Artificial Intelligence MCQ Quiz in தமிழ் - Objective Question with Answer for Artificial Intelligence - இலவச PDF ஐப் பதிவிறக்கவும்

The correct answer is Training

Key Points

• In AI and machine learning projects, the "training" phase refers to the process where the machine learning model is trained on a large dataset, which is also known as the training dataset.
• The quality and relevance of this data is extremely important for the model's performance.
• Efficiency in a machine learning model refers to its ability to accurately predict or categorize data it has not seen before (in the evaluation or test set).
• If the data used to train the model is relevant (related to the context or the problem the model is designed to solve) and authentic (real, accurate, reliable, and not misleading), the model is likely to be more accurate and make better predictions.
• That's because these models learn from the data they are given: they identify patterns, make inferences, and adjust their parameters through exposure to this data.
• If the training data is poor, misleading, or irrelevant, the model will perform poorly when it encounters new, unseen data.

Additional Information

• On the other hand, "Row" refers to a single, horizontal set of data or entry in a dataset, and "Testing" is a phase in machine learning where a trained model is tested on unseen data to evaluate its performance.
• While these are crucial components of an AI project, the statement is specifically pointing towards the training phase when the data's relevance and authenticity have the biggest impact on improving the efficiency of an AI project.

The correct answer is ​Reinforcement learning

Key Points

• Reinforcement Learning: As correctly identified, reinforcement learning models learn by making a series of decisions and adapting based on the rewards or penalties (reinforcement signals) they receive in response to their actions.
• They are especially valuable in navigating complex, unpredictable environments like game playing or robotics, where the model needs to iterate and improve its policy by continuously interacting with its environment.

Additional Information 

• Unsupervised Learning: This type of machine learning involves training a model using a dataset that has not been labeled, classified, or categorized. Instead of responding to feedback, unsupervised learning models identify commonalities in the data and react based on the presence or absence of such commonalities. They are most commonly used for clustering and association tasks, like grouping customers into segments based on their behavior.
• Supervised Learning: In supervised learning, the machine learning model is trained on a labeled dataset. In other words, during training, the model is provided with inputs along with the corresponding desired outputs (labels). The model learns to map the inputs to the correct outputs, and the performance is evaluated based on the model's ability to accurately predict the output for a new input. Supervised learning is commonly used in tasks like image classification, spam detection, or predition tasks, where each example in the training data is associated with a specific label.

The correct answer is option 3.

Concept:

Deep learning:

Deep learning is a machine learning technique that teaches computers to learn by example in the same way that humans do. Deep learning is a critical component of self-driving automobiles, allowing them to detect a stop sign or discriminate between a pedestrian and a lamppost.

• The backbone of deep learning techniques is neural networks, which are a branch of machine learning.
• A neural network is a set of algorithms that attempts to detect underlying relationships in a batch of data using a method that mimics how the human brain works.
• In reality, the number of node layers, or depth, separates a single neural network from a deep learning algorithm, which must have more than three layers.
• In deep learning, A computer model learns to execute categorization tasks directly from pictures, text, or sound in deep learning.
• Deep learning models can attain state-of-the-art accuracy, even surpassing human performance in some cases.

Hence the correct answer is Neural Network.

The correct answer is Abstraction

Key PointsAbstraction is the process of removing detail from a given state representation. It is a key concept in AI as it allows AI systems to focus on the most important aspects of a problem and ignore the less important details.

For example, a representation of a car could include the following levels of detail:

• Low-level representation: This representation would include all of the details of the car, such as the make, model, year, color, and VIN number.
• Medium-level representation: This representation would include some of the details of the car, such as the make, model, and year.
• High-level representation: This representation would include only the most important details of the car, such as the make and model.

An AI system could use different levels of abstraction to solve different problems. For example, if the AI system was trying to identify a car, it could use the low-level representation to identify the make, model, and year of the car. If the AI system was trying to decide whether or not to buy a car, it could use the medium-level representation to identify the make, model, and year of the car, as well as the price. Abstraction is a powerful tool that can be used to solve a wide variety of problems.

The correct answer is Speech recognition

Key Points

• The technology that allows chatbots to interact in spoken language involves a combination of several elements, but the key component for understanding and processing spoken language is Speech Recognition. 
• However, it's worth noting that the effectiveness of chatbots in spoken language interaction often relies on other technologies as well, such as Natural Language Understanding (NLU), which helps in comprehending the meaning and context of user input. Machine learning algorithms and sequence-to-sequence neural networks can also play roles in enhancing the overall performance of spoken language interaction for chatbots.

Key Points

 Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality.

There are several dimensionality reduction methods that can be used with different types of data for different requirements

• Principal Component Analysis
• Linear Discriminant Analysis
• Factor Analysis

Hence the correct answer is Correlation Analysis.

Additional Information Correlation analysis is used to quantify the degree to which two variables are related. Through the correlation analysis, you evaluate the correlation coefficient that tells you how much one variable changes when the other one does. Correlation analysis provides you with a linear relationship between two variables.

The correct answer is Lisp

Key Points

• Lisp: Known for its symbolic manipulation capabilities, Lisp has a historical association with early AI research due to its suitability for tasks involving symbolic reasoning.

Additional Information

• Perl: While a versatile language, Perl is not as commonly associated with AI as other languages. It is more widely used in web development, system administration, and text processing.
• Prolog: Designed for logic programming, Prolog is used in AI for tasks requiring rule-based systems and knowledge representation, making it suitable for certain AI applications.
• C++: A general-purpose language with a focus on efficiency, C++ is used in AI for performance-critical components, though it is not as predominant as languages like Python or Lisp in AI development.

The correct answer is Artificial Neural Network

 Key Points

• Artificial Neural Networks (ANNs) form the basis of deep learning.
• Inspired by the structure and function of the human brain, ANNs comprise connected nodes, or "neurons." Unlike the decision trees and random forests of traditional machine learning, ANNs can learn complex patterns and representations by processing the data through these interconnected neurons in multiple layers, hence making them the fundamental building blocks of deep learning.

Additional Information

•   Unlike other machine learning algorithms, artificial neural networks try to simulate the human brain's functioning to make predictions or decisions.

The correct answer is option 2.

Key Points:Concept:​

• John McCarthy, the father of AI, were to coin a new phrase for "artificial intelligence" today, he would probably use "computational intelligence."
• McCarthy is not just the father of AI, he is also the inventor of the Lisp (list processing) language.
• John McCarthy was a computer scientist and cognitive scientist from the United States.
• McCarthy was a pioneer in the field of artificial intelligence.
• He co-wrote the paper that popularized the term "artificial intelligence" (AI), created the Lisp programming language family, impacted the design of the ALGOL computer language, promoted time-sharing, and devised garbage collection.

Hence the correct answer is John McCarthy.

Additional Information

• Ada M. Fisher is a retired physician from Salisbury, North Carolina, who has run for office several times as a Republican.
• Alan Mathison Turing was a mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist from the United Kingdom.
• Allen Newell worked at the RAND Corporation and Carnegie Mellon University's School of Computer Science, Tepper School of Business, and Department of Psychology as a computer science and cognitive psychology researcher.

The correct answer is ​Narrow AI is capable of doing any intellectual work that a human being can do

Key Points

• Narrow Artificial Intelligence (AI), also known as Weak AI, involves systems designed to accomplish specific tasks.
• These systems can be incredibly sophisticated and capable within their designed purpose, but their scope is narrow, which means they are limited to their predefined tasks and cannot exhibit general intelligence or perform tasks outside their programming.
• Examples include recommendation systems, like those on e-commerce sites, and voice recognition systems, such as Siri and Alexa.

Additional Information

• Narrow AI: This is AI developed for a specific task, like speech recognition or image recognition. Its scope and abilities are limited to the specific task it was designed for.
• Strong AI: Also known as General AI, this kind of AI would have the capacity to understand, learn, and apply knowledge across various domains, akin to human intelligence.