Artificial Intelligence (AI) has become an integral part of our daily lives, influencing various sectors, from entertainment and education to healthcare and finance. This article aims to provide a comprehensive understanding of AI and its related terminologies, helping you make informed decisions in this new-age technology era.
A Brief History of AI
The concept of AI dates back to the 1940s, but the term “Artificial Intelligence” was coined by American Computer Scientist John McCarthy in 1955. The first AI conference was held at Dartmouth University a year later, marking the beginning of formal AI research.
In the 1980s, the focus shifted towards neural networks and machine learning, inspired by the structure and functioning of the human brain. This allowed machines to learn from data and improve their performance over time. However, the late 1980s and early 1990s saw a period known as the “AI Winter,” characterized by a significant decline in interest and funding due to unmet expectations.
The late 1990s witnessed a resurgence in the field, with advancements in areas like data mining, natural language processing, and computer vision. In recent years, the availability of vast amounts of data and advancements in computational power have fueled breakthroughs in AI. Deep learning, a subfield of machine learning that utilizes neural networks with multiple layers, has led to significant advancements in image and speech recognition, natural language processing, and other AI applications.
Top Artificial Intelligence Terms to Know
1. AI Algorithm
An AI algorithm is a set of instructions that enables a computer to perform a certain task. These algorithms help a computer understand how to perform certain tasks and achieve the desired results independently, setting the process for decision-making.
2. Machine Learning (ML)
Machine Learning is a subset of AI that enables machines to “learn” using algorithms, data, and statistical models to make better decisions. While AI is a broad term that refers to the ability of computers to mimic human thought and behaviours, ML is an application of AI used to train computers to do specific tasks using data and pattern recognition.
3. Deep Learning (DL)
Deep Learning, a subset of ML, trains computers to do what humans can—learn by example. Computer models can be taught to perform tasks by recognizing patterns in images, text, or sound, sometimes surpassing humans’ ability to make connections. Deep Learning is employed in cutting-edge technology like driverless cars to process a stop sign or differentiate between a human and a lamp post.
4. Natural Language Processing (NLP)
Natural Language Processing is an application of ML that helps machines understand, interpret, and process human language to perform routine tasks. It uses rules of linguistics, statistics, ML, and DL to equip computers to fully understand what a human is communicating through text or audio and perform relevant tasks. AI virtual assistants and AI voice recognition systems like voice-operated GPS are examples of NLP.
5. Computer Vision (CV)
Computer Vision is a form of AI that trains computers to recognize visual input. For instance, a machine will be able to analyze and interpret images, videos, and other visual objects to perform certain tasks that are expected of it. An example is medical professionals using this technology to scan MRIs, X-rays, or ultrasounds to detect human health problems.
6. Robotics
Robotics is a branch of engineering, computer science, and AI that designs machines to perform human-like tasks without human intervention. These robots can be used to perform a wide variety of tasks that are either too complex and difficult for humans or are repetitive, or both. For example, building a robotic arm to assemble cars in an assembly line is an example of a robot.
7. Data Science
Data Science uses large sets of structured and unstructured data to generate insights that data scientists and others can use to make informed decisions. Often, data science employs ML practices to find solutions to different challenges and solve real-world problems. For instance, financial institutions may employ data science to analyze a customer’s financial situation and bill-paying history to make better decisions on lending.
An extension of data science is data mining. It involves extracting useful and pertinent information from a large data set and providing valuable insights. It is also known as knowledge discovery in data (KDD). Data mining has numerous applications, including in sales and marketing, education, fraud detection, and improving operational efficiency.
8. Quantum Computing
Quantum Computing uses theories of quantum physics to solve complex problems that classic computing cannot solve. It is used to run complex simulations in a matter of seconds by converting real-time language into quantum language. Google has a quantum computer that they claim is 100 million times faster than an average computer. Quantum computing can be used in various fields ranging from cybersecurity to pharmaceuticals to solve big problems with fewer resources.
9. Chatbots
A chatbot employs AI and NLP to simulate human conversations. It can operate through text or voice conversations. Chatbots use AI to analyze millions of conversations, learn from human responses, and mimic them to provide human-like responses. This tech has found great usage in customer service and as AI virtual assistants.
10. AI Bias
AI Bias refers to the tendency of machines to adopt human biases because of how and by whom they are coded or trained. Algorithms can often reinforce human biases. For instance, a facial recognition platform may be able to recognize Caucasian people better than people of colour because of the data set it has been fed. It is possible to reduce AI bias through more testing in real-life circumstances, accounting for these biases, and improving how humans operating these systems are educated.
11. Neural Networks
Neural Networks are a set of algorithms modelled after the human brain. They are designed to recognize patterns and interpret sensory data through a kind of machine perception, labelling, or clustering of raw input.
12. Supervised Learning
Supervised Learning is a type of Machine Learning where the model is trained on labelled data. The model makes predictions based on this data, and its accuracy is measured based on its ability to match the labels.
13. Unsupervised Learning
Unsupervised Learning is a type of Machine Learning where the model is given unlabeled data and must find patterns and relationships within the data on its own.
14. Reinforcement Learning
Reinforcement Learning is a type of Machine Learning where an agent learns to behave in an environment by performing actions and receiving rewards or penalties.
15. Generative Adversarial Networks (GANs)
GANs are a class of AI algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework.
16. Transfer Learning
Transfer Learning is a Machine Learning method where a pre-trained model is used on a new problem. It is a popular method in deep learning where pre-trained models are used as the starting point for computer vision and natural language processing tasks.
17. Feature Extraction
Feature Extraction involves reducing the amount of resources required to describe a large set of data accurately. In machine learning, it starts from an initial set of measured data and builds derived values intended to be informative and non-redundant.
18. Overfitting
Overfitting is a concept in statistics and machine learning where a statistical model fits the data too closely. It may result in a model that performs well on training data but poorly on test data.
19. Underfitting
Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. It occurs when the model or the algorithm does not fit the data well enough.
20. Bias-Variance Tradeoff
The bias-variance tradeoff is a problem in machine learning that prevents a model from learning enough from the training data and from learning too much from the training data.
21. Grid Search
Grid Search is a traditional way to perform hyperparameter optimization, which is simply an exhaustive search through a manually specified subset of the hyperparameter space of a learning algorithm.
22. Random Search
Random Search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. It is different from a Grid Search in that it does not try all possible combinations but selects at random to sample a certain number of them.
23. Cross-Validation
Cross-Validation is a technique used to assess how the statistical analysis generalizes to an independent data set. It is primarily used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.
24. Precision and Recall
Precision is the fraction of relevant instances among the retrieved instances. At the same time, recall (also known as sensitivity) is the fraction of the total amount of relevant instances that were actually retrieved. Both values range from 0 to 1, where 1 is the best possible score.
25. F1 Score
The F1 score is the harmonic mean of precision and recall. It measures a test’s accuracy and ranges from 0 to 1, where 1 is the best possible score.
26. ROC Curve
The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
27. AUC-ROC
AUC stands for “Area under the ROC Curve.” It provides an aggregate measure of performance across all possible classification thresholds. AUC measures the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1).
28. Confusion Matrix
A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known.
29. Hyperparameters
In machine learning, hyperparameters are parameters whose values are set before the learning process begins. These parameters help guide the learning process.
30. Epochs
In terms of artificial neural networks, an epoch refers to one cycle through the full training dataset. During an epoch, the weights of the neural network are updated to reduce the error in prediction.
31. Batch Size
Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration.
32. Learning Rate
The learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum loss function.
33. Activation Function
In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.
34. Backpropagation
Backpropagation is a method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data. It is a special case of an older and more general technique called automatic differentiation.
35. Gradient Descent
Gradient Descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point because this is the direction of the steepest descent.
36. Stochastic Gradient Descent (SGD)
Stochastic gradient descent is a simple and very efficient approach to fit linear models. It is particularly useful when the number of samples is very large.
37. Regularization
Regularization is a technique used to prevent overfitting by adding an additional penalty term to the loss function.
38. Dropout
Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data.
39. Convolutional Neural Networks (CNNs)
Convolutional Neural Networks are a class of deep neural networks most commonly applied to analyzing visual imagery.
40. Recurrent Neural Networks (RNNs)
Recurrent Neural Networks are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behaviour.
41. Long Short-Term Memory (LSTM)
Long Short-Term Memory units are units of a recurrent neural network (RNN). An RNN composed of LSTM units is often called an LSTM network. A common LSTM unit comprises a cell, an input gate, an output gate, and a forget gate.
42. Autoencoders
Autoencoders are artificial neural networks used to learn efficient codings of input data.
43. Reinforcement Learning
Reinforcement Learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximize the notion of cumulative reward.
44. Q-Learning
Q-Learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state.
45. Monte Carlo Methods
Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results.
46. Decision Trees
A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome.
47. Random Forests
Random forests or random decision forests are an ensemble learning method for classification, regression, and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
48. Support Vector Machines (SVM)
Support Vector Machines are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis.
49. K-Nearest Neighbors (KNN)
K-Nearest Neighbors is a type of instance-based learning, or lazy learning, where the function is only approximated locally, and all computation is deferred until function evaluation.
50. Naive Bayes
Naive Bayes classifiers are a family of simple “probabilistic classifiers” based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.
51. Linear Regression
Linear Regression is a linear approach to modelling the relationship between a dependent variable and one or more independent variables.
52. Logistic Regression
Logistic Regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable.
53. K-Means Clustering
K-Means Clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups).
54. Hierarchical Clustering
Hierarchical Clustering is a method of cluster analysis which seeks to build a hierarchy of clusters.
55. Dimensionality Reduction
Dimensionality Reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
56. Principal Component Analysis (PCA)
Principal Component Analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
57. t-Distributed Stochastic Neighbor Embedding (t-SNE)
t-SNE is a machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions.
58. Singular Value Decomposition (SVD)
In linear algebra, the Singular Value Decomposition is a factorization of a real or complex matrix that generalizes the eigendecomposition of a square normal matrix to any m × n matrix via an extension of the polar decomposition.
59. Latent Dirichlet Allocation (LDA)
In natural language processing, Latent Dirichlet Allocation is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
60. Word Embeddings
Word Embeddings are a type of word representation that allows words with similar meanings to have a similar representation.
61. Word2Vec
Word2Vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
62. GloVe (Global Vectors for Word Representation)
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
63. FastText
FastText is a library for efficient learning of word representations and sentence classification.
64. BERT (Bidirectional Encoder Representations from Transformers)
BERT is a transformer-based machine learning technique for natural language processing pre-training developed by Google.
65. GPT (Generative Pretrained Transformer)
GPT is an autoregressive language model that uses deep learning to produce human-like text.
66. Transformer Models
The Transformer is a model architecture, introduced in the paper “Attention is All You Need”, which relies entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
67. Attention Mechanisms
Attention Mechanisms in deep learning, introduced by the transformer model, are based on the same feature of the human brain that allows us to focus on specific elements while ignoring others.
68. Seq2Seq Models
Seq2Seq Models (Sequence-to-Sequence Models) are deep learning models that convert sequences from one domain (e.g., sentences in English) to sequences in another domain (e.g., the same sentences translated into French).
69. AutoML
AutoML, or Automated Machine Learning, is the process of automating the end-to-end process of applying machine learning to real-world problems.
70. Explainable AI (XAI)
Explainable AI refers to methods and techniques in the application of artificial intelligence such that the results of the solution can be understood by humans.
71. Federated Learning
Federated Learning is a machine learning approach that allows a model to be trained across multiple decentralized edge devices or servers holding local data samples, without exchanging them.
72. Swarm Intelligence
Swarm Intelligence is the collective behaviour of decentralized, self-organized systems, natural or artificial.
73. Genetic Algorithms
Genetic Algorithms are optimization algorithms that mimic the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem.
74. Fuzzy Logic
Fuzzy Logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive.
75. Expert Systems
Expert Systems are computer systems that emulate the decision-making ability of a human expert.
76. Computer-Aided Diagnosis
Computer-Aided Diagnosis is a procedure that has been used in medical imaging for assisting radiologists to make decisions through the output from computer software.
77. Natural Language Generation (NLG)
Natural Language Generation is a software process that transforms structured data into natural language.
78. Text-to-Speech (TTS)
Text-to-Speech is a type of assistive technology that reads digital text aloud.
79. Speech Recognition
Speech Recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers.
80. Optical Character Recognition (OCR)
Optical Character Recognition is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text.
81. Sentiment Analysis
Sentiment Analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
82. Emotion AI
Emotion AI, also known as affective computing, is a technology that is capable of reading, imitating, interpreting, and responding to human facial expressions and emotions.
83. Predictive Analytics
Predictive Analytics encompasses various statistical techniques, from data mining, predictive modelling, and machine learning, that analyze current and historical facts to predict future or otherwise unknown events.
84. Prescriptive Analytics
Prescriptive Analytics not only anticipates what will happen and when it will happen but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option.
85. Anomaly Detection
Anomaly Detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
86. Outlier Detection
Outlier Detection is similar to anomaly detection, but it specifically involves identifying the extreme values in the dataset that deviate from other observations.
87. Time Series Analysis
Time Series Analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
88. Association Rule Learning
Association Rule Learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
89. Ensemble Learning
Ensemble Learning is a machine learning paradigm where multiple models (often called “weak learners”) are trained to solve the same problem and combined to get better results.
90. Bagging
Bagging, or Bootstrap Aggregating, is a simple and powerful ensemble method. It is used to reduce the variance of a decision tree. Here the objective is to create several subsets of data from the training sample chosen randomly with replacement.
91. Boosting
Boosting is an ensemble method for improving the model predictions of any given learning algorithm. The idea of boosting is to train weak learners sequentially, each trying to correct its predecessor.
92. Stacking
Stacking, also called Super Learning or Stacked Generalization, is an ensemble learning technique that combines multiple classification or regression models via a meta-classifier or a meta-regressor.
93. Multi-Task Learning
Multi-Task Learning is a learning paradigm in machine learning where multiple learning tasks are solved at the same time while exploiting commonalities and differences across tasks.
94. One-Shot Learning
One-Shot Learning is a concept in machine learning where the learning algorithm is required to learn from one single example.
95. Few-Shot Learning
Few-Shot Learning refers to the practice of feeding a learning model with a very small amount of training data, expecting it to generalize its knowledge to unseen data.
96. Zero-Shot Learning
Zero-Shot Learning refers to a learning task where no labelled examples are available for one or several classes during the training phase.
97. Semi-Supervised Learning
Semi-Supervised Learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labelled data with a large amount of unlabeled data.
98. Self-Supervised Learning
Self-Supervised Learning is a type of supervised learning where the data provides the supervision. In other words, labels are generated from the input data.
99. Multi-Instance Learning
Multi-Instance Learning is a way of learning from labelled bags of instances. In the standard supervised learning setting, each training example is a pair consisting of an input object and a desired output value.
100. Online Learning
Online Learning is a method of machine learning in which data becomes available in sequential order and is used to update the best predictor for future data at each step.
101. Active Learning
Active Learning is a special case of machine learning in which a learning algorithm can interactively query the user (or some other information source) to obtain the desired outputs at new data points.
The Future of AI
Today, AI represents the human capacity to create, innovate, and push the boundaries of what was once thought impossible. So, whether you are an AI enthusiast, a curious learner, or a decision-maker shaping the future, you must equip yourself with the right knowledge to survive in a world that AI and its tools are increasingly powering.
FAQs
What is the difference between AI, Machine Learning, and Deep Learning?
AI is a broad term that refers to the ability of computers to mimic human thought and behaviours. Machine Learning is a subset of AI that enables machines to “learn” using algorithms, data, and statistical models to make better decisions. Deep Learning, a subset of ML, trains computers to do what humans can—learn by example.
How does Natural Language Processing work?
Natural Language Processing is an application of ML that helps machines understand, interpret, and process human language to perform routine tasks. It uses rules of linguistics, statistics, ML, and DL to equip computers to fully understand what a human is communicating through text or audio and perform relevant tasks.
What is AI Bias, and how can it be reduced?
AI Bias refers to the tendency of machines to adopt human biases because of how and by whom they are coded or trained. It is possible to reduce AI bias through more testing in real-life circumstances, accounting for these biases, and improving how humans operating these systems are educated.
What is Quantum Computing?
Quantum Computing uses theories of quantum physics to solve complex problems that classic computing cannot solve. It is used to run complex simulations in a matter of seconds by converting real-time language into quantum language.
How do Chatbots work?
A chatbot employs AI and NLP to simulate human conversations. It can operate through text or voice conversations. Chatbots use AI
to analyze millions of conversations, learn from human responses, and mimic them to provide human-like responses.
How does Computer Vision work?
Computer Vision is a form of AI that trains computers to recognize visual input. For instance, a machine will be able to analyze and interpret images, videos, and other visual objects to perform certain tasks that are expected of it.
What is the role of Robotics in AI?
Robotics is a branch of engineering, computer science, and AI that designs machines to perform human-like tasks without human intervention. These robots can be used to perform a wide variety of tasks that are either too complex and difficult for humans are repetitive, or both.
What is Data Science, and how is it related to AI?
Data Science uses large sets of structured and unstructured data to generate insights that data scientists and others can use to make informed decisions. Often, data science employs ML practices to find solutions to different challenges and solve real-world problems.
What is the history of AI?
The concept of AI dates back to the 1940s, but the term “Artificial Intelligence” was coined by American Computer Scientist John McCarthy in 1955. The first AI conference was held at Dartmouth University a year later, marking the beginning of formal AI research.
What is the future of AI?
AI represents the human capacity to create, innovate, and push the boundaries of what was once thought impossible. It is essential to equip yourself with the right knowledge to survive in a world where AI and its tools are increasingly powering.
