Machine Learning vs AI

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.

Diagram showing the relationship between AI, ML, Neural Nets, and Deep Learning.

Machine Learning vs AI

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.

What is Machine Learning?

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.

Diagram showing how data is processed in an ML Model.

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:

  • Supervised Learning: Training a model using labeled data, where the desired output is known, to predict or classify new unseen examples.

  • Unsupervised Learning: Discovering patterns and structures within unlabeled data without explicit guidance.

  • Semi-Supervised Learning: Combining labeled and unlabeled data to train a model, leveraging both supervised and unsupervised techniques.

  • Reinforcement Learning: Teaching an agent to learn optimal behaviors by receiving rewards or punishments based on its actions in an environment.

  • Transfer Learning: Utilizing knowledge learned from one task or domain to improve performance on a different but related task or domain.

  • Deep Learning: Employing neural networks with multiple layers to learn complex patterns and representations from data.

  • Ensemble Learning: Combining multiple models to make predictions or decisions, often resulting in improved accuracy and robustness.

  • Active Learning: Interactively selecting and labeling the most informative data instances for training, optimizing the learning process.

  • Online Learning: Continuously updating and refining a model as new data arrives in a sequential manner.

  • Feature Engineering: Transforming and selecting relevant features from raw data to improve the performance and interpretability of models.

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.

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What is Artificial Intelligence?

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:

  • Machine Learning: Using algorithms to enable computers to learn from and make predictions or decisions based on data.

  • Natural Language Processing: Enabling computers to understand, interpret, and generate human language.

  • Computer Vision: Teaching computers to understand and analyze visual data, such as images or videos.

  • Deep Learning: Employing neural networks with multiple layers to process and extract complex patterns and features from data.

  • Reinforcement Learning: Training agents to make sequential decisions by receiving feedback from their environment.

  • Knowledge Representation and Reasoning: Representing information in a structured manner and using logical rules for problem-solving and decision-making.

  • Expert Systems: Building computer programs that emulate human expertise in a specific domain to provide intelligent recommendations or solutions.

  • Robotics: Combining AI with robotics to develop intelligent machines capable of perceiving, interacting, and manipulating the physical world.

  • Genetic Algorithms: Applying evolutionary principles to solve complex problems by iteratively generating and refining solutions.

  • Fuzzy Logic: Handling uncertainty and imprecise information by using degrees of truth rather than strict binary values.

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.

How ML and AI Work Together

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.

Diagram showing the relationship between AI, ML, Neural Nets, and Deep Learning.

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:

Diagram showing the steps to incorporate predictive modeling.

  1. System Setup: Build an AI system using machine learning and other techniques.

  2. Problem Definition: Clearly define the problem you aim to solve and determine if it’s suitable for an ML approach.

  3. Data Collection: Gather relevant data that’s representative of the problem domain and ensure it is properly labeled and structured.

  4. Data Exploration: Review your raw dataset to uncover characteristics and initial patterns for further analysis. This will help you understand the size and quality of your data, detect outliers or anomalies, and identify potential relationships among data elements, files, and tables.

  5. Data Preprocessing: Clean the data by handling missing values, outliers, and performing transformations or feature engineering to make it suitable for training.

  6. Model Selection: Choose an appropriate ML algorithm or model architecture based on the problem type, available data, and performance requirements.

  7. Model Training: Split the data into training and validation sets, feed it to the chosen algorithm/model, and iteratively adjust the model's parameters to optimize performance.

  8. Model Evaluation: Assess the trained model's performance using appropriate evaluation metrics on the validation set to ensure it meets the desired criteria.

  9. Adjust Hyperparameters: Fine-tune the model by adjusting hyperparameters (e.g., learning rate, regularization) through techniques like grid search or random search to improve performance.

  10. Model Validation: Test the final model on a separate, unseen test dataset to obtain a reliable estimate of its performance.

  11. Deployment: Integrate the trained model into a production environment, which may involve implementing it within a software system such as augmented analytics, API, or cloud platform.

  12. Monitoring and Maintenance: Continuously monitor your model's performance in the deployed environment, retraining or updating it as necessary to account for changing data patterns or evolving requirements.

  13. Iterative Improvement: Gather user feedback, collect new data, and periodically re-evaluate and refine your ML model to enhance its performance and address any limitations.

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 vs AI: Key Differences and Benefits

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

Benefits

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.

  • Automation: ML enables automation of your tasks and processes, reducing human effort and increasing your efficiency.

  • Data-Driven Decision Making: ML algorithms analyze your data to make predictions and inform decision-making, leading to more accurate and data-driven outcomes.

  • Personalization: ML algorithms can personalize experiences and recommendations based on individual preferences and behaviors.

  • Efficiency and Productivity: ML automates tasks, optimizing resource allocation and increasing productivity.

  • Real-time Insights: ML algorithms can process your data in real-time, providing timely insights for proactive decision-making.

  • Scalability: ML models can handle large and diverse datasets, making them scalable for handling big data.

  • Adaptability: ML models can adapt and learn from new data, improving performance over time.

  • Specific Task Focus: ML is designed to solve specific tasks and learn from data related to those tasks, making it efficient for specialized applications.

  • Pattern Recognition: ML algorithms excel at recognizing patterns and extracting insights from complex data.

  • Efficiency in Structured Data: ML algorithms are particularly effective with structured data, which is organized and labeled.

Applications of AI vs ML

In this section, we explore a range of real-world applications showcasing the diverse and impactful use cases of Artificial Intelligence (AI) and Machine Learning (ML) technologies. As you’ll see, many are examples of predictive analytics.

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Predictive Analytics

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.

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Fraud Detection

Applying ML techniques to identify patterns and anomalies in financial transactions, helping detect fraudulent activities and prevent unauthorized access.

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Financial Services

Natural Language Processing (NLP)

Computer Vision

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Recommender Systems

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Healthcare and Medicine

Autonomous Systems

Smart Assistants

Industrial Automation

Virtual Reality and Gaming

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Energy and Sustainability

These are just a few examples, and AI and ML have broad applications across various industries, driving innovation and improving efficiency in many domains.

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