Spot, developed by Boston Dynamics, is a four-legged ground robot designed to navigate various terrains, including stairs and rough surfaces. It is used in industrial applications like inspection, exploration, and carrying payloads. Spot's ability to adapt to different environments makes it highly versatile.
How does Spot's ability to navigate rough terrain enhance its usability in industrial applications?
Answer
Spot's ability to move on rough and uneven terrain (like stairs, rubble, slippery floors and narrow paths) increases its industrial usability because it can reach places that are difficult or unsafe for humans and wheeled machines. Therefore, it can carry out inspection and exploration in factories, plants and construction sites, and can also carry tools/sensors or small payloads. This improves efficiency, reduces downtime, and increases worker safety in hazardous areas.
Robots that work in hazardous environments, such as chemical plants or oil rigs, can perform dangerous tasks like handling toxic materials, cutting, or welding. These robots are controlled remotely, allowing humans to prevent themselves from any harm while completing these dangerous tasks.
How do robots improve safety in hazardous work environments, such as chemical plants or nuclear reactors?
Answer
Robots improve safety in hazardous work environments, such as chemical plants or oil rigs, by performing dangerous tasks in place of humans. Robots can work in hazardous environments, handle toxic materials, and perform tasks involving sharp objects, welding, or extreme temperatures without risking human life. They can be remotely controlled or operate autonomously, which minimises human exposure to radiation, chemicals, explosions, or other life-threatening conditions. This significantly reduces the chances of accidents, injuries, and health risks to humans.
A factory uses industrial robots for high-precision tasks. These robots are programmed to perform tasks like welding and cutting, ensuring consistent quality. Which feature of these robots allows them to perform these tasks with high accuracy and minimal error?
- They are equipped with infrared sensors.
- They operate in extreme environments.
- They have a high degree of precision.
- They require continuous human guidance.
Answer
They have a high degree of precision.
Reason — Robots are capable of performing tasks with a high degree of accuracy and precision, which allows them to repeat the same task consistently without making mistakes. This feature enables industrial robots to carry out high-precision tasks such as welding and cutting while ensuring minimal error and consistent quality.
Consider a robot used in military applications to handle hazardous materials like explosives. Which feature is most essential for this robot to ensure human safety while performing such critical tasks?
- Autonomous decision-making abilities
- High durability and strength for rough terrains
- Sensors to detect temperature changes
- Remote control for human operators to stay at a safe distance
Answer
Remote control for human operators to stay at a safe distance
Reason — Robots used in hazardous and dangerous environments can be remotely controlled, which allows humans to operate them from a safe distance. This reduces direct human exposure to explosives, toxic materials, radiation, or other life-threatening risks, thereby ensuring human safety during critical military operations.
Sensors are vital components of robots, enabling them to detect environmental changes. These devices gather data related to temperature, pressure, light, sound, and distance, which are used by robots to detect obstacles, navigate, and make decisions. Common types of sensors include temperature sensors, ultrasonic sensors, and proximity sensors. These sensors act as input devices, providing essential information that helps robots function autonomously in different settings.
Mention two types of sensors used in robots and their functions.
Answer
Two types of sensors used in robots and their functions are:
- Temperature Sensors – These sensors are used to detect changes in temperature in the environment. They help robots take appropriate decisions based on temperature variations, such as sensing a rise in temperature and performing necessary actions in fire-control robots.
- Ultrasonic Sensors – These sensors are used to detect the presence of objects or obstacles using sound waves. They help robots navigate from one place to another by identifying obstacles through the transmission and reflection of sound waves.
Robots are designed differently depending on the tasks they are intended to perform. For example, underwater robots are built to handle extreme conditions of the ocean depths, while aerial robots (drones) are designed for flight and various aerial tasks. Similarly, mobile robots are intended for autonomous navigation in diverse environments such as warehouses, hospitals, and outdoor areas.
What are the key design considerations when creating a robot for a specific environment, such as underwater or aerial tasks?
Answer
Underwater robots are designed to work in the extreme depths of oceans. So, by keeping this in consideration, underwater robots should have the following design features:
Pressure Resistant Mechanism – While working in the depths of oceans, the robots should have a pressure resistant mechanism to handle the extreme water pressure.
Buoyancy Control – The robot should be able to control its buoyancy. It can be achieved by using different buoyancy control mechanisms.
Propulsion System – Underwater robots should have a propulsion system to help them move in water. It can be achieved by using thrusters, propellers, or other propulsion systems.
Cameras and Sensors – A robot consists of cameras and sensors, which help it to navigate in underwater environment. The used sensors may be sonar, depth sensors, or any other imaging devices.
Manipulator Arms – Underwater robots use manipulator arms to perform various tasks, like collecting objects, manipulating objects, mining, or repairing.
Communication System – A communication system is required in underwater robots to communicate with their operators. It may be achieved by using wireless or tethered cable communication systems.
Power Source – To operate the underwater robots, a power source is required. Waterproof batteries and cells are used.
Control System – A robust control system is required to control the operations of underwater robots.
Materials – A robot should be made up of a material which is able to handle environmental factors in the ocean. Underwater robots are made up of titanium, stainless steel, and polymers
A humanoid robot is designed to interact with its environment, mimicking human-like movements and tasks. The robot's arms and legs move using motors and sensors. Which component is responsible for translating electrical signals into physical movements, such as arm rotation or leg motion?
- Controller
- Actuators
- Sensors
- Manipulators
Answer
Actuators
Reason — Actuators are the devices that convert electrical signals into physical movements. These movements are used to control the motion of a robot’s arms, legs, wheels, or grippers, enabling actions such as arm rotation and leg motion in humanoid robots.
In an industrial setting, a robotic arm performs precise tasks, such as assembling small parts. To create movements of the robotic arm, which actuator would be most effective for controlling precise rotations and fine adjustments?
- Hydraulic actuator
- Pneumatic actuator
- Electric actuator
- Piezoelectric actuator
Answer
Piezoelectric actuator
Reason — Piezoelectric actuators are used for small-scale applications where accurate solutions are required. They are suitable for ultra-precise positioning systems, making them most effective for precise rotations and fine adjustments in tasks such as assembling small parts in an industrial robotic arm.
Two-dimensional motion enables a robot to move in a plane, typically along both the X and Y axes. This allows the robot to move forward, backward, left, right, and diagonally. Robots that can navigate in two dimensions are more versatile and can interact within a broader workspace, making them ideal for more complex applications.
What advantages does two-dimensional motion provide to robots, and in which directions can they move within this plane?
Answer
Two-dimensional motion provides several advantages to robots by allowing them to move and navigate on a two-dimensional plane. Robots with two-dimensional motion can combine linear and rotational movement along the X and Y axes, which increases their ability to perform tasks efficiently.
Advantages of two-dimensional motion:
- It allows robots to move and interact within a wider workspace.
- Robots can navigate surfaces, avoid obstacles, and perform tasks such as transportation, inspection, and surveillance.
- Two-dimensional motion makes robots more versatile and suitable for complex applications compared to one-dimensional motion.
Directions of movement within the plane:
- Forward
- Backward
- Left
- Right
- Diagonally
The degree of freedom (DoF) refers to the number of independent movements a robot can perform. The DoF of a robot is determined by the number of joints and their specific design. Each joint adds a degree of freedom, allowing the robot to move or rotate along a particular axis.
What is meant by the degree of freedom (DoF) in robots, and how is it determined?
Answer
The Degree of Freedom (DoF) in robots refers to the number of independent movements a robot can perform. It indicates the different ways in which a robot or a robotic arm can move or rotate in space.
The DoF of a robot is determined by its mechanical design and the number of joints present. Each joint contributes one degree of freedom, allowing the robot to move or rotate along a particular axis. Therefore, a robot with more joints has a higher degree of freedom, which provides greater flexibility and range of motion.
In a robot designed to move in a circular pattern while maintaining a constant distance from a fixed point, which type of motion does the robot primarily exhibit?
- Linear motion
- Angular motion
- Circular motion
- Two-dimensional motion
Answer
Circular motion
Reason — Circular motion is defined as the movement of an object along a circular path while maintaining a constant distance from a fixed point, axis, or centre. Since the robot moves in a circular pattern and keeps a constant distance from a fixed point, it primarily exhibits circular motion.
In a robot designed to navigate within a two-dimensional plane and make directional changes, which combination of motions would be essential for its functionality?
- One-dimensional motion and circular motion
- Angular motion and linear motion
- Linear motion and two-dimensional motion
- Circular motion and two-dimensional motion
Answer
Angular motion and linear motion
Reason — Two-dimensional motion in robots employs both linear and rotational (angular) mobility along the X and Y axes, allowing the robot to move and make directional changes within a plane. Linear motion enables straight movement, while angular motion allows turning or changing direction. Hence, the essential combination is angular motion and linear motion.
The Turing test, proposed by Alan Turing in 1950, is designed to assess the intelligence of machines. In this test, an evaluator converses with both a human and a machine. If the evaluator cannot distinguish between the responses of human and the machine, the machine is said to have passed the Turing test.
What is the Turing test, and how does it determine whether a machine possesses human-like intelligence?
Answer
The Turing Test was proposed by Alan Turing in 1950 to test the intelligence of a machine. In this test, an evaluator has a conversation with a human and a machine simultaneously. The evaluator checks the responses given by both. If the evaluator finds that there is no difference between the responses generated by the human and the machine, then it is considered that the machine has passed the Turing test, indicating that the machine shows human-like intelligence.
Ethics in AI refers to the moral implications and principles that guide the use of AI technologies. Issues such as bias, fairness, accountability, and transparency are essential to address when developing AI systems. Ethical considerations ensure AI is used responsibly, minimising harm and maximising benefits for society.
Mention two ethical issues in AI with their significance.
Answer
Two ethical issues in AI and their significance are:
Bias – Bias in AI refers to the tendency of an AI system to give results based on data that is not collected fairly. If biased data is used to train an AI system, it may produce unfair or partial outcomes, leading to discrimination against certain individuals or groups. Addressing bias is significant to ensure fair and just decision-making in AI systems.
Accountability – Accountability refers to who is responsible for the decisions and outcomes of AI systems, including their creators, operators, and organisations. It is significant because it ensures that there are clear guidelines of responsibility in case AI systems cause harm or make incorrect decisions, and it helps in monitoring, auditing, and improving AI systems for safe and reliable use.
Natural language processing (NLP) algorithms are crucial in AI systems, such as chatbots, that simulate human conversation. These AI systems rely on NLP to understand and respond to human language. The Turing test is an important measure of how effectively these systems interact with humans.
What is the main purpose of using the Turing test in AI development?
- To improve the security of AI systems
- To assess how AI systems handle sensitive data
- To evaluate if AI systems can pass for conversation with humans
- To train AI systems on large data sets
Answer
To evaluate if AI systems can pass for conversation with humans
Reason — The Turing Test is used in artificial intelligence to test the intelligence of a machine. In this test, an evaluator has a conversation with both a human and a machine. If the evaluator cannot distinguish between the responses of the human and the machine, the machine is considered to have passed the Turing Test. Since AI systems such as chatbots use Natural Language Processing (NLP) to understand and respond to human language, the Turing Test helps in evaluating whether an AI system can simulate human-like conversation effectively.
Which of the following is an important ethical consideration in AI that relates to ensuring fairness and accountability?
- The development of Natural Language Processing tools
- Bias, prejudice, and fairness in AI decision-making
- The transparency of AI algorithms
- The effectiveness of AI in healthcare applications
Answer
Bias, prejudice, and fairness in AI decision-making
Reason — Bias, prejudice, and fairness are important ethical issues in AI. Bias and prejudice can lead to unfair or discriminatory decisions, while fairness ensures equal treatment of individuals or groups. Addressing these issues is essential to maintain accountability and responsible use of AI systems, so that AI decisions are just, transparent, and do not harm society.
To acquire relevant data for AI projects, methods such as surveys, observations, and web scraping are used. For instance, a company might use online surveys to gather data on customer preferences, which can then be used to enhance AI systems in e-commerce.
What is the role of surveys and web scraping in acquiring data for AI projects? How do these methods contribute to improving AI-based systems?
Answer
Surveys and web scraping play an important role in acquiring relevant data for AI projects.
- Survey – Surveys are used to collect data directly from users by asking them a set of questions. Surveys can be conducted online or offline using tools such as web forms or emails, where user's responses are recorded as text, audio, or video. This helps in understanding user opinions, preferences, and behaviour, which can be used to train AI systems and improve decision-making, such as enhancing recommendation systems in e-commerce.
- Web Scraping – With web scraping, useful information can be collected from websites and other online tools. Scripts or scraping tools are used to crawl web pages and extract structured or unstructured data, which can be used for AI project development.
Data visualisation techniques, such as bar graphs, scatter plots, and heat maps, are used to explore and understand large datasets. These visual representations help in identifying patterns and making decisions based on the data's characteristics.
Why is data visualisation important in AI projects? How does it help in understanding complex data patterns and relationships?
Answer
Data visualisation techniques allow the graphical representation of data in the form of charts, graphs, or diagrams. Data visualisation can reveal patterns, trends, or relationships within the data, making it simpler to recognise important features and understand the data.
By using techniques such as bar graphs, scatter plots, histograms, heat maps, and line graphs, data visualisation helps developers to:
- Identify important features in the data
- Understand the characteristics of the dataset
- Detect patterns, trends, or anomalies
- Support better decision-making during AI model development
How is data different from information in the context of decision-making?
- Data and information are the same in decision-making.
- Data becomes information only when it is processed and holds meaning or context.
- Data is always more useful than information.
- Information refers only to numerical data while data includes all forms of content.
Answer
Data becomes information only when it is processed and holds meaning or context.
Reason — Data refers to raw or unprocessed facts that do not have meaning or context on their own. When this data is processed, organised, or analysed, it becomes information, which is meaningful and useful for decision-making. Therefore, data is converted into information only after processing gives it context and significance.
What would happen if AI models were trained with insufficient or poor-quality data?
- The AI model would likely provide highly accurate predictions.
- The AI model would not work at all.
- The performance and accuracy of the AI model would be negatively impacted.
- The AI model would become faster but less accurate.
Answer
The performance and accuracy of the AI model would be negatively impacted.
Reason — AI algorithms require a huge amount of relevant, good-quality data to learn patterns and make accurate predictions. The performance and accuracy of AI models are considerably impacted by the quantity, quality, and relevance of the data used. Therefore, if an AI model is trained with insufficient or poor-quality data, it will produce incorrect predictions and unreliable results.
Artificial Intelligence (AI) has played a crucial role in expanding the capabilities of computing systems. AI applications like speech recognition, autonomous driving, and natural language understanding rely heavily on algorithms, big data, and computational power.
Mention two specific AI applications and their impact on real-world systems.
Answer
Two specific AI applications and their impact on real-world systems are:
- Speech Recognition – Speech recognition is an AI application that enables machines to understand and process human speech. It allows computing systems to interact with users through voice commands. Its impact can be seen in virtual assistants, voice-controlled devices, and accessibility tools, making human–computer interaction more natural and efficient.
- Autonomous Driving – Autonomous driving uses AI algorithms, big data, and high computational power to enable vehicles to drive without human intervention. This application helps vehicles analyse surroundings, make decisions, and navigate safely. Its real-world impact includes improved road safety, reduced human error, and increased efficiency in transportation systems.
In deterministic computing, the system's output is determined solely by the input and a predefined set of rules or algorithms. This rigid system is highly structured but lacks adaptability to new data or real world complexities.
Mention any two limitations of deterministic computing.
Answer
Two limitations of deterministic computing are:
- Rigidness – Deterministic computing is rigid since it is based on pre-programmed instructions and predefined algorithms. It is incapable of adjusting to new or changing data and learning from it.
- Lack of reasoning capability – Deterministic computing struggles with situations that need reasoning, judgement, and pattern identification.
Probabilistic computing is used to manage uncertainty in complex systems. In which scenario would probabilistic computing be more beneficial than deterministic computing?
- When precise and predictable results are required for simple tasks
- When the system needs to adapt to uncertain or changing conditions
- When the program requires the execution of a fixed set of instructions
- When the input data is always complete and accurate
Answer
When the system needs to adapt to uncertain or changing conditions
Reason — Probabilistic computing is designed to handle uncertainty and probabilistic reasoning. It is more suitable for real-world problems where data may be incomplete, uncertain, or continuously changing. Probabilistic computing allows systems to learn from data, adapt to new situations, and make flexible decisions, unlike deterministic computing which follows rigid predefined rules.
In telecommunications, AI may not be needed. Why are predefined rules and protocols sufficient for efficient data transmission and call routing?
- They ensure randomness and flexibility in data handling
- They provide a fixed, predictable framework that avoids uncertainty
- They adapt to every user's preferences and historical data
- They allow for probabilistic decision-making in uncertain environments
Answer
They provide a fixed, predictable framework that avoids uncertainty
Reason — In telecommunications, predefined rules and protocols regulate the efficient transmission and routing of data. These systems rely on deterministic computing, which offers predictable and reliable behaviour without uncertainty. Hence, fixed rules are sufficient for tasks like call routing and data packet switching, and AI is not required in such scenarios.
In Python, a variable is used to store data that can change during program execution. Variables have certain rules for naming, including starting with a letter or an underscore and being case-sensitive.
What are the rules for naming variables in Python?
Answer
The rules for naming variables in Python are as follows:
- A variable name must start with a letter (a–z or A–Z) or an underscore (_).
- It cannot start with a digit.
- A variable name can contain letters, digits, and underscores only.
- Special characters such as
@,#,$,%, etc., are not allowed. - Variable names are case-sensitive.
- Keywords reserved by Python cannot be used as variable names.
Python offers various data types, including lists, tuples, dictionaries, and sets. These data types determine the types of values a variable can hold and the operations that can be performed on them. A dictionary, for example, is an unordered collection of key-value pairs.
What are the differences between a dictionary and a set in Python?
Answer
The differences between a dictionary and a set in Python are:
| Dictionary | Set |
|---|---|
| A dictionary is an unordered collection of key–value pairs. | A set is an unordered collection of unique elements. |
| Each element is stored in the form of key : value. | Elements are stored without keys or values. |
| Keys must be unique, but values can be repeated. | All elements must be unique. |
Which of the following errors occur when there is a grammatical mistake in the code, such as a misspelling of a function name?
- Logical errors
- Linking errors
- Syntax errors
- Runtime errors
Answer
Syntax errors
Reason — Syntax errors occur due to wrongly typed statements or grammatical mistakes in a programming language. For example, a misspelling of a function name such as writing pint() instead of print() results in a syntax error.
What is a dictionary in Python?
- A collection of ordered and immutable elements
- A collection of unordered, changeable, and indexed key-value pairs
- A collection of unique elements
- A collection of ordered values with no keys
Answer
A collection of unordered, changeable, and indexed key-value pairs
Reason — A dictionary in Python is defined as a collection of unordered data values that are changeable and indexed. Each element in a dictionary is stored as a key–value pair, where the key is unique and used to access the corresponding value. Dictionaries are enclosed in curly braces {}.
Python allows repetitive execution of a block of code through loops. The two primary loops in Python are for and while. You may use the range() function with a for loop to iterate over a sequence of numbers.
Write Python code that calculates the sum of all even numbers between 1 and 100 using for loop.
sum_even = 0
for i in range(1, 101):
if i % 2 == 0:
sum_even = sum_even + i
print("Sum of all even numbers between 1 and 100 =", sum_even)Sum of all even numbers between 1 and 100 = 2550
A nested loop refers to a loop inside another loop. This is often used when you need to perform operations that involve multi-dimensional data or multiple conditions.
Write Python code to print the multiplication table for numbers from 1 to 5.
for i in range(1, 6):
for j in range(1, 11):
print(i, "x", j, "=", i * j)
print()1 x 1 = 1
1 x 2 = 2
1 x 3 = 3
1 x 4 = 4
1 x 5 = 5
1 x 6 = 6
1 x 7 = 7
1 x 8 = 8
1 x 9 = 9
1 x 10 = 10
2 x 1 = 2
2 x 2 = 4
2 x 3 = 6
2 x 4 = 8
2 x 5 = 10
2 x 6 = 12
2 x 7 = 14
2 x 8 = 16
2 x 9 = 18
2 x 10 = 20
3 x 1 = 3
3 x 2 = 6
3 x 3 = 9
3 x 4 = 12
3 x 5 = 15
3 x 6 = 18
3 x 7 = 21
3 x 8 = 24
3 x 9 = 27
3 x 10 = 30
4 x 1 = 4
4 x 2 = 8
4 x 3 = 12
4 x 4 = 16
4 x 5 = 20
4 x 6 = 24
4 x 7 = 28
4 x 8 = 32
4 x 9 = 36
4 x 10 = 40
5 x 1 = 5
5 x 2 = 10
5 x 3 = 15
5 x 4 = 20
5 x 5 = 25
5 x 6 = 30
5 x 7 = 35
5 x 8 = 40
5 x 9 = 45
5 x 10 = 50
What is the purpose of the 'in' operator in Python?
- To check if a value exists in a sequence
- To start a loop
- To create a function
- To define a variable
Answer
To check if a value exists in a sequence
Reason — The in operator is a membership operator used to check whether a particular value is present in a sequence such as a list, tuple, string, or set. It returns True if the value is found in the sequence and False otherwise.
What is the purpose of the 'pass' statement in Python?
- It terminates a loop when a condition is met
- It skips the current iteration of a loop
- It serves as a placeholder, allowing code to be syntactically correct while leaving the body empty
- It repeats the current iteration in a loop
Answer
It serves as a placeholder, allowing code to be syntactically correct while leaving the body empty
Reason — The pass is a keyword and a null statement. It is used as a placeholder when a loop, function, or conditional statement is required syntactically but no action needs to be performed. Unlike comments, the pass statement is not ignored by the interpreter and helps keep the code syntactically correct while the body is left empty.
Python modules offer a wide range of functionality. The math and random modules are some of the most commonly used modules in programming. The random module is used to generate random numbers, and the math module provides mathematical functions like sqrt() and factorial().
Write a program that generates a random integer between 1 and 100 using the random module. Then, use the math module to calculate the square root of the generated number.
import random
import math
num = random.randint(1, 100)
print("Random number:", num)
result = math.sqrt(num)
print("Square root of the number:", result)Random number: 66
Square root of the number: 8.12403840463596
Random number: 81
Square root of the number: 9.0
Void functions are functions that perform actions but do not return a value. These functions often have side effects, such as printing to the console or modifying global variables.
Create a function called print_table_of_numbers(n) that prints the multiplication table of a number (n).
Answer
def print_table_of_numbers(n):
for i in range(1, 11):
print(n, "x", i, "=", n * i)How can you use the randrange() function in your program?
- By importing the math module
- By importing the random module
- By importing the statistics module
- By importing the datetime module
Answer
By importing the random module
Reason — The randrange() function belongs to the random module. It is used to generate random numbers from a given range. Therefore, to use randrange() in a Python program, the random module must be imported.
Which of the following statements is true about fixed length arguments in Python functions?
- They allow an indefinite number of arguments to be passed.
- They can only accept one argument.
- They require a predefined number of arguments.
- They allow values to be passed but not variables.
Answer
They require a predefined number of arguments.
Reason — Fixed length arguments in Python functions mean that the number of arguments to be passed is already defined in the function definition. When such a function is called, it must be provided with exactly the same number of arguments, otherwise an error occurs.
Artificial intelligence is categorised into two parts based on the capability of performing tasks: Narrow AI and Broad AI. Narrow AI is designed to perform a single task or a collection of related tasks, while Broad AI refers to systems that mimic human-like cognitive abilities, allowing them to perform a variety of intellectual tasks.
How do Narrow AI and Broad AI differ in terms of their capabilities and applications?
Answer
Narrow AI (also known as Weak AI) is designed to perform a specific task or a set of closely related tasks. It works within a limited scope and cannot perform tasks beyond what it is programmed for. Examples include speech recognition systems, recommendation systems, and image recognition systems. Narrow AI is widely used in real-world applications to solve specific problems efficiently.
Broad AI (also known as Strong AI) refers to AI systems that aim to mimic human intelligence and cognitive abilities. Such systems can understand, learn, reason, and apply knowledge across different tasks, similar to humans. Broad AI is not limited to a single task and can handle multiple intellectual tasks. Broad AI is still largely theoretical and under research, with limited real-world implementation.
Expert systems, computer vision, natural language processing, and neural networks are key components of Artificial Intelligence. These technologies are used in various fields, including healthcare, automation, and data analysis. Expert systems replicate human knowledge for specific tasks, computer vision allows machines to "see" and analyse visual data, NLP enables computers to understand human language, and neural networks help in making predictions and decisions based on data.
Mention any two areas where AI technologies like expert systems and computer vision can be applied and explain their significance in those fields.
Answer
Two areas where AI technologies such as expert systems and computer vision can be applied and their significance are:
1. Healthcare
- Expert systems: They use stored medical knowledge and rules to help doctors analyse symptoms, suggest possible diseases and support treatment decisions.
- Computer vision: Using medical images (like X-rays/CT/MRI), it helps in detecting abnormalities and supports early and accurate diagnosis which improves patient care.
2. Security and Surveillance (e.g., museums/public places/industry)
- Expert systems: They can apply rule-based logic to raise alerts and support quick decisions (e.g., suspicious activity, restricted-area access), reducing human error.
- Computer vision: With camera-based surveillance, it can monitor areas continuously, identify objects/people and help prevent incidents like theft of valuable items. This improves safety and protection while saving time and manpower.
Which of the following best describes the role of computer vision in AI?
- Enabling computers to recognise and interact with human language
- Enabling computers to process and analyse text data
- Enabling computers to interpret visual data, such as photos and videos
- Creating neural networks for machine learning tasks
Answer
Enabling computers to interpret visual data, such as photos and videos
Reason — Computer vision enables computers and machines to perceive, analyse, and extract meaningful information from visual data, such as images and videos.
How do neural networks in AI work?
- They simulate the human brain's structure and function to process data and make predictions or decisions based on input data.
- They are based on predefined rules and cannot learn from data.
- They replicate human visual and auditory abilities.
- They use mathematical formulas to process natural language.
Answer
They simulate the human brain's structure and function to process data and make predictions or decisions based on input data.
Reason — A neural network is a computational model inspired by the structure and functioning of biological neural networks in the human brain. Artificial Neural Networks (ANN) consist of interconnected neurons arranged in layers, which learn from input data and are used to predict or make judgements based on that data.