How AI works?

The video tutorial introduces artificial intelligence (AI) and its goal to create self-solving software. It distinguishes AI from machine learning, explaining that machine learning is a subset of AI. Traditional programming is contrasted with machine learning, which learns from data rather than explicit rules. The tutorial covers the basics of machine learning, including training phases and data-driven learning. It introduces deep learning, inspired by human brain neurons, and highlights its computational demands. Finally, it outlines machine learning tasks like classification, regression, and clustering.

What is the ultimate goal of AI as discussed in the video?

• To develop faster computers

• To create a software that can solve problems independently

• To replace human intelligence completely

• To automate all human tasks

How does traditional programming differ from machine learning?

• Machine learning does not involve any data

• Traditional programming involves explicit rule definition

• Machine learning requires explicit rule definition

• Traditional programming uses data to learn rules

In the training phase of machine learning, what is provided to the model instead of rules?

• The expected output

• The exact logic

• The programming language

• The hardware specifications

What is the role of data in machine learning according to Dr. Tom Mitchell?

• Data is used to write programs

• Data is irrelevant in machine learning

• Data helps machines improve through experience

• Data is only used for testing

What inspires deep learning algorithms?

• The architecture of traditional computers

• The design of modern software

• The structure of the internet

• The working of neurons in the human brain

Which of the following is NOT a task performed by machine learning?

• Classifying new data into existing categories

• Performing regression and predicting future data

• Clustering observed data into subsets

• Designing new computer hardware

What has enabled deep learning to become more feasible in recent years?

• Reduction in computational demands

• Advancements in computer technology

• Simplification of algorithms

• Decrease in data generation