This article provides an overview of AI and Machine Learning algorithms. It lists their strengths, weaknesses, and applications across various fields. It also emphasizes the importance of selecting appropriate algorithms based on specific application needs.

Machine Learning (ML) is an application of Artificial Intelligence (AI). Microsoft defines AI as “the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions” (Microsoft, n.d.). ML can be defined as an application of AI. “It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience” (Microsoft, n.d.). AI and ML algorithms are used to solve complex problems across a large variety of industries. Each algorithm is better suited for certain tasks than others, each with its strengths and weaknesses. Thus, selecting the appropriate algorithm that meets the specific needs of an application is essential. This post explores the different types of AI and ML strengths, weaknesses, and the specific applications for which they are best suited to solve problems.

The field of AI utilizes a substantial number of algorithms. The table below lists some of them with their descriptions:

Table 1
AI Algorithms



Note: Data adapted from multiple sources: (Baheti, 2024; Baluja, 2024; Biswal, 2024; Boesch, 2024; Lui et al., 2018; Kerner, 2024; Tableau, n.d.; & Vargas et al., 2023)

The algorithms in Table 1 can be further categorized into seven different categories as follows:

1. Supervised Learning Algorithms (ML-specific):

Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM).
“Supervised Learning is a technique that is widely used in various fields such as finance, healthcare, marketing, and more. In supervised learning, the supervised learning algorithm learns a mapping between the input features and output labels” (Gupta, 2024)

2. Unsupervised Learning Algorithms (ML-specific):

K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA).

“Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision” (JavaPoints, n.d.)

3. Neural Networks and Deep Learning (ML and AI):

Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
“Neural Networks are one particular type of Machine Learning technique. They are a type of artificial intelligence modeled on the brain. There are nodes or artificial neurons that are each responsible for a simple computation. These nodes are networked together with connections of varying strengths, and learning is reflected in changes to those connections. An important characteristic of neural networks is the relationship between nodes. Often, there is an input layer, an output layer, and one or more in between layers (called “hidden layers”), which can result in a model that has a lot of complexity, but may be difficult to interpret” (Network of the National Library of Medicine, n.d.)

4. Reinforcement Learning (AI-specific):

Q-Learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO).
“Reinforcement learning (RL) is a type of machine learning process that focuses on decision making by autonomous agents. An autonomous agent is any system that can make decisions and act in response to its environment independent of direct instruction by a human user. Robots and self-driving cars are examples of autonomous agents. In reinforcement learning, an autonomous agent learns to perform a task by trial and error in the absence of any guidance from a human user. It particularly addresses sequential decision-making problems in uncertain environments, and shows promise in artificial intelligence development” (Murel, & Kavlakoglu, 2024)

5. Optimization Algorithms (Used in ML and AI):

Gradient Descent, Adam, and Simulated Annealing.

“Optimization algorithms are a class of algorithms that are used to find the best possible solution to a given problem. The goal of an optimization algorithm is to find the optimal solution that minimizes or maximizes a given objective function” (Complexica, n.d.).

6. Probabilistic and Evolutionary Models (ML-specific and used in AI):

Bayesian Networks, Markov Chain Monte Carlo (MCMC), and Evolutionary Algorithms (Genetic Algorithms).
“Probabilistic models are an essential component of machine learning, which aims to learn patterns from data and make predictions on new, unseen data. They are statistical models that capture the inherent uncertainty in data and incorporate it into their predictions” (Visha, 2023)

7. Other:

Autoencoders, Transfer Learning, t-SNE, and Fuzzy Logic Algorithms.
They are used for specialized tasks. Tasks such as feature extraction, knowledge transfer, dimensionality reduction, and reasoning under uncertainty.
AI algorithms have their strengths and weaknesses, which make them suitable and efficient for certain tasks and less suitable and efficient for others.

The table below lists the strengths and weaknesses of different AI-ML algorithms, as well as, the applications for which they are best suited.

Table-2
AI-ML Algorithms Strengths, Weaknesses, and Applications


Note: Data adapted from “A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools” by Lui et al. (2018).

As illustrated in Table 2, different AI Algorithms are better suited for specific tasks generally based on their accuracy, efficiency, scalability, and robustness. For example, Long Short-Term Memory (LSTMs) algorithms are well suited for tasks that require high accuracy over a period of time, such as time-series forecasting, because they can retain long-term dependencies in memory. Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) suitable for real-time or lower-resource environments (efficiency), because they are faster to train and require less computational power for inference; however, they are less precise than other algorithms. Convolutional Neural Networks (CNNs) are well suited for image classification tasks and large datasets, due to their ability to learn patterns making them robust and highly scalable algorithms.

Thus, when selecting an AI algorithm for an application, it is essential to understand the strengths and weaknesses of the algorithm, as well as how well it meets the specific needs of the application. For example, if I have to choose a method, CNNs are well suited for image analysis applications such as detecting tumors from MRI scans; because they can learn and extract patterns from images. However, when trained, they require large datasets and significant computational resources when inferencing. Therefore, when using CNNs for this application, it is important to ensure that the application has access to sufficient data and computational power.

References:

Baheti, P. (2024, July 2). The Essential Guide to Neural Network architectures. V7. https://www.v7labs.com/blog/neural-network-architectures-guide

Baluja, H. (2024, October 7). The Complete Guide to AI Algorithms. Hubspot. https://blog.hubspot.com/marketing/ai-algorithms

Biswal, A. (2024, July 16). Top 10 Deep Learning Algorithms You Should Know in 2024. Simplilearn.com. https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm

Boesch, G. (2024, August 19). Deep Neural Network: The 3 Popular Types (MLP, CNN and RNN). viso.ai. https://viso.ai/deep-learning/deep-neural-network-three-popular-types/

Complexica (n.d.). Optimization Algorithms. Complexica Glossary. https://www.complexica.com/narrow-ai-glossary/optimization-algorithms

Gupta, M. (2024, October 16). Supervised machine learning. GeeksforGeeks. https://www.geeksforgeeks.org/supervised-machine-learning/

Javatpoint (n.d.). Unsupervised Machine learning. Javatpoint. www.javatpoint.com. https://www.javatpoint.com/unsupervised-machine-learning

Kerner, S. M. (2024, October 16). Types of AI algorithms and how they work. Enterprise AI. https://www.techtarget.com/searchenterpriseai/tip/Types-of-AI-algorithms-and-how-they-work

Tavasoli, S. (2024, October 10). 1_0 Types of Machine Learning Algorithms and Models_. Simplilearn.com. https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article

Liu, C.-H, Chang, C.-W.,& Lee, H.-W. (2018). A review of artificial intelligence algorithms used for smart machine tools. In Prof. Dr. Chien-Hung Liu (Ed.), Inventions [Journal-article]. https://doi.org/10.3390/inventions3030041

Microsoft (n.d.). The difference between AI and machine learning. Microsoft Azure. https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/artificial-intelligence-vs-machine-learning

Murel, J., & Kavlakoglu, E. (2024, August 30). Reinforcement Learning. What is reinforcement learning? IBM. https://www.ibm.com/topics/reinforcement-learning

Network of the National Library of Medicine (n.d.). Neural Networks. NNLM. https://www.nnlm.gov/guides/data-glossary/neural-networks

Tableau (n.d.)._ Artificial intelligence (AI) algorithms: a complete overview_. Salesforce. https://www.tableau.com/data-insights/ai/algorithms

Vargas, R., Jiang, N., & Sircar, S. (2023, December 1). What makes AI algorithms different from traditional computer algorithms? https://www.linkedin.com/advice/3/what-makes-ai-algorithms-different-from-omp7f

Visha (2023, May 29). Probabilistic Models in Machine Learning. GeeksforGeeks. https://www.geeksforgeeks.org/probabilistic-models-in-machine-learning/

Originally published by Alex.omegapy at Medium on November 1, 2024.