Module 1 - What is AI?

Question 1

What is Artificial Intelligence?

Answer 1

AI is the study of creating machines that think, learn, and act rationally. There are two approaches:

• human-centered AI (mimicking human cognition)
• rational AI (optimal decision-making).

Question 2

What is rationality in AI?

Answer 2

AI should be rational by making decisions that maximize success.

Human-centered approach: Observing human behavior to model intelligence.

Rationalist approach: Using logic and mathematics to define AI decision-making.

Question 3

The Turing test & total Turing test

Answer 3

Turing test: A machine is intelligent if a human cannot distinguish it from another human in conversation.

Total turing test: Adds perception (computer vision) and manipulation (robotics) as extra challenges.

Question 4

What are the six disciplines required to pass the total Turing test?

Answer 4

Natural language processing – understanding text/speech.
Knowledge representation – storing and retrieving facts.
Automated reasoning – drawing conclusions from facts.
Machine learning – improving performance over time.
Computer vision – understanding visual input.
Robotics – moving and manipulating objects.

Question 5

What is the difference between strong AI and weak AI?

Answer 5

Strong AI: aims to replicate full human intelligence (AGI).

Weak AI: designed for specific tasks (e.g., chatbots, recommendation systems).

Question 6

In Depth Q. The Turing Test has been criticized as an insufficient measure of intelligence. What are some key objections to it?

Answer 6

Chinese Room argument (Searle) – a machine could pass the test without truly understanding.
Consciousness issue – intelligence might require subjective experiences.
Behavior vs. Thought – passing the test proves imitation, not real thinking.

Question 7

In Depth Q. Why is achieving perfect rationality in AI often infeasible?

Answer 7

AI systems operate in complex environments where time and computational resources are limited. Instead, AI uses limited rationality by approximating the best decisions under constraints.

Question 8

What are the main concerns regarding AI safety?

Answer 8

1. AI could lead to autonomous weapons.
2. AI decisions may become unpredictable and dangerous.
3. Experts like Stephen Hawking warn AI could be a threat to humanity.

Question 9

How does John McCarthy define AI?

Answer 9

The science and engineering of making intelligent machines.”

Question 10

Why is it difficult to define intelligence in AI?

Answer 10

Intelligence can mean different things (problem-solving, reasoning, adapting, creativity).
Even in psychology, there is no universal definition of intelligence.

Question 11

Can AI be truly intelligent if it doesn’t have emotions?

Answer 11

Some argue emotions are necessary for decision-making (e.g., humans consider emotions in moral choices).
Others argue rationality alone is enough for intelligence.

Question 12

What does Stephen Hawking say about AI?

Answer 12

AI could spell the end of the human race

Hawking’s view reflects a significant concern about the potential dangers of AI.

Question 13

What is Elon Musk’s perspective on AI?

Answer 13

It’s like summoning the demon

Musk emphasizes the unpredictable and potentially harmful consequences of AI.

Question 14

What does Brett Kennedy believe about robots?

Answer 14

I have first-handed knowledge of how hard it is for us to make a robot that does much of anything

Kennedy’s experience highlights the challenges in AI development.

Question 15

How does Alan Winfield describe fears of superintelligent robots?

Answer 15

Fears are greatly exaggerated

Winfield suggests that concerns about AI taking over the world may not be justified.

Question 16

What is David Harding’s view on the debate about AI and machine learning?

Answer 16

Nine parts hype to one part substance

Harding critiques the disproportionate hype surrounding AI compared to its actual achievements.

Question 17

What is the main idea of Harding’s approach to finance?

Answer 17

Use science to identify and exploit inefficiencies in the markets

Harding advocates for a scientific approach to enhance financial strategies.

Question 18

What does Theodore Rosak claim about AI’s public deception?

Answer 18

AI’s record of barefaced public deception is unparalleled

Rosak points to a history of exaggerated claims in AI research.

Question 19

How does John McCarthy define Artificial Intelligence?

Answer 19

The science and engineering of making intelligent machines

McCarthy is one of the pioneers in AI and his definition emphasizes the engineering aspect.

Question 20

What is Marvin Minsky’s definition of AI?

Answer 20

The science of making machines do things that would require intelligence if done by men

Minsky highlights the goal of AI to mimic human intelligence.

Question 21

According to John Haugeland, what is AI?

Answer 21

The exciting new effort to make computers think.. machines with minds, in the full and literal sense

Haugeland focuses on the cognitive aspect of AI.

Question 22

What is Ray Kurzweil’s perspective on AI?

Answer 22

The art of creating machines that perform functions that require intelligence when performed by people

Kurzweil emphasizes the creative aspect of developing intelligent machines.

Question 23

What is the definition of intelligence according to John McCarthy?

Answer 23

The computational part of the ability to achieve goals in the world

McCarthy’s definition links intelligence to goal achievement.

Question 24

What does the term ‘artificial’ imply in the context of intelligence?

Answer 24

Non-biological, constructed by humans

The term distinguishes between biological and non-biological forms of intelligence.

Question 25

What is a key characteristic of the clean robot referred to as Nigel?

Answer 25

Forms a map and finds its way ## Footnote This behavior indicates that Nigel uses techniques to achieve a specific goal.

Question 26

Does Nigel learn or improve over time?

Answer 26

No, he doesn’t improve ## Footnote Nigel operates based on pre-programmed algorithms without learning.

Question 27

What was the aim of the Dartmouth Summer Research Project on Artificial Intelligence in 1956?

Answer 27

Every aspect of learning or any other feature of intelligence can in principle be so precisely defined that a machine can be made to simulate it ## Footnote This hypothesis laid the groundwork for future AI research.

Question 28

Is catching a mouse considered a sort of intelligence?

Answer 28

Yes, it reflects the ability to achieve goals ## Footnote This suggests that intelligence can manifest in various forms across species.

Question 29

What does the term 'hype' refer to in the context of AI?

Answer 29

Exaggerated claims about AI capabilities ## Footnote Hype can create unrealistic expectations about what AI can achieve.

Question 30

According to the text, what is a common skepticism regarding AI?

Answer 30

Too much hype, too less achieved ## Footnote This reflects a critical view on the actual progress made in AI compared to public perception.

Question 31

What is AI as a field of research?

Answer 31

AI is part of Cognitive Science.

Question 32

Why is Cognitive Science considered a collaborative/interdisciplinary field?

Answer 32

Understanding the mind/brain may be too difficult for a single discipline in isolation.

Question 33

What do researchers in cognitive science seek to understand?

Answer 33

Brain processes as computational systems which manipulate representations.

Question 34

What does the computational theory of mind propose?

Answer 34

The mind stores symbolic representations and manipulates them via syntactic operations.

Question 35

How do cognitive scientists view the human mind?

Answer 35

As a complex system that receives, stores, retrieves, transforms, and transmits information.

Question 36

What is the issue with the fragmentation of AI research?

Answer 36

Researchers don’t always have the same goals or ideas.

Question 37

How are big AI questions approached in research?

Answer 37

They are often viewed from different perspectives and fields.

Question 38

What was the trend in AI hype from 1980 to 1996?

Answer 38

AI hype started in 1980 and went down in 1996 due to slow development and high costs.

Question 39

What happened to machine learning hype during the decline of AI hype?

Answer 39

Machine learning hype went up.

Question 40

What research area gained attention as AI hype declined?

Answer 40

Neural networks gained attention as more research was done on how they worked.

Question 41

Fill in the blank: Cognitive scientists view the human mind as a complex system that _______.

Answer 41

receives, stores, retrieves, transforms, and transmits information.

Question 42

True or False: Most AI research tackles the big AI questions directly.

Answer 42

False.

Question 43

What is a consequence of the fragmentation of AI research?

Answer 43

Most research is just done on a little part of the big question.

Question 44

What was the trend for expert systems during the AI hype decline?

Answer 44

Expert systems went down.

Question 45

What is a primary focus for further AI research based on neural networks?

Answer 45

Modeling after the human brain.

Question 46

What is Strong AI?

Answer 46

Goal: To understand the human mind/brain as a computational device. Specific: Focused on the human mind and replicating its functions. General: Humans can solve a wide range of problems. Belief: Machines can be built with thought, emotions, and human-like reasoning.

Question 47

How does Weak AI differto Strong AI in its approach?

Answer 47

It aims to solve problems using AI, without necessarily replicating human-like intelligence.

Question 48

What is Weak AI?

Answer 48

Goal: To develop intelligent machines that solve problems but are not necessarily human-like. General: Can involve any AI technique or problem-solving approach. Specific: Typically focuses on one task (e.g., vision, machine learning). Doesn’t focus on theories of intelligence—only cares about solving problems.

Question 49

Why is Strong AI considered "specific" while Weak AI is "general"?

Answer 49

Strong AI is specific because it focuses on human intelligence only. Weak AI is general because it considers any type of AI technique.

Question 50

In Depth Q. Could Weak AI eventually lead to Strong AI? Why or why not?

Answer 50

Some argue that advancing weak AI could accumulate knowledge and eventually lead to Strong AI. Others believe Weak AI lacks consciousness and true understanding, making Strong AI impossible.

Question 51

In Depth Q. Which AI approach (Strong or Weak) is more useful today? Why?

Answer 51

Weak AI is more useful today because it powers real-world applications like chatbots, self-driving cars, and recommendation systems. Strong AI is still a theoretical concept, with no known implementations.

Question 52

What is AI as Science?

Answer 52

Goal: Reverse-engineer the human mind/brain. The object of interest exists in nature (e.g., studying human intelligence). AI as a science might fail if it cannot fully understand human intelligence. Depends on humans to study and model intelligence.

Question 53

What is AI in Engineering?

Answer 53

Goal: Engineer clever machines, regardless of human cognition. The object of interest is unknown and must be discovered or created. AI as engineering has already succeeded in applications like automation and robotics. Independent of humans—focuses on solving problems, not replicating human thought.

Question 54

What is an example of AI as science?

Answer 54

Studying how neurons in the brain work to create biologically inspired AI.

Question 55

What is an example of AI as engineering?

Answer 55

Creating self-driving cars or AI-powered robots without human-like reasoning.

Question 56

In Depth Q. Can AI as engineering exist without AI as science? Why or why not?

Answer 56

AI as engineering can succeed without science because it focuses on practical solutions (e.g., speech recognition, computer vision). However, AI science helps improve AI engineering by advancing understanding of cognition and learning processes.

Question 57

In Depth Q. Why is AI as science considered more uncertain than AI as engineering?

Answer 57

AI as science depends on whether human intelligence can be fully understood—which is still unknown. AI as engineering already works because machines solve tasks without requiring human-like thought.