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).

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.

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.

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#

Want to explore this world further now that you've got a grip on the differences between artificial intelligence and machine learning? Let's keep the learning going! Embark on your journey with Educative's comprehensive courses on AI and ML. Our platform provides a diverse array of courses designed to increase your understanding and skills:

• Mastering Artificial Intelligence with Azure Cognitive Services

• A Practical Guide to Machine Learning with Python

• Machine Learning with Python Libraries

• Build Your Own Chatbot in Python

These courses can help you learn about AI and ML through detailed lessons and hands-on exercises. So don't wait! Dive in and enhance your understanding of AI and ML with these comprehensive courses.