Difference between artificial intelligence and machine learning

In today's digitally evolving world, artificial intelligence (AI) and machine learning (ML) are two concepts that have attracted significant attention, boosted development, and sparked debate. Though frequently used synonymously in conversations ranging from tech seminars to casual coffee shop talks, they have distinct characteristics and potential.

Understanding the distinctions and interconnections between AI and ML isn't just for tech enthusiasts or professionals. With these technologies touching every aspect of modern life—from how we shop and entertain ourselves to critical areas like healthcare and finance—understanding the subtle differences between artificial intelligence and machine learning is becoming increasingly important. We'll explore their core aspects, applications, and real-world implications.

What is artificial intelligence?#

Artificial intelligence (AI) embodies a broad domain of computational study, focusing on crafting mechanisms that mirror human thinking processes. This covers diverse mental tasks, from problem-solving and decision-making to grasping language and perceiving senses like seeing and hearing. Here are some AI techniques:

What is machine learning?#

Machine learning (ML) can be viewed as a modern approach to realizing the objectives of AI. Instead of relying solely on hard-coded behaviors, ML revolves around the idea of feeding algorithms with data and letting them learn patterns and make decisions from this data autonomously. Data is the fuel for ML algorithms. With the increase of big data and sophisticated tools to process and analyze it, ML models have become more capable. Here are some ML techniques:

• Supervised learning: This is the most common technique where algorithms are trained on a dataset where the outcome variable (or label) is known. The goal is to learn mapping from inputs to outputs. Examples include regression and classification tasks.

• Unsupervised learning: Here, algorithms are exposed to data without explicit labels and are tasked with finding structures or patterns on their own. Clustering and association are common unsupervised learning tasks.

• Reinforcement learning: In this technique, an agent learns how to behave in an environment by performing actions and receiving rewards or penalties. It's like teaching a pet new tricks: the pet is the agent, the environment is the place where the pet can perform tricks, the actions are the tricks themselves, and the rewards (or penalties) are the treats (or the lack of them).

Generative AI and foundation models: The new era of AI#

In recent years, a new class of AI systems called foundation models has completely changed how we think about artificial intelligence. These are large-scale models trained on massive amounts of data and capable of solving a wide range of tasks — from writing essays and generating code to creating images and music.

Examples include large language models (LLMs) like GPT-4, image generation models like Stable Diffusion, and multimodal models that can process text, images, and audio simultaneously. These models are a perfect example of how machine learning powers artificial intelligence — they’re built using deep learning techniques but applied in ways that feel intelligent and human-like.

This shift shows that AI isn’t just about mimicking human decision-making anymore — it’s about creating systems that can understand, generate, and reason at scale.

Self-supervised learning and pretraining: How modern ML learns#

Traditional machine learning relies heavily on labeled data — but labeling millions of examples is expensive and time-consuming. That’s where self-supervised learning comes in. Instead of requiring human-annotated data, models learn from patterns within the data itself.

For example, language models can predict the next word in a sentence, and image models can learn to fill in missing parts of a picture. These tasks teach the model deep representations of language or vision that can then be fine-tuned for specific tasks.

This pretraining approach is behind many of today’s most powerful AI systems. It shows how modern ML has evolved beyond simple supervised or unsupervised learning to a more efficient, scalable paradigm.

How are AI and ML connected?#

The interrelation between AI and ML is intrinsic. ML provides the algorithms, and AI is the broader field that applies these algorithms. Here’s a simple analogy: If AI were a fruit-bearing tree, ML would be one of its most fruitful branches. The techniques and principles of ML have spurred significant advances in AI applications. In many modern AI applications, the boundaries between AI and ML are blurred since ML algorithms are the driving force behind these systems. As technology has evolved, the overlap between AI and ML has expanded, and we might not fully segregate AI and ML.

From research to production: The rise of MLOps#

Building a machine learning model is only half the battle — deploying, maintaining, and improving it in the real world is just as important. That’s where MLOps (Machine Learning Operations) comes in.

MLOps combines the principles of DevOps with ML workflows, enabling teams to:

• Automate data pipelines and model training

• Monitor model performance in production

• Detect and fix model drift (when data changes over time)

• Version models and roll out updates safely

For organizations, this means machine learning can move from a research project to a reliable, scalable product that evolves with new data and changing needs.

Ethics, bias, and explainability in AI#

As AI becomes more powerful and widespread, it also raises important questions about fairness, transparency, and accountability. Machine learning models can unintentionally learn biases from their training data, leading to unfair or harmful outcomes — for example, in hiring algorithms or facial recognition systems.

To address these challenges, researchers and engineers are developing tools and practices for Explainable AI (XAI). Techniques like LIME and SHAP help reveal how models make decisions, making them more transparent and trustworthy.

Including ethics and explainability in any discussion of AI and ML is now essential — not just from a technical standpoint but also for regulatory compliance and public trust.

Key differences#

While both AI and ML aim to develop intelligent systems, their approaches and scopes differ significantly. ML can be viewed as one approach among many for realizing the objectives of AI. ML techniques serve as powerful tools for achieving these objectives by focusing on data-driven learning and decision-making. The table below highlights the key differences between AI and ML:

In the physical world, we're seeing the development of autonomous vehicles that rely heavily on broad AI principles for navigation, decision-making, and interaction with the environment, as well as specific ML algorithms for tasks like pedestrian detection. Meanwhile, ML’s impact isn’t just in predictive systems or recommendation engines. In healthcare, ML models help predict diseases in patients from complex bioinformatics datasets, improving treatment precision.

Hybrid AI: Combining symbolic reasoning with machine learning#

Machine learning isn’t the only approach to building intelligent systems. Before ML dominated the field, many AI systems used symbolic reasoning — a rule-based approach that relies on logic and knowledge representation.

Today, researchers are combining the strengths of both approaches in what’s known as hybrid AI. By integrating knowledge graphs or symbolic logic with machine learning, these systems can reason more effectively, explain their decisions, and operate with less data.

Hybrid AI shows how the future of AI isn’t one-size-fits-all — it’s about blending multiple techniques to achieve human-level reasoning and decision-making.

AutoML and the democratization of AI#

Not everyone building AI today is a machine learning expert — and that’s by design. Tools like AutoML, no-code platforms, and low-code AI builders are making it possible for developers, analysts, and even non-technical teams to train and deploy models with minimal coding.

This democratization of AI is helping more industries adopt ML quickly and efficiently, driving innovation in fields like healthcare, finance, education, and logistics.

Edge AI and federated learning: Bringing ML closer to the user#

Machine learning doesn’t always have to run in the cloud. With edge AI, models are deployed directly on devices like smartphones, IoT sensors, and autonomous vehicles. This reduces latency, improves privacy, and allows AI to function even without internet access.

Meanwhile, federated learning enables models to learn from data distributed across many devices — like your phone — without that data ever leaving the device. This privacy-preserving approach is becoming increasingly important in applications like healthcare and personalized recommendations.

Real-world examples#

Here are some real-world examples of the applications of AI and ML:

AI deployments#

• Natural language processing (NLP): NLP concerns itself with the interaction between computers and human language. It’s about programming systems to process and analyze vast amounts of natural language data. For example, virtual assistants like Apple’s Siri and Amazon’s Alexa employ advanced natural language processing methods to understand and respond accurately to user commands. Applications such as ChatGPT, Microsoft Bing, or Google Bard are AI applications that simulate human-like conversation. It powers chatbots and virtual assistants, providing human-like responses in natural language interactions.

• Robotics: This domain deals with the design, creation, implementation, and use of independent robotic systems. For example, Boston Dynamics’ Spot is a robot that can navigate various terrains, showcasing AI’s prowess in motion and environmental interaction.

• Expert systems: These are computer systems that mimic the decision-making abilities of a human expert in specific domains. For example, IBM’s Watson, particularly in its application in healthcare, aids doctors in diagnosing diseases by analyzing vast medical datasets.

ML deployments#

• Recommendation systems: Many ML algorithms are designed to suggest items to users based on input data. These systems are particularly prevalent in online platforms, where personalized user experiences are crucial. For example, Spotify's Discover Weekly playlist uses ML to analyze users’ listening habits and recommends songs they might enjoy.

• Predictive analytics: Predictive analysis employs statistical algorithms and machine learning methodologies to ascertain the probability of forthcoming events by leveraging past data. For example, predictive analytics is employed by credit card companies to assess the likelihood of a transaction being fraudulent.

• Image recognition: This is about giving machines the ability to visually recognize and interpret the world. ML algorithms are trained on vast datasets containing images to enable them to recognize other images in the future. For example, Google Photos employs image recognition to categorize photos, enabling users to search for terms like “beach” or “dog” and find relevant photos from their albums.

Can we learn AI without ML?#

AI covers a wide range of concepts and applications. It’s important to clarify that not all AI systems rely on ML. To begin with AI, it’s a good idea to study its foundational principles, including understanding its objectives and various problem-solving methods. These principles provide us with a solid grasp of artificial intelligence, its history, and potential uses. It’s worth remembering that if we aim to create advanced AI systems or engage in practical AI projects, a deep understanding of ML is often beneficial. ML is a part of AI that concentrates on using statistical methods and algorithms to enable AI systems to learn and adapt based on data. Exploring ML can be extremely valuable in gaining a more comprehensive grasp of AI and the ability to construct AI systems.

Conclusion and future implications#

AI's broader implications, including potential biases in decision-making or the automation of complex tasks, might differ from those presented by machine learning models that serve more specific, data-driven purposes. In the end, while the distinctions between AI and ML are both subtle and substantial, understanding them equips us with a clearer perspective of the digital horizon, enabling us to make use of their potential responsibly and effectively.

Next learning steps#

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