Ian Talks AI A-Z

By Ian Eress

Unlock the mysteries of Artificial Intelligence with this guide to the key concepts and definitions. From machine learning and deep learning to natural language processing and computer vision, this book provides a clear and accessible introduction to the field of AI. Written for beginners, it is the perfect resource for anyone looking to deepen their understanding of this rapidly evolving technology. With clear explanations and real-world examples, this book is your go-to reference for all things AI.

 

• Language: English
• Publisher: Ian Eress
• Release date: Jan 22, 2023
• ISBN: 9798215724965

Ian Eress

Born in the seventies. Average height. Black hair. Sometimes shaves. Black eyes. Nearsighted. Urban. MSc. vim > Emacs. Mac.

Book preview

Ian Talks AI A-Z

Ian Eress

Published by Ian Eress, 2023.

While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

IAN TALKS AI A-Z

First edition. January 22, 2023.

Copyright © 2023 Ian Eress.

ISBN: 979-8215724965

Written by Ian Eress.

Table of Contents

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

INDEX

For Caitlyn

A

Action recognition: Action recognition is the task of identifying the actions performed by humans or objects in videos or images. It is a key area of computer vision and machine learning. The goal of action recognition is to understand and recognize the actions performed by humans or objects in videos or images. This information can be used in various applications such as surveillance, human-computer interaction, and sports analysis. There are various techniques used to recognize actions such as using convolutional neural networks (CNNs), Recurrent Neural Networks (RNNs), and Hidden Markov Models (HMM) among others. In simple terms, action recognition is the task of identifying the actions performed by humans or objects in videos or images, it is a key area of computer vision and machine learning.

Active learning: Active learning is a type of machine learning where the algorithm is able to actively select the data it wants to learn from. Instead of using a fixed dataset, the algorithm can interact with the environment, asking for labels on specific examples in order to improve its performance. This allows the algorithm to focus on the most informative examples and to learn more efficiently. Active learning is particularly useful in scenarios where labeled data is scarce, expensive or hard to obtain. It can also be used to improve the performance of traditional machine learning algorithms in scenarios where the dataset is large and diverse. In simple terms, active learning is a type of machine learning where the algorithm is able to actively select the data it wants to learn from, it allows the algorithm to focus on the most informative examples and to learn more efficiently.

Adaptive systems: Adaptive systems are systems that can adjust their behavior or performance based on the input or environment. These systems can learn from their past experiences, change their parameters, and adapt to new situations. They are able to improve their performance over time by adjusting their internal states or parameters. Adaptive systems can be found in a wide variety of fields such as control systems, signal processing, computer science, and machine learning. In simple terms, adaptive systems are systems that can adjust their behavior or performance based on the input or environment, they are able to improve their performance over time by adjusting their internal states or parameters.

Adversarial examples: Adversarial examples are inputs to a machine learning model that have been specifically crafted to cause the model to make a mistake. These examples are created by adding small, carefully chosen perturbations to an input, such as an image, that are not noticeable to humans but cause the model to misclassify the input. Adversarial examples can be used to evaluate the robustness of a model and to identify weaknesses in the model's architecture or training data. They have been found to be a problem in several real-world applications, such as image recognition and natural language processing. In simple terms, adversarial examples are inputs to a machine learning model that have been specifically crafted to cause the model to make a mistake, they are used to evaluate the robustness of a model and to identify weaknesses in the model's architecture or training data.

Adversarial networks: Adversarial networks are a type of neural network architecture that is used in machine learning. They consist of two parts: a generator network, which generates new examples, and a discriminator network, which tries to distinguish the generated examples from real ones. The two networks are trained together in a process called adversarial training, in which the generator tries to create examples that can fool the discriminator, and the discriminator tries to correctly identify the generated examples as fake. This process allows the generator to improve its ability to create realistic examples, and the discriminator to improve its ability to identify fake examples. In simple terms, adversarial networks are neural networks that are trained to identify if something is real or fake.

Adversarial training: Adversarial training is a method used to improve the robustness of machine learning models. It is based on the idea of creating adversarial examples, which are inputs that are specifically crafted to cause a model to make a mistake, and then using these examples to train the model to be more robust to them. The process of adversarial training involves creating adversarial examples, and then adjusting the model's parameters so that it performs better on these examples. This is done by minimizing the difference between the model's predictions on the adversarial examples and the correct labels. The goal of adversarial training is to make the model more robust to unseen examples that have similar properties to the adversarial examples used in training. In simple terms, adversarial training is a method used to improve the robustness of machine learning models by creating adversarial examples and using them to train the model to be more robust to them.

Anomaly detection: Anomaly detection is a technique used in data mining and machine learning to identify unusual or abnormal patterns in data. It is used to identify unusual data points, events or observations that deviate from the norm. Anomaly detection algorithms are typically used to monitor systems and detect rare events or events that deviate from the expected behavior. It can be used in various applications such as detecting fraud, monitoring computer networks, identifying defective equipment, and identifying medical conditions. Anomaly detection can be done using supervised or unsupervised methods, depending on the nature of the data. In simple terms, anomaly detection is a technique used to identify unusual or abnormal patterns in data, it is used to detect rare events or events that deviate from the expected behavior.

Artificial General Intelligence: Artificial General Intelligence (AGI) refers to a type of artificial intelligence that is capable of understanding or learning any intellectual task that a human can. AGI is often thought of as a strong or full AI, as opposed to narrow or weak AI, which is designed to perform specific tasks. AGI is considered as a future development of AI, where the machine can perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects, learning from experience and so on. The goal of AGI research is to create a machine that can perform any intellectual task that a human can, and that can learn to perform new tasks on its own, without being explicitly programmed for each one. In simple terms, AGI is a type of AI that can perform any intellectual task that a human can, and learn to do new tasks on its own.

Artificial Intelligence: Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. These machines can be made to perform tasks such as recognizing speech, making decisions, and playing games. The goal of AI is to create technology that can understand and reason about the world in a way that's similar to how humans do.

Artificial life: Artificial life refers to the simulation or emulation of life, its processes and phenomena using computer technology. The goal of artificial life is to understand the principles of living systems and to create artificial systems that exhibit the characteristics of life such as growth, reproduction, adaptation, and evolution. This field of study encompasses a wide range of topics such as artificial intelligence, robotics, computer science, and biology. Examples of artificial life include virtual creatures in computer games, simulations of ecosystems, and autonomous robots that exhibit behavior that resembles that of living organisms. In simple terms, artificial life refers to the simulation or emulation of life, its processes and phenomena using computer technology, the goal is to understand the principles of living systems and to create artificial systems that exhibit the characteristics of life such as growth, reproduction, adaptation, and evolution.

Artificial neural networks: Artificial Neural Networks (ANNs) are a type of machine learning algorithm that are modeled after the structure and function of the human brain. They consist of layers of interconnected nodes, called artificial neurons, which process and transmit information. Each neuron receives input from other neurons, performs a computation on that input, and then sends the output to other neurons in the next layer. ANNs can be trained to perform a wide range of tasks, such as image recognition, natural language processing, and prediction. They are trained using large sets of labeled data and an optimization algorithm, which adjusts the connection weights of the network to minimize the error between the network's predictions and the true values. In simple terms, Artificial Neural Networks are a type of algorithm that are modeled after the human brain, used to perform tasks such as image recognition and natural language processing.

Artificial Super Intelligence: Artificial Super Intelligence (ASI) refers to a type of artificial intelligence that is significantly more intelligent than the best human minds in virtually every field, including scientific creativity, general wisdom and social skills. It is often considered as a future development of AI, where machine intelligence surpasses human intelligence in multiple domains. The concept of ASI implies that machine intelligence would be capable of understanding and solving problems that humans cannot, and to improve itself at an increasing rate. ASI is still a hypothetical concept and currently, there is no AI that can be considered as ASI. The development of ASI raises ethical and societal concerns. In simple terms, ASI is a type of AI that is significantly more intelligent than the best human minds in virtually every field, and has the potential to improve itself at an increasing rate.

Attention-based models: Attention-based models are a type of neural network that uses attention mechanisms to focus on specific parts of the input when making predictions. Attention mechanisms allow the model to weigh the importance of different parts of the input and to selectively focus on the most relevant parts when making predictions. This is particularly useful in tasks such as machine translation, image captioning, and speech recognition, where the model needs to understand the input in context and to selectively focus on different parts of the input at different times. Attention-based models are typically built using a combination of recurrent neural networks (RNNs) and attention mechanisms, and have shown to improve performance on a wide range of natural language processing tasks. In simple terms, attention-based models are a type of neural network that uses attention mechanisms to focus on specific parts of the input when making predictions, allowing the model to selectively focus on the most relevant parts when making predictions.

Attention mechanism: An attention mechanism is a technique used in certain types of neural networks, such as machine translation and image captioning models, to allow the network to focus on specific parts of the input when making predictions. The idea is that not all parts of the input are equally important for the task at hand, so the network should pay more attention to certain parts and less to others. The attention mechanism works by creating a weighting or attention for each part of the input, which is then used to selectively focus on certain parts when making predictions. In simple terms, the attention mechanism is a way for neural networks to focus on specific parts of an input when making decisions or predictions, allowing the model to be more efficient and accurate.

Augmented Reality: Augmented Reality (AR) is a technology that superimposes digital content, such as images, videos, or 3D models, on the user's view of the real world. The goal of AR is to enhance the user's perception of the real world by providing additional information or context. AR is typically achieved through the use of head-mounted displays, smartphones, or tablets, which display the digital content on top of the real-world view. It can be used in various applications such as gaming, education, and industrial training, among others. In simple terms, Augmented Reality (AR) is a technology that superimposes digital content on the user's view of the real world, the goal is to enhance the user's perception of the real world by providing additional information or context.

Autoencoder: An autoencoder is a type of neural network that is trained to learn a compact representation of the input data. It does this by first encoding the input data into a lower-dimensional representation, called the encoding, and then decoding the encoding back into the original input data. The encoding is typically smaller than the original input data, so it can be thought of as a compression of the input. The goal of the autoencoder is to learn a representation of the input data that captures the most important features, while discarding the less important ones. The autoencoder is trained by comparing the original input data with the output of the decoder and adjusting the parameters of the network to minimize the difference between the two. In simple terms, an autoencoder is a neural network designed to learn a compressed representation of input data.

Autonomous agents: Autonomous agents are systems that can sense their environment, reason about it, and take actions to achieve their goals. They are able to operate independently of human supervision and are designed to make decisions and perform tasks on their own. Autonomous agents can be physical, such as robots, or virtual, such as computer programs. They are used in a wide range of applications such as robotics, control systems, and self-driving cars. Autonomous agents can be reactive, meaning they respond to the current state of their environment, or deliberative, meaning they use reasoning and planning to make decisions. In simple terms, autonomous agents are systems that can sense their environment, reason about it, and take actions to achieve their goals. They are able to operate independently of human supervision and are designed to make decisions and perform tasks on their own.

Autonomous systems: Autonomous systems are systems that can sense their environment, reason about it, and take actions to achieve their goals without human intervention. They are able to operate independently and make decisions based on their internal state and external conditions. Autonomous systems can be physical, such as robots, drones, and self-driving cars, or virtual, such as software programs that make decisions based on input data. They are used in a wide range of applications such as transportation, manufacturing, and defense. Autonomous systems are characterized by their ability to make decisions, adapt to changing conditions, and operate independently. In simple terms, autonomous systems are systems that can sense their environment, reason about it, and take actions to achieve their goals without human intervention. They are able to operate independently and make decisions based on their internal state and external conditions.

Autoregression: Autoregression is a statistical model that describes the dependence of a variable's current value on its past values. It is a technique used in time series analysis to model and forecast the future values of a time series. Autoregression models assume that the current value of a time series is a linear function of its past values. For example, an autoregression model of order 1 (AR(1)) assumes that the current value of the time series is a linear function of the previous value. More complex autoregression models of order 2 (AR(2)) or higher can be used to model the dependence on multiple past values. Autoregression models are widely used in finance, economics, and other fields where time series data is analyzed. In simple terms, autoregression is a statistical model that describes the dependence of a variable's current value on its past values, it is a technique used in time series analysis to model and forecast the future values of a time series.

Autoregressive integrated moving average (ARIMA): Autoregressive integrated moving average is a statistical technique used to predict future values of a data series based on past values. It is an advanced method from time series analysis that allows us to make better predictions about future events.

There are several different types of autoregressive integrated moving average models. One popular model is the autoregressive moving average (ARMA) model, which fits the data series using two autoregressive terms and one moving average term. Other models can use more or less terms, and some can be non-linear.

Autoregressive models: Autoregressive (AR) models are a class of time series models that describe the current value of a time series as a function of its previous values. In an autoregressive model, the value of the time series at time t is modeled as a function of the values at previous times. For example, an AR(1) model models the current value as a linear function of the previous value, while an AR(2) model models it as a linear function of the two previous values. The order of the model, represented by the number in parentheses, refers to the number of previous values used in the model. Autoregressive models are commonly used in finance, economics, and other fields where time series data is analyzed. In simple terms, autoregressive models are a class of time series models that describe the current value of a time series as a function of its previous values.

B

Backpropagation: Backpropagation is an algorithm used to train neural networks by adjusting the weights of the network in order to minimize the error between the predicted output and the true output. The algorithm is used to train multi-layer feedforward neural networks, also known as multi-layer perceptrons (MLPs). The backpropagation algorithm starts by making a prediction using the current weights of the network, then it calculates the error between the predicted output and the true output. The error is then propagated backwards through the network, adjusting the weights of the network in order to reduce the error. The process is repeated for multiple iterations, each time the algorithm makes a prediction, calculates the error, and adjusts the weights. This process is called backpropagation because the error is propagated backwards through the network. In simple terms, backpropagation is an algorithm used to train neural networks by adjusting the weights of the network to minimize the error between the predicted output and the true output.

Bagging: Bagging stands for Bootstrap Aggregating. It is an ensemble method that is used to improve the performance of machine learning models by combining the predictions of multiple models. The idea behind bagging is to train multiple models on different subsets of the training data, and then combine the predictions of these models to make a final prediction. Each subset of the training data is created by randomly selecting samples from the original dataset with replacement. Because each subset of the training data is different, each model trained on it will also be different. By combining the predictions of these models, bagging aims to reduce the variance and improve the overall accuracy of the predictions. In simple terms, bagging is an ensemble method that is used to improve the performance of machine learning models by combining the predictions of multiple models, each of which is trained on a different subset of the training data.

Bandit problem: The bandit problem is a type of reinforcement learning problem where an agent must choose between several options (or arms) in order to maximize its total reward over time. Each arm has a probability distribution of rewards and the agent's goal is to find the arm that gives the highest expected reward. The agent receives feedback in the form of rewards after each action and must learn to balance the exploration of new arms with the exploitation of the best arm found so far. The problem is called a bandit problem because it resembles the problem of a gambler trying to learn which arm of a slot machine is the most profitable while the machine is still in operation. The name also refers to the fact that the agent is one-armed or one-armed bandit, because it can only pull one lever at a time. In simple terms, the bandit problem is a type of reinforcement learning problem where an agent must choose between several options (or arms) in order to maximize its total reward over time, each arm has a probability distribution of rewards and the agent's goal is to find the arm that gives the highest expected reward.

Batch gradient descent: Batch gradient descent is an optimization algorithm used to minimize a cost function in machine learning and deep learning models. It is a type of stochastic gradient descent (SGD) algorithm. The main difference between batch gradient descent and regular stochastic gradient descent is that in batch gradient descent, the model's parameters are updated after each iteration using the average of the gradients of the cost function with respect to the parameters, calculated over the entire training dataset. In contrast, in regular stochastic gradient descent, the parameters are updated after each iteration using the gradient of the cost function with respect to the parameters calculated on a random single training example. Batch gradient descent is known to converge to the global minimum, but it can be computationally expensive and slow when the dataset is large, that's why it is usually combined with mini-batch gradient descent. In simple terms, batch gradient descent is an optimization algorithm used to minimize a cost function in machine learning and deep learning models. It updates the model's parameters after each iteration using the average of the gradients of the cost function with respect to the parameters, calculated over the entire training dataset.

Batch normalization: Batch normalization is a technique used to improve the stability and performance of deep neural networks. It normalizes the output of each neuron by adjusting and scaling the values so that they have a mean of 0 and a standard deviation of 1. Batch normalization helps to reduce the internal covariate shift, which occurs when the distribution of the inputs to each layer of the network changes during training. By normalizing the inputs, batch normalization helps to ensure that the inputs to each layer have a similar distribution, which in turn helps to speed up the training process and improve the performance of the network. It is typically applied to the activations of the layers, before the activation function is applied. Batch normalization is usually applied during training, and the normalization parameters are learned along with the other parameters. In simple terms, batch normalization is a technique used to improve the stability and performance of deep neural networks by normalizing the output of each neuron by adjusting and scaling the values so that they have a mean of 0 and a standard deviation of 1. It helps to reduce the internal covariate shift and improve the performance of the network.

Bayesian Decision Theory: Bayesian Decision Theory is a framework for making decisions under uncertainty. It is based on the Bayesian statistical framework, which allows to update the belief of the decision maker given new evidence. It provides a way of modeling decision-making problems in which the decision maker has uncertain knowledge about the state of the world. This uncertain knowledge is represented by a probability distribution over the possible states of the world. The decision maker's goal is to choose an action that maximizes some measure of performance, such as expected reward, given the uncertain knowledge about the state of the world. The decision maker's belief about the state of the world is represented by a probability distribution called the prior, which is updated after observing new evidence through the process of Bayesian inference. The resulting probability distribution is called the posterior.

Bayesian deep learning: Bayesian deep learning is a field that combines the principles of Bayesian statistics and deep learning. It aims to incorporate uncertainty into deep learning models and to provide a way to quantify the uncertainty of the predictions made by these models. The main idea behind Bayesian deep learning is to use Bayesian methods to infer the probability distributions of the model's parameters, rather than point estimates, as it is done in traditional deep learning. This allows us to estimate the uncertainty of the predictions and to use this uncertainty in decision-making tasks. Bayesian deep learning methods are particularly useful in scenarios where the data is noisy or where the model is under-constrained. They can also be used to