Artificial Intelligence Quiz
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Questions and Answers

What is the primary characteristic of Artificial narrow intelligence?

  • It mimics human emotional intelligence.
  • It typically does one job well. (correct)
  • It performs multiple complex tasks simultaneously.
  • It can surpass human intelligence.

Which type of AI is described as being as smart as a human?

  • Artificial narrow intelligence.
  • Artificial super intelligence.
  • Artificial emotional intelligence.
  • Artificial general intelligence. (correct)

What are the two broad areas within Artificial Intelligence?

  • Neural Networks and Expert Systems.
  • Symbolic Reasoning and Machine Learning. (correct)
  • Deep Learning and Natural Language Processing.
  • Genetic Algorithms and Robotics.

Which of the following is NOT an example of statistical learning in Machine Learning?

<p>Symbolic Reasoning. (B)</p> Signup and view all the answers

What does the AND function in Boolean logic return when both inputs are 1?

<p>1 (A)</p> Signup and view all the answers

Who among the following is known as a significant figure in the field of Machine Learning?

<p>Geoffrey Hinton. (A)</p> Signup and view all the answers

What is the key implication of Artificial super intelligence?

<p>It can consistently outperform all human capabilities. (B)</p> Signup and view all the answers

What is one reason why advances in AI stalled historically?

<p>Boolean logic functions restricted advancements. (D)</p> Signup and view all the answers

What is the output of the XOR operation when both inputs are 1?

<p>0 (A)</p> Signup and view all the answers

Which of the following statements best describes the function of XOR?

<p>It returns true only if one of the inputs is true, but not both. (A)</p> Signup and view all the answers

What significant problem did Minsky and Papert highlight regarding XOR?

<p>It cannot be derived from AND and OR functions accurately. (C)</p> Signup and view all the answers

How many neurons are estimated to be in a human brain?

<p>100 billion (B)</p> Signup and view all the answers

In the basic Perceptron model, what is the role of weights?

<p>They control the influence of each input on the output. (D)</p> Signup and view all the answers

What kind of problem does the XOR function represent?

<p>Non-linearly separable problem. (A)</p> Signup and view all the answers

Which component of an AI neuron is responsible for generating the output based on inputs and weights?

<p>Activation Function (C)</p> Signup and view all the answers

What is the risk of overfitting in an artificial neural network?

<p>It only recognizes the training data set. (D)</p> Signup and view all the answers

In supervised learning, when can unlabelled data be introduced?

<p>Only after achieving acceptable results. (D)</p> Signup and view all the answers

Which of the following techniques uses a reward signal for training?

<p>Reinforcement learning (C)</p> Signup and view all the answers

What characterizes hybrid learning methods?

<p>They use a small amount of labeled data alongside unlabelled data. (A)</p> Signup and view all the answers

Which type of neural network is particularly effective for sequential data processing?

<p>Recurrent Neural Network (B)</p> Signup and view all the answers

What is typically true about unsupervised learning?

<p>The algorithm identifies underlying patterns in unlabelled data. (A)</p> Signup and view all the answers

Which statement best describes the concept of underfitting?

<p>The model fails to capture the underlying trend of the data. (C)</p> Signup and view all the answers

What is an example of a task where hybrid learning might be particularly useful?

<p>Situations where acquiring labeled data is difficult or costly. (D)</p> Signup and view all the answers

What is the primary purpose of using multiple linear classifiers in the context of the XOR problem?

<p>To create a non-linear decision boundary (B)</p> Signup and view all the answers

In supervised learning, what is necessary for the model to improve its accuracy?

<p>Access to labeled test data (A)</p> Signup and view all the answers

Which of the following best describes the concept of unsupervised learning?

<p>Adjusting parameters based on a derived cost function (C)</p> Signup and view all the answers

What technique does reinforced learning primarily utilize to guide a neural network's actions?

<p>Continuous feedback through rewards and penalties (A)</p> Signup and view all the answers

What role does a cost function play in the context of neural networks?

<p>It measures the network's performance against the target (D)</p> Signup and view all the answers

When training a neural network, what is the significance of the process involving 'Compare the output against the known result'?

<p>It allows for adjusting model parameters (B)</p> Signup and view all the answers

What is an advantage of using multiple layers in a neural network?

<p>To improve the ability to learn non-linear relationships (A)</p> Signup and view all the answers

Which of the following statements best describes the objective of backpropagation in neural networks?

<p>To adjust weights based on the error calculated (B)</p> Signup and view all the answers

Flashcards

Artificial Narrow Intelligence (ANI)

A type of AI designed to perform specific tasks, often with a highly specialized skillset. Examples include image recognition software, chess-playing AI, and voice assistants.

Artificial General Intelligence (AGI)

A hypothetical type of AI that possesses human-level intelligence across a wide range of cognitive tasks. It would be able to reason, learn, and problem-solve like a human in various domains.

Artificial Super Intelligence (ASI)

A highly speculative type of AI that surpasses human intelligence in all aspects. It would possess superhuman abilities, potentially posing both opportunities and challenges.

Artificial Intelligence (AI)

The area within computer science that focuses on creating intelligent systems. It encompasses diverse approaches, including symbolic reasoning and machine learning.

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Statistical Learning

A type of machine learning where statistical models are trained on data to make predictions or analyze patterns. It is used in applications like speech recognition and natural language processing.

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Deep Learning

A powerful technique in machine learning that involves creating complex artificial neural networks with multiple layers. It's capable of learning complex patterns and making sophisticated predictions.

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Expert System

A system that uses rules and knowledge to solve problems in specific domains. It relies on human-defined knowledge and logic to make decisions.

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Boolean Logic

The use of logical operations like AND, OR, and NOT to represent and manipulate information. It's a fundamental concept in computer science and AI.

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XOR (Exclusive OR)

A logical operator that returns 'true' if one input is 'true' and the other is 'false', and returns 'false' otherwise.

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Non-linearly Separable Problem

A problem in machine learning where a simple line cannot effectively separate data points into their respective classes.

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Perceptron

A basic model in artificial intelligence that simulates a single neuron in the brain.

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Activation Function

A function used in artificial neural networks to determine the output of a neuron based on the weighted sum of its inputs.

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Biomimicry

The process of mimicking natural biological processes and systems to create artificial solutions.

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Neuron

A biological cell specialized for transmitting information in the nervous system.

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Input (in a perceptron)

The input values that a neuron receives before processing.

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Weight (in a perceptron)

The numerical weight assigned to each input in a perceptron, determining its influence on the neuron's output.

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Backpropagation

A method used to train neural networks by adjusting parameters based on error feedback. It works by propagating error signals back through the network, allowing weights and biases to be updated to minimize errors.

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Supervised Learning

The process of learning from labeled data. It involves inputting data with known outcomes, allowing the network to adjust its parameters to predict future outcomes accurately.

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Unsupervised Learning

The process of learning from unlabeled data by identifying patterns and relationships without explicit examples. It is commonly used for tasks like clustering and dimensionality reduction.

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Reinforced Learning

A technique where the AI system learns by receiving rewards for correct actions and penalties for incorrect actions. It is used in applications like robotics and game playing.

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Feedforward Neural Network

A type of neural network architecture where information flows in one direction, from input to output, without loops or feedback.

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Overfitting

An AI model that has been trained too much on the training data and performs poorly on new data.

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Underfitting

An AI model that has not been trained enough and performs poorly on both training and new data.

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Hybrid Learning

A type of machine learning that combines both supervised and unsupervised learning techniques.

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Reinforcement Learning

A type of machine learning where the model learns by receiving rewards or punishments based on its actions.

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Convolutional Neural Network (CNN)

A type of neural network that is specifically designed for processing image data.

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Recurrent Neural Network (RNN)

A type of neural network that is designed to process sequential data, such as text or speech.

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Study Notes

Artificial Intelligence Introduction

  • Artificial intelligence (AI) is a subset of computer science
  • AI involves two broad areas: Symbolic Reasoning and Machine Learning
  • Symbolic reasoning is the work of expert systems and hard-coded human reasoning (if-then statements)
  • Machine Learning includes statistical learning, speech recognition, natural language processing and deep learning

Types of AI

  • Artificial Narrow Intelligence (Weak AI): performs specific tasks well
  • Artificial General Intelligence (Strong AI): as smart as a human
  • Artificial Superintelligence: surpasses human intelligence

Influential Machine Learning Minds

  • Geoffrey Hinton
  • Michael I. Jordan
  • Andrew Ng
  • Yann LeCun
  • Yoshua Bengio
  • Demis Hassabis

Why AI Development Stalled

  • Boolean logic functions are fundamental in computer operation
  • "True" is represented as 1, "false" as 0
  • Boolean logic, particularly XOR, faced problems in separating data linearly.

Biological Neurons

  • The human brain has approximately 100 billion neurons
  • Neurons communicate through electrical signals along axons

Artificial Neural Network (ANN)

  • ANNs are modeled after biological neurons
  • The basic Perceptron model is a type of artificial neuron.
  • Inputs are weighted then passed to a node and output based on activation function

Activation Functions

  • Activation functions introduce non-linearity into ANNs examples include step, sigmoid, tanh, ReLU and leaky ReLU, ELU

Supervised Learning

  • Uses labeled training data
  • Aims to produce acceptable results, then unlabeled data can be used

Unsupervised Learning

  • Works with unlabeled data
  • Helps with clustering and association

Reinforcement Learning

  • No labelled data is needed
  • A method to quantify performance with reward
  • Popular in recent development

Hybrid Learning

  • A combination of supervised and unsupervised approaches
  • Useful when only a small amount of labelled data is available in real world applications

Deep Learning

  • Multi-layered neural networks
  • Uses feed forward but back propagation is needed

XOR

  • XOR presented non-linearly separable problem

Solving XOR with Deep Learning

  • Using multiple layers, deep learning can solve issues presented by single layered ANNs

Other AI Topics

  • Convolutional Neural Networks
  • Generative Adversarial Networks
  • Recurrent Neural Networks
  • Long Short-Term Memory Networks
  • Recursive Neural Networks
  • Natural Language Processing

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Test your knowledge on the fundamentals of Artificial Intelligence with this quiz. Explore topics such as characteristics of AI, functions of logic gates, and key figures in the field. Perfect for anyone interested in strengthening their understanding of AI concepts.

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