Artificial intelligence vs machine learning: it’s often misunderstood. The two terms are increasingly on our radars—and in our devices—as technology advances, but how well do we actually know what they are?

The first thing to realize is that they’re not actually distinct things at all. It’s not artificial intelligence vs machine learning, because machine learning (ML) is actually a subset of AI; it cannot exist without AI. Let’s look deeper into the differences—and dependencies—of the two terms.

What is artificial intelligence (AI)?

The term artificial intelligence was created to describe a technology solution, system, or machine that mimics human intelligence to perform tasks. It encompasses a wide variety of ideas, methods, and techniques—both historical and contemporary. As a scientific term, AI was first coined at the Dartmouth Conference in 1956 organized by pioneers in the field such as John McCarthy, Marvin Minsky, and Claude Shannon.

Since then, the world of AI has developed—slowly at first, and then not slowly at all. AI research includes neural networks, Bayesian networks, data mining, and deep learning. These techniques are based on theoretical foundations that apply to a wide variety of different problems, and which have led to the creation of numerous AI subdomains such as natural language processing (NLP), genetic algorithms, robotics, and machine learning.

What is machine learning (ML)?

Machine learning is a subset of AI that focuses on building software systems that can learn and improve from the data they consume.

Machine learning refers to a series of logical or statistical operations performed by machines aimed at learning one or more tasks based on input data. These systems are built with the capacity to automatically learn and improve with experience. To analyze the input data, ML uses complex algorithms to identify patterns.

ML models can mimic human cognitive abilities by implementing methods that solve a variety of real-world problems. They “learn” the optimal approach without the need for explicit programming by humans. Machine learning research is typically broken down into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

What is the difference between AI and machine learning?

AI is a broad field that encompasses various approaches to creating intelligent systems, including machine learning. On the other hand, machine learning is a specific subset of AI that uses algorithms to guide decision-making and execute tasks. While AI applies numerous methods to achieve intelligence, ML specifically relies on patterns from data to solve problems.

AI and machine learning are, therefore, closely related, but differ in scope, purpose, and implementation.

While ML excels at detecting patterns in data, AI research aims to combine ML with other techniques to achieve more general human-like intelligence. AI strives to create computer systems that can think and understand concepts flexibly. Unlike machine learning, which learns from specific examples, AI aims to enable computers to think more like humans. This means systems can apply general rules to new situations they haven’t encountered before and make decisions independently.

Key use cases of AI and machine learning

AI and ML are transforming how businesses and organizations approach complex challenges across diverse sectors.

By combining advanced analytics with data-driven learning, together AI and ML enable solutions that can adapt, predict, and optimize processes in innovative ways.

Healthcare and life sciences

The healthcare sector is where the use of AI and ML is increasingly found. Medical professionals can use AI to enhance imaging (think CT and MRI scans), streamline diagnostics, automate routine tasks, and more, while ML improves accuracy in detecting patterns and anomalies, often surpassing human capabilities.

Agentic AI systems that leverage ML can also be used to monitor patients and develop personalized treatment plans. These systems can detect anomalies, enhance patient engagement, and recommend adjustments to treatment by providing reminders, health alerts, and predictive insights.

Financial services

Financial institutions are already making extensive use of AI/ML to enhance assessments of credit and lending risks. AI can analyze multiple variables such as credit history, financial statements, and outstanding loans to make a calculated evaluation for potential credit card or mortgage loans.

In addition, financial companies now use AI/ML to augment or replace traditional credit scoring. Traditional lenders have been using predictive models, such as statistical regression, for decades. More recently, firms have deployed ML-based models to analyze larger, more diverse datasets and discern meaningful relationships not visible in traditional models. ML underwriting models have been shown to improve predictive accuracy and cost savings relative to traditional models.

Public sector

Governments collect and maintain critical public databases, from health and medical information to employment records and social security. This makes governments a frequent target for cyberattacks and intrusions.

In malware detection, AI cybersecurity systems and ML models work together to protect public sector networks. AI-driven antivirus programs can be used by public sector agencies to identify and neutralize threats targeting private government information, including financial information, national security data, and personal health records. ML systems, on the other hand, learn how to recognize patterns from new threats.

These systems analyze patterns and behaviors to detect vulnerabilities and intrusions, helping governments and public institutions manage complex networks and digital infrastructure.

Energy and utilities

AI and ML are being used for predictive analytics in the energy and utilities sector. Energy distribution across a power grid can be optimized using smart grid technology powered by AI and ML. These grids can predict the expected output from renewable energy sources such as solar and wind farms, allowing for more efficient integration with conventional energy sources.

AI/ML can also be used to predict costly outages and outline actionable insights to anticipate and prevent future disruption.

Manufacturing and supply chain

In modern manufacturing, AI and ML are used to transform industrial operations by capitalizing on the large amounts of data generated by Internet of Things (IoT) devices and smart factories.

AI-powered tools work on the assembly line to inspect vehicle parts and even perform final assessments before market launch.

This improves product quality and minimizes production costs associated with reworks. AI/ML-driven visual inspections are integrated into the production process to reduce human error, increase efficiency, and improve customer quality control.

What are the benefits of using AI and ML together?

Using AI and ML together offers a range of benefits to organizations of all shapes and sizes.

Increased efficiency

AI and ML can be used in tandem to increase operational efficiency and reduce costs. Combined, these technologies provide the ability to analyze large amounts of data at speed and scale with minimal human intervention. This can produce rapid outputs and productivity gains that can potentially increase return on investment (ROI). It can also generate new opportunities and enable workers to shift their energy towards more strategic or higher-value projects.

The automation of tasks that were previously conducted manually can also help optimize resource allocation and drive further increases in productivity.

Improved decision-making

An additional benefit of using AI and ML together is enhanced decision-making. By accelerating data processing, reducing human error, and enabling faster decisions, these technologies help organizations make better-informed choices. They also allow organizations to integrate predictive analytics and insights into business reporting and applications. ML algorithms can process data in real time, providing timely insights for proactive decision-making.

Enhanced problem-solving

In complex projects, AI and ML can work together to help solve multifaceted problems. ML techniques help quantify uncertainties in data and predictions, and AI can incorporate this uncertainty into its decision-making process, resulting in more reliable solutions.

By combining the data-processing power of ML with the decision-making capabilities of AI, complex problems can be approached from multiple angles, leading to more comprehensive and effective outcomes.

Challenges of combining AI and machine learning

Of course, harnessing the benefits of AI and machine learning isn’t without its challenges. Forewarned is forearmed, however, and understanding those challenges makes it easier to mitigate them.

Data quality

The importance of data quality and accessibility for AI and machine learning cannot be overstated. High-quality, clean data is the foundation upon which effective AI and ML models are built. Data that is inconsistent or contains gaps can severely reduce the efficiency of ML models and adversely impact the level of outputs.

A comprehensive data governance framework offers a solution by establishing standardized data collection protocols, implementing automated validation tools, and creating centralized data management systems. This approach allows organizations to identify and resolve data inconsistencies, address gaps, and maintain high-quality datasets to support AI and ML models.

Resource constraints

The application of AI and ML requires a considerable amount of resources, such as large amounts of training data, skilled personnel, and computing power.

In some contexts, access to these resources might be limited. For example, in the financial sector, data privacy regulations can limit the availability of necessary training data. Small businesses might struggle to hire data scientists and AI engineers due to limited budgets.

To address resource constraints, researchers are developing more efficient machine learning solutions. These include integrating physics-based simulations to generate and augment training data, encoding domain-specific principles directly into ML models to improve efficiency, and designing planning methods with in-built verifications to ensure system resilience and safety while reducing the need for extensive computational resources.

Interoperability

Integrating AI and ML solutions with existing infrastructure poses significant challenges.

The process involves connecting advanced AI/ML technologies with legacy systems, navigating complex networks, breaking down data silos, and minimizing workflow disruptions.

A successful integration requires a phased approach, comprehensive testing, and the creation of tailored adaptors or middleware—custom-built solutions that act as bridges, allowing new AI and ML systems to communicate with and work alongside existing legacy systems. Through a methodical approach to implementation, companies can successfully integrate new AI/ML advancements while protecting their technological investments.

The future of AI and machine learning

While AI and ML have distinct roles in the technical advancement of industries, they are deeply interconnected, creating powerful synergies that drive innovation.

Future trends may include enhanced human-AI collaboration through increased worker training, multimodal AI integration combining diverse data inputs, and adaptive AI systems that learn and adjust in real time.

These advancements are particularly promising in industries like healthcare, where AI can analyze medical images, patient records, and genetic data simultaneously. Additionally, conversational AI assistants are evolving to provide more personalized responses by analyzing tone, sentiment, and context.

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Artificial intelligence versus machine learning