From Recipes to Reasoning
Introduction
The term algorithm predates modern computers by centuries. The word traces back to the Persian mathematician al‑Khwarizmi, whose work on arithmetic procedures became algorism in Medieval Latin and eventually algorithm[1]. Today, Merriam‑Webster defines an algorithm as “a procedure for solving a mathematical problem … in a finite number of steps,” broadly meaning a step‑by‑step method for solving a problem or achieving some end[2]. Whether you follow a recipe to bake bread or a sequence of operations to sort a list, you are executing an algorithm. In computing, algorithms are the building blocks of every program; they specify exactly what the machine should do, in what order, and under what conditions. Importantly, a conventional algorithm is rigid: it does not change itself in response to outcomes—it follows its prescribed steps each time[3].
Artificial intelligence (AI) is a broader, more aspirational concept. As the Tableau team explains, AI is a branch of computer science focused on creating machines that can think and make decisions independently[4]. Instead of merely following a fixed recipe, AI systems are designed to learn from data, update their internal models and, over time, perform tasks—translation, planning, diagnosis—that were once the exclusive province of human cognition. In essence, AI systems are clusters of algorithms that can adapt, refine, and sometimes even generate new algorithms based on experience[5]. This ability to alter their own decision rules in response to new data constitutes the “intelligence” in AI.
Distinguishing between AI and algorithms is crucial. Algorithms are instructions; AI is a framework that uses adaptive collections of algorithms to simulate intelligent behavior. As data scientist Niranjan Krishnan notes, mature AI can be viewed as a gear system with interlocking components: data processing, machine learning and business action. These gears turn autonomously; data is ingested, models are updated and decisions are executed without human intervention[6]. Where a simple algorithm pushes the brake whenever a sensor detects an obstacle, an AI driver learns to interpret myriad environmental cues, predict hazards and adjust its own braking strategy[7].
Foundations of Algorithms
At their core, algorithms are finite sequences of instructions. In mathematics, Euclid’s algorithm for finding the greatest common divisor of two numbers exemplifies how a deterministic sequence can guarantee a correct outcome[2]. In computing, algorithms can be expressed in pseudocode, flowcharts or natural language. They may involve selection (if/else), iteration (loops) and recursion (functions that call themselves). The design and analysis of algorithms revolve around efficiency (time and space complexity), correctness and generality.
Algorithms permeate everyday life: search engines use ranking algorithms to decide which results appear first[8]; credit scoring algorithms evaluate loan applicants; social media feeds are curated by engagement algorithms. In these contexts, algorithms encode human goals and biases into machine‑executable rules. However, they do not learn on their own; any adaptation requires a human to modify the code or adjust the parameters. This limitation motivates the development of learning algorithms that underpin AI.
From Algorithms to Artificial Intelligence
Learning to Learn
AI is built on algorithms, but AI algorithms differ from traditional ones by virtue of their plasticity. The Tableau overview describes how AI algorithms take in training data—either labeled or unlabeled—to learn patterns and improve performance[9]. The training approach defines three major categories:
- Supervised learning: models are trained on labeled data, learning to map inputs to known outputs. Examples include classification and regression algorithms that predict whether an email is spam or estimate house prices[10]. Supervised learning remains the most widely used approach in business applications because performance is easily measured and tuned.
- Unsupervised learning: models explore unlabeled data to uncover structure. Many unsupervised algorithms perform clustering—organizing data into groups so that points in the same cluster are similar and points in different clusters are distinct[11]. K‑means clustering, for instance, chooses centroid points and assigns other data points to the nearest centroid, iteratively refining the groups[12]. Gaussian mixture models generalize this concept by allowing clusters to take on elongated shapes[13].
- Reinforcement learning: agents learn optimal behavior through trial and error in an environment. The algorithm receives a state signal, takes an action and receives a reward; over time it updates its policy to maximize expected long‑term return[14]. Reinforcement learning underpins game‑playing AIs, robotics and adaptive control systems.
These categories encompass dozens of specific algorithms—decision trees, support vector machines, neural networks—each with its own assumptions and strengths. Neural networks, modeled loosely on brain connectivity, are especially notable; they learn hierarchical representations and can be used for both supervised and unsupervised tasks, including image recognition and language modeling[15].
The Difference Engine
CMSWire underscores that an algorithm is “a set of instructions—a preset, rigid, coded recipe that gets executed when it encounters a trigger,” whereas AI is “a group of algorithms that can modify its algorithms and create new algorithms in response to learned inputs and data”[5]. Dr. Mir Emad Mousavi likens the relationship to that between cars and flying cars: an algorithm defines the process through which a decision is made, while AI uses training data to learn how to decide[7]. In other words, an algorithm encodes known logic; an AI system uncovers and updates logic from experience.
This distinction resonates with the difference between traditional programming and machine learning. In conventional software, a programmer explicitly writes instructions to solve a problem. If the problem or data changes, the code must be manually updated[16]. In machine learning, the programmer specifies a model structure and training regime; the algorithm learns the solution by optimizing its parameters on data[16]. Thus, machine learning automates part of the algorithm‑creation process.
Algorithms as the Building Blocks of AI
Despite their differences, AI systems are built on the scaffolding of algorithms. Learning algorithms rely on foundational methods such as gradient descent (an optimization algorithm), backpropagation (used in neural networks) and dynamic programming. These low‑level algorithms perform the repetitive computations necessary for learning. At a higher level, AI pipelines include algorithms for data preprocessing, feature extraction, model selection and evaluation. Without these components—each an algorithm in its own right—AI could not function.
Ethical Considerations and Bias
While AI promises transformative benefits, it also raises profound ethical questions. Psychologists emphasize that “biased algorithms can promote discrimination or other forms of inaccurate decision‑making that can cause systematic and potentially harmful errors; unequal access to AI can exacerbate inequality”[17]. Bias can creep in because the training data are incomplete or unrepresentative, because the model architecture amplifies correlations, or because developers embed subjective assumptions. Moreover, generative AI systems can behave unpredictably due to black‑box reasoning—their internal operations are not fully transparent even to their creators[18]. This opacity can obscure errors and hinder accountability.
To address bias, experts recommend auditing AI models for fairness, examining how training data are collected and whether they generalize to the intended population[19]. They also urge transparency, human oversight and the integration of diverse perspectives. As psychologist Rhoda Au notes, society should avoid simplistic judgments that AI is purely good or bad; we must “embrace its complexity and understand that it’s going to be both”[20]. Ethical deployment of algorithms thus requires not only technical safeguards but also socio‑legal frameworks that define acceptable uses and ensure equitable access.
Conclusion and Future Directions
Algorithms and AI form a recursive loop: algorithms enable AI to learn, and AI, in turn, generates new algorithms. The distinction lies in adaptability. Algorithms are explicit, finite procedures; AI systems are collections of algorithms with the capacity to modify themselves based on data, producing behavior that approximates human cognition. Recognizing this difference helps avoid hype and fosters responsible innovation.
Looking ahead, advances in explainable AI seek to make learning algorithms more transparent, bridging the gap between deterministic algorithms and black‑box AI. Integration with other domains—energy systems, linguistics, ethics—will give rise to new recursive frameworks where algorithms not only process information but participate in cyber‑ecological cycles connecting data, action and meaning. As we build these systems, we must keep both the elegance of Euclid’s algorithm and the humility of psychological insight at the forefront, ensuring that the algorithms we design serve human flourishing rather than undermine it.
[1] [2] [8] ALGORITHM Definition & Meaning – Merriam-Webster
https://www.merriam-webster.com/dictionary/algorithm
[3] [5] [6] [7] AI vs. Algorithms: What’s the Difference?
https://www.cmswire.com/information-management/ai-vs-algorithms-whats-the-difference
[4] [9] [10] [11] [12] [13] [14] [15] Artificial intelligence (AI) algorithms: a complete overview | Tableau
https://www.tableau.com/data-insights/ai/algorithms
[16] Are algorithms the same as artificial intelligence (AI)?
https://www.scribbr.com/frequently-asked-questions/are-algorithms-same-as-artificial-intelligence-ai
[17] [18] [19] [20] Addressing equity and ethics in artificial intelligence
https://www.apa.org/monitor/2024/04/addressing-equity-ethics-artificial-intelligence