What are the key differences and benefits? This guide provides definitions and practical advice to help you understand these concepts as you evaluate AI vs machine learning for your organization.
Like a hammer in a toolbox, machine learning (ML) is a specific tool within the broader scope of artificial intelligence (AI). ML is a technique that focuses on developing algorithms and models for learning and adapting to tasks and data. Artificial intelligence encompasses a wide range of techniques and aims to create intelligent machines capable of human-like intelligence.
As the data in your organization expands in both complexity and size, the integration of automated and intelligent systems becomes crucial for you to streamline tasks, extract value, and derive actionable insights that lead to improved outcomes.
Machine learning is a subset of artificial intelligence focused on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. So, instead of relying on your instructions, ML systems learn from data and improve their performance over time through experience.
The process typically requires you to feed large amounts of data into a machine learning algorithm. Typically, a data scientist builds, refines, and deploys your models. However, with the rise of AutoML (automated machine learning), data analysts can now perform these tasks if the model is not too complex.
The ML algorithm analyzes and identifies patterns, relationships, and trends within the data and then uses these insights to build a mathematical model that can make predictions, can power predictive analytics, or take actions when presented with new, unseen data.
Key ML techniques:
Machine learning has a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, fraud detection, prescriptive analytics, and autonomous vehicles. It plays a crucial role in enabling AI systems to adapt, improve, and perform complex tasks with minimal human intervention.
Download the AutoML guide with 5 factors for machine learning success
Artificial intelligence (AI) refers to the field of computer science focused on developing intelligent machines that can perform complex tasks, such as analyzing, reasoning, and learning that would typically require human intelligence. AI systems are designed to perceive their environment, reason and learn from data, and make decisions or take actions to achieve specific goals.
Explainable AI (XAI) refers to a set of techniques and processes that help you understand the rationale behind the output of a machine learning algorithm. With XAI, you can meet regulatory requirements, improve and debug your models, and have more trust in your AI models’ decisions and predictions.
As stated above, AI includes machine learning. It also encompasses other techniques such as deep learning, natural language processing, computer vision, and robotics as described below.
Key AI techniques:
AI’s applications span across industries, including healthcare, finance, transportation, and entertainment. AI has the potential to automate your processes, enhance your productivity, and solve complex problems.
Among the many AI techniques described above, machine learning has proven to be especially effective in a wide range of applications. This is why ML has gained such popularity and become a central component of many AI implementations.
In this representation of AI vs machine learning vs deep learning, AI is the broadest concept, with machine learning (ML) as a subset of AI. Within ML, there are neural networks, which are computational models with interconnected artificial neurons. And deep learning refers to a specific type of neural network architecture which has multiple layers for hierarchical representation learning. So, deep learning is a subset of neural networks, which in turn is a subset of ML, and ML is a subset of AI.
Here’s how you’d incorporate an ML model in your artificial intelligence system:
These steps may not necessarily be linear and may involve iterations or adjustments based on your specific project requirements and feedback received during the development process.
Machine learning and artificial intelligence are related concepts and the terms are often used interchangeably. But they’re actually distinct concepts. As you can see below comparing AI vs machine learning, the two concepts are more alike than different and it’s the aspect of human-like intelligence that separates them.
Features | Machine Learning | Artificial Intelligence |
---|---|---|
Learning
|
Yes
|
Yes
|
Automation
|
Yes
|
Yes
|
Problem-solving
|
Yes
|
Yes
|
Decision-making
|
Yes
|
Yes
|
Data-driven
|
Yes
|
Yes
|
Adaptability
|
Yes
|
Yes
|
Pattern recognition
|
Yes
|
Yes
|
Human-like intelligence
|
No
|
Yes
|
Broad applications
|
Yes
|
Yes
|
Interpretability
|
Yes
|
Variable
|
Model complexity
|
Variable
|
High
|
Feature engineering
|
Yes
|
Yes
|
Scalability
|
Yes
|
Yes
|
Real-time processing
|
Yes
|
Yes
|
Ethics and bias considerations
|
Yes
|
Yes
|
ML and AI offer many benefits across industries, revolutionizing processes, enabling new capabilities, and driving advancements in technology and society. As stated above, ML is a subset of AI, so all of machine learning’s benefits described below can also be attributed to AI.
The key applications of AI and ML include:
Using ML models to make predictions and forecasts based on historical data, finding applications in sales forecasting, demand prediction, risk assessment, and stock market analysis.
Applying ML techniques to identify patterns and anomalies in financial transactions, helping detect fraudulent activities and prevent unauthorized access.
AI and ML find applications in credit scoring, algorithmic trading, fraud prevention, risk assessment, financial analysis, and personalized financial recommendations.
Enabling machines to understand, interpret, and generate human language, which finds applications in chatbots, virtual assistants, language translation, and sentiment analysis.
Empowering machines to interpret and analyze visual data, enabling applications such as image and object recognition, video analysis, autonomous vehicles, and medical imaging.
Utilizing ML algorithms to provide personalized recommendations, commonly seen in recommendation engines for movies, products, music, and content.
AI and ML are used for medical image analysis, disease diagnosis, drug discovery, personalized medicine, and patient monitoring, aiding in improved healthcare outcomes.
Enabling self-driving cars, drones, and robots to perceive and interact with their environment, making autonomous decisions based on ML algorithms.
Reduce risk by automatically analyzing credit risk or loan default likelihood.
AI and ML are employed for optimizing manufacturing processes, predictive maintenance, quality control, and supply chain management.
AI and ML techniques enhance immersive experiences, character behavior, natural language interaction, and intelligent opponents in virtual reality and gaming applications.
AI and ML are used for energy optimization, demand forecasting, grid management, renewable energy integration, and environmental monitoring.
These are just a few examples, and AI and ML have broad applications across various industries, driving innovation and improving efficiency in many domains.