machine learning and AI due to the availability of huge volumes of data and increasing processing power. We are on the verge of getting better results for humanity as a result of the convergence of machine intelligence and Big Data. 

Q: What are the basic categories of Big Data frameworks?

A: Based on the differentials between the event time and processing time, there are two types of framework: batch processing and real-time processing. 

Q: What is the goal of AI?

A: The fundamental goal of AI is to augment and complement human life.

Q: What is the difference between machine learning and AI?

A: Machine learning is a core concept which is integral to AI. In machine learning, the conceptual models are trained based on data and the models can predict outcomes for the new datasets. AI systems try to emulate human cognitive abilities and are context sensitive. Depending on the context, AI systems can change their behaviors and outcomes to best suit the decisions and actions the human brain would take.

Have a look at the following diagram for a better understanding: