set-1

Answer: 4. All of mentioned above

Explanation:

• Intelligence involves the ability to acquire knowledge, think, reason, and solve problems.
• It also includes superior mental capabilities and the ability to apply knowledge in various situations.

Answer: 4. All of mentioned above

Explanation:

• AI is designed to mimic human intelligence, which includes problem-solving, rational decision-making, and acting in ways that resemble human behavior.
• These abilities collectively define the scope of AI.

Answer: 4. All of the mentioned above

Explanation:

• AI involves reasoning, knowledge representation, planning, learning, and communication.
• It also includes perception and the ability to interact with the environment.
• These principles collectively form the foundation of AI.

Answer: 1. Making a machine Intelligent

Explanation:

• AI focuses on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
• The goal is to make machines intelligent, not to transfer human intelligence into machines.

Answer: 3. Artificial Intelligence is a field that aims to develop intelligent machines

Explanation:

• AI is a branch of computer science that focuses on creating machines capable of performing tasks that require human intelligence.
• The primary goal is to develop intelligent machines, not to enhance human intelligence or focus solely on data mining or security.

Answer: 2. John McCarthy

Explanation:

• John McCarthy is widely recognized as the father of Artificial Intelligence.
• He coined the term “Artificial Intelligence” in 1956 and made significant contributions to the field.

Answer: 2. Intelligent

Explanation:

• An intelligent machine can adapt its behavior based on changes in the environment.
• This ability to learn and make decisions autonomously is a hallmark of intelligence.

Answer: 3. Machine Learning

Explanation:

• Machine Learning is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data.
• It is one of the core branches of AI.

Answer: 3. To solve real-world problems

Explanation:

• The primary goal of AI is to develop systems that can solve real-world problems efficiently.
• AI aims to mimic human intelligence to address practical challenges.

Answer: 4. Language understanding and problem-solving (Text analytics and NLP)

Explanation:

• Natural Language Processing (NLP) is a key application of AI that involves understanding and generating human language.
• NLP enables machines to interact with humans in a natural way, solving problems related to language understanding.

Answer: 2. Categorized into 3 categories

Explanation:

• The processes in AI are generally categorized into 3 main types: supervised learning, unsupervised learning, and reinforcement learning.
• These categories are based on how the AI system learns from data.

Answer: 4. Both A and C

Explanation:

• AI is categorized based on functionality (e.g., reactive machines, limited memory, theory of mind, self-awareness).
• It is also categorized based on capability (e.g., narrow AI, general AI, super AI).

Answer: 3. Containerization

Explanation:

• Containerization is a technology used in software development and deployment, such as Docker.
• It is not directly related to AI, unlike computer vision, NLP, and image recognition.

Answer: 4. All of the mentioned above

Explanation:

• AI faces several challenges, including the black box problem (lack of transparency in decision-making).
• Algorithm bias (biased outcomes due to biased data).
• High computing power requirements for training and running AI models.

Answer: 1. Myth

Explanation:

• While AI will automate certain tasks, it is unlikely to completely take over jobs.
• Instead, it will transform the nature of work, creating new opportunities and requiring humans to adapt.

Answer: 4. All of mentioned above

Explanation:

• Acumos AI, ClearML, and H2O.ai are all open-source AI platforms.
• They provide tools for developing and deploying AI models.

Answer: 4. PHP

Explanation:

• LISP, PROLOG, and Python are commonly used in AI development.
• PHP is primarily used for web development and is not commonly associated with AI.

Answer: 1. Agent

Explanation:

• An agent in AI is an entity that perceives its environment through sensors and acts upon it through actuators to achieve specific goals.
• This definition aligns with the concept of an intelligent agent.

Answer: 2. Agent function

Explanation:

• The agent function maps percept histories (sequences of percepts) to actions.
• This function defines the behavior of the agent based on its perception of the environment.

Answer: 4. Agent Program

Explanation:

• The agent program runs on the physical architecture to implement the agent function $f: P^* \rightarrow A$.
• It determines how the agent behaves based on its percepts.

Answer: 2. Rational Agent

Explanation:

• A rational agent selects actions that maximize its performance measure.
• It uses percept sequences and built-in knowledge to make optimal decisions.

Answer: 4. All of the mentioned

Explanation:

• An agent perceives its environment through sensors and acts upon it through actuators.
• It uses intelligence to perform operations and can be implemented as an embedded program, such as in a line-following robot.

Answer: 4. Agent function

Explanation:

• The behavior of an agent is best described by its agent function, which maps percept sequences to actions.
• This function defines how the agent responds to its environment.

Answer: 4. Architecture and Program

Explanation:

• An agent is composed of both architecture (the physical hardware) and program (the software that implements the agent function).
• Together, they define the agent’s structure and behavior.

Answer: 1. True

Explanation:

• A rational agent is designed to always select actions that maximize its performance measure.
• It consistently does the “right thing” based on its knowledge and percepts.

Answer: 1. True

Explanation:

• Performance measures vary depending on the agent’s goals and environment.
• Different agents may have different criteria for success.

Answer: 4. All of these

Explanation:

• The task environment includes sensors, performance measures, and actuators.
• These components define how the agent interacts with and evaluates its environment.

Answer: 4. All of these

Explanation:

• Rationality depends on the agent’s possible actions, its knowledge of the environment, and the performance measure.
• These factors collectively determine the agent’s decision-making process.

Answer: 2. Problem and Solution

Explanation:

• The task environment and rational agents in AI involve identifying problems and finding solutions.
• Rational agents aim to solve problems within their environment.

Answer: 3. Performance, Environment, Actuators, Sensors

Explanation:

• PEAS stands for Performance, Environment, Actuators, Sensors.
• It is a framework used to define the task environment of an agent.

Answer: 2. Environment Generator

Explanation:

• An environment generator is used to create or select a specific environment for the agent to operate in.
• It allows for controlled testing and simulation of the agent’s behavior.

Answer: 1. Comfortable trip

Explanation:

• For a taxi driver agent, the performance measure would likely focus on providing a comfortable and efficient trip for passengers.
• Other options like roads, steering wheels, and cameras are part of the environment, not the performance measure.

Answer: 4. Both B and C

Explanation:

• The environment for a medical diagnosis system includes patients and the hospital setting.
• These are the contexts in which the system operates.

Answer: 3. Camera

Explanation:

• A part-picking robot would likely use cameras as sensors to identify and locate parts.
• Bins and arms are part of the robot’s physical structure, not sensors.

Answer: 3. 2

Explanation:

• There are two types of observing environments in AI: fully observable and partially observable.
• This classification depends on whether the agent can perceive the entire state of the environment.

Answer: 2. 5

Explanation:

• There are five basic types of agents in AI: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents.
• These types differ in their complexity and decision-making processes.

Answer: 4. Table driven agent

Explanation:

• The agent uses a look-up table to map percepts to actions, which is characteristic of a table-driven agent.
• This type of agent relies on pre-defined mappings rather than complex reasoning.

Answer: 1. Simple reflex agent

Explanation:

• The agent uses condition-action rules to directly map percepts to actions, which is characteristic of a simple reflex agent.
• This type of agent does not maintain an internal model of the world.

Answer: 2. Model-based reflect agent

Explanation:

• The agent maintains an internal state and uses it to track how the world evolves, which is characteristic of a model-based reflex agent.
• This type of agent can handle partially observable environments.

Answer: 3. Goal-based agent

Explanation:

• The agent considers goals and evaluates the outcomes of its actions, which is characteristic of a goal-based agent.
• This type of agent aims to achieve specific objectives.

Answer: 1. Utility-based agent

Explanation:

• The agent evaluates actions based on a utility function, which measures how “happy” it will be in a given state.
• This is characteristic of a utility-based agent.

Answer: 4. Learning agents

Explanation:

• The agent includes a learning element and a critic to improve its performance over time, which is characteristic of a learning agent.
• This type of agent adapts based on feedback and experience.

Answer: 4. Utility based agent

Explanation:

• A utility-based agent evaluates states based on a utility function, which measures how “happy” or “unhappy” the agent is in a given state.
• This allows the agent to make decisions that maximize its overall happiness.

Answer: 3. Learning agent

Explanation:

• A learning agent includes a problem generator to explore new actions and improve its performance over time.
• This component helps the agent learn from its environment.

Answer: 2. Condition-action rule

Explanation:

• A simple reflex agent operates using condition-action rules, which directly map percepts to actions.
• These rules are pre-defined and do not involve complex reasoning.

Answer: 2. Current perception

Explanation:

• A simple reflex agent bases its actions solely on the current perception of the environment.
• It does not consider past perceptions or future consequences.

Answer: 3. Both A and B

Explanation:

• An agent can use both relaxed and relational architectures depending on the task and environment.
• These architectures provide flexibility in how the agent processes information and makes decisions.

Answer: 2. Fully observable

Explanation:

• If an agent’s sensors provide complete access to the environment’s state at all times, the environment is fully observable.
• This means the agent has all the information it needs to make decisions.

Answer: 2. Fully observable

Explanation:

• In a chess game, the player can see the entire board, making the environment fully observable.
• The player has complete information about the game state at all times.

Answer: 1. Partially observable

Explanation:

• In a poker game, a player can only see their own cards, making the environment partially observable.
• The player does not have complete information about the game state.