Types of AI algorithms and how they work
AI algorithms can help businesses gain a competitive advantage. Learn the main types of AI algorithms, how they work and why companies must thoroughly evaluate benefits and risks.
If companies are not using AI and machine learning, their risk of becoming obsolete increases exponentially. That observation was made back in 2020 by my former teacher, now colleague, at the University of California, Berkeley, the AI expert Alberto Todeschini. AI's value to business has only become more evident over the years, as I have collaborated with distinguished enterprises.
AI algorithms can help sharpen decision-making, make predictions in real time and save companies hours of time by automating key business workflows. They can improve customer service, bubble up new ideas and bring other business benefits -- but only if organizations understand how AI algorithms work, know which type is best suited to the problem at hand and take steps to minimize AI risks.
Let's begin the work of understanding AI algorithms.
What is an AI algorithm?
AI algorithms are a set of instructions or rules that enable machines to learn, analyze data and make decisions based on that knowledge. These algorithms can perform tasks that would typically require human intelligence, such as recognizing patterns, understanding natural language, problem-solving and decision-making.
It is important in any discussion of AI algorithms to also underscore the value of the using the right data and not so much the amount of data in the training of algorithms. We'll dive into why quality data testing is so critical. With that said, the following are some general types of AI algorithms and their use cases.
This article is part of
A guide to artificial intelligence in the enterprise
Types of AI algorithms
There are three main types of AI algorithms.
1. Supervised learning algorithms. In supervised learning, the algorithm learns from a labeled data set, where the input data is associated with the correct output. This approach is used for tasks such as classification and regression problems such as linear regression, time-series regression and logistic regression. Supervised learning is used in various applications, such as image classification, speech recognition and sentiment analysis.
Examples of supervised learning algorithms include decision trees, support vector machines and neural networks.
2. Unsupervised learning algorithms. In unsupervised learning, an area that is evolving quickly due in part to new generative AI techniques, the algorithm learns from an unlabeled data set by identifying patterns, correlations or clusters within the data. This approach is commonly used for tasks like clustering, dimensionality reduction and anomaly detection. Unsupervised learning is used in various applications, such as customer segmentation, image compression and feature extraction.
Examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA) and autoencoders.
3. Reinforcement learning algorithms. In reinforcement learning, the algorithm learns by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting its actions to maximize the cumulative rewards. This approach is commonly used for tasks like game playing, robotics and autonomous vehicles.
Examples of reinforcement learning algorithms include Q-learning, SARSA (state-action-reward-state-action) and policy gradients.
Algorithms have been around for thousands of years
Derived from the name of the ninth-century Persian mathematician Muhammad ibn Musa al-Khwarizmi, the term algorithm has been in use for thousands of years to denote a detailed set of step-by-step instructions for solving a problem or completing a task.
The ancient Greeks, for example, developed mathematical algorithms for calculating square roots and finding prime numbers.
In the computer age, the availability of massive amounts of digital data is changing how we think about algorithms and the types and complexity of the problems computer algorithms can be trained to solve.
Techniques used in AI algorithms
There are several techniques that are widely used in AI algorithms, including the following:
- Machine learning. Machine learning is a subset of AI and is the most prevalent approach for training AI algorithms. ML uses statistical methods to enable machines to learn from data without being explicitly programmed. ML algorithms, as explained above, can be broadly classified into three types: supervised learning, unsupervised learning and reinforcement learning. Common machine learning techniques include linear regression, decision trees, support vector machines and neural networks.
- Deep learning. Deep learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers (think ResNet50) to learn complex patterns in large amounts of data. Deep learning has been successful in a wide range of applications, such as computer vision, speech recognition and natural language processing. Popular deep learning techniques include convolutional neural networks and recurrent neural networks.
- Natural language processing. NLP is a field of AI that deals with the interaction between computers and human language. NLP techniques enable machines to understand, interpret and generate human language in textual and spoken forms. Common NLP techniques include sentiment analysis, named-entity recognition and machine translation.
General applications and use cases for AI algorithms
AI algorithms have numerous applications across all industries, making it safe to say that the state of AI is near-ubiquitous in business. The following are some examples of AI's reach:
Healthcare. AI algorithms can assist in diagnosis, drug discovery, personalized medicine and remote patient monitoring. In healthcare, AI algorithms can help doctors and healthcare professionals make better decisions by providing insights from large amounts of data. For example, AI algorithms can analyze medical images to identify anomalies or predict disease progression.
Finance. AI is used for fraud detection, credit scoring, algorithmic trading and financial forecasting. In finance, AI algorithms can analyze large amounts of financial data to identify patterns or anomalies that might indicate fraudulent activity. AI algorithms can also help banks and financial institutions make better decisions by providing insight into customer behavior or market trends.
Retail and e-commerce. AI enables personalized recommendations, inventory management and customer service automation. In retail and e-commerce, AI algorithms can analyze customer behavior to provide personalized recommendations or optimize pricing. AI algorithms can also help automate customer service by providing chat functions.
The rise of LLMs
Large language models, a type of AI system based on deep learning algorithms, have been built on massive amounts of data to generate amazingly human-sounding language, as users of ChatGPT and interfaces of other LLMs know.
As of this article, LLMs have mainly been built on general platforms by the big tech companies. But customization is happening -- for example, BloombergGPT just created its own finance LLM.
We forecast that LLMs will quickly become industry specific and even hyper-localized to highly specific audiences. Let's say a company is targeting a group of senior citizens. Or maybe you wanted to communicate in a different era? The LLMs would be able to speak their language in the exact tone and terminology they are accustomed to. It's hard for humans to do this.
The need for responsible AI
It's important to understand the full scope and potential of AI algorithms. These algorithms enable machines to learn, analyze data and make decisions based on that knowledge. They are widely used across all industries and have the potential to revolutionize various aspects of our lives. However, as we integrate AI into more aspects of our lives, it is crucial to consider the ethical implications and challenges to ensure responsible AI adoption.
One of the biggest ethical concerns with AI algorithms is bias. If the data used to train the algorithm is biased, the algorithm will likely produce biased results. This can lead to discrimination and unfair treatment of certain groups. It is crucial to ensure that AI algorithms are unbiased and do not perpetuate existing biases or discrimination.
Another ethical concern with AI algorithms is privacy. As AI algorithms collect and analyze large amounts of data, it is important to ensure that individuals' privacy is protected. This includes ensuring that sensitive information is not being used inappropriately and that individuals' data is not being used without their consent.
To address these ethical concerns and challenges, various doctrines of ethical-based AI have been developed, including those set by the White House. These doctrines outline principles for responsible AI adoption, such as transparency, fairness, accountability and privacy.
In addition to ethical considerations, many high-level executives are considering a pause on AI-driven solutions. This is due to the speed at which algorithms are evolving and the plethora of use cases. It is crucial to thoroughly evaluate the potential benefits and risks of AI algorithms before implementing them.
As a data scientist, it is important to stay up to date with the latest developments in AI algorithms and to understand their potential applications and limitations. By understanding the capabilities and limitations of AI algorithms, data scientists can make informed decisions about how best to leverage these powerful tools.
