Artificial Intelligence (AI) vs. Machine Learning (ML): Key Differences and Examples

What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a broad concept that refers to machines or computers performing tasks that would typically require human intelligence. This includes tasks such as problem solving, learning, planning, understanding language, recognizing patterns, and perception. AI is essentially the science of training machines to mimic human behavior and intelligence.

AI can be classified into two main types: narrow AI and general AI. Narrow AI is designed to perform a specific task, such as voice recognition. It operates under a limited context and is highly competent at the specific task it is designed for. On the other hand, general AI, at its best, can perform any intellectual task that a human being can. It has the ability to understand, learn, adapt, and implement knowledge in different fields.

The ultimate goal of AI is to create machines that can function autonomously and intelligently, without human intervention. The pinnacle of AI development is the creation of intelligent agents, systems that perceive their environment and take independent actions to achieve human goals.

What Is Machine Learning (ML)?

Machine learning (ML) is a subset of AI that focuses on the development of computer programs that can learn from and adapt to new data, without being explicitly programmed to do so. In other words, ML is a method of feeding data to algorithms, and training them to make decisions based on that data.

There are three main types of machine learning algorithms: 

• Supervised learning: Involves training the model using labeled data, also known as ground truth. 
• Unsupervised learning: Involves training the model using data which is neither classified nor labeled, allowing the machine to act on the data without guidance. 
• Reinforcement learning: Involves training the model to make specific decisions by learning from its past experiences, rewarding it for successes and penalizing it for failures.

How Are AI and ML Connected?

AI and ML are interconnected fields. While AI is the broader concept of machines being able to carry out tasks in a way that humans would consider intelligent, ML is an application or subset of AI that allows machines to learn from data.

Machine learning is the means by which we can achieve AI. It is the process that powers many of the services we use every day – recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social media feeds like Facebook and Instagram; and chatbots like ChatGPT and Google Bard. The goal is to develop machines that can adapt their behavior without being explicitly programmed to perform a specific task.

Machine learning is a field within AI that has been able to provide powerful and practical solutions to wide-ranging  applications. But the discipline of AI is more than just ML; it includes broader aspects like deployment and operations (also known as MLOps), governance, explainability (the ability to explain or justify why an AI system made a decision), safety, and the overall impact of AI systems on individuals and society in general.

AI vs ML: Key Differences

1. Objectives

The primary objective of AI is to create systems that can function intelligibly and independently. AI aims to make our lives easier by automating tasks that require human intelligence. 

The main objective of ML is to enable machines to learn from data so they can give accurate predictions or make decisions without being explicitly programmed to perform the task.

2. Technologies

AI technologies encompass a broad range of tools, including expert systems, natural language processing (NLP), robotics, and neural networks. These technologies enable AI systems to interpret and respond to complex data, automate decision-making, and simulate human-like interactions. For instance, robotics combined with AI algorithms can lead to autonomous machines capable of performing tasks in varied environments.

ML technologies focus primarily on algorithms and statistical models. Key technologies in ML include decision trees, classifiers, and neural networks. Deep learning, a subset of ML, utilizes layered neural networks to analyze large datasets, enabling machines to make accurate predictions or identify complex patterns. These ML technologies are crucial for tasks such as image recognition, speech recognition, and predictive analytics, where the system learns from vast amounts of data to perform specific tasks more efficiently over time.

3. Requirements

AI systems require a comprehensive understanding of the domain they are designed to operate in, as well as a robust infrastructure to support complex computations and data processing. These systems often need significant computing power, sophisticated algorithms, and large datasets to mimic human intelligence and decision-making processes effectively. Additionally, AI development demands an interdisciplinary approach, integrating insights from fields like psychology, neuroscience, and linguistics.

ML requires a more focused set of prerequisites, primarily revolving around data. The quality, quantity, and relevance of the data are critical in ML, as the algorithms learn directly from the data fed into them. This means that extensive and well-annotated datasets are crucial for training accurate ML models. Furthermore, ML relies heavily on statistical methods and analysis, necessitating proficiency in areas such as probability, statistics, and data analysis. The hardware requirements for ML can vary depending on the complexity of the tasks, ranging from personal computers for basic models to thousands of powerful servers for some deep learning applications.

4. Skills and Expertise

The skills and expertise required in AI and ML reflect the technical and conceptual complexities of these fields. For AI, professionals need a broad skill set that includes programming proficiency (in languages like Python, R, or Java), a deep understanding of algorithms and data structures, knowledge of neural networks, and familiarity with AI frameworks and tools. Additionally, soft skills such as problem-solving, critical thinking, and creativity are essential, as AI development often involves designing systems that emulate complex human behaviors and cognitive processes.

In contrast, ML expertise focuses more on data-centric skills. This includes proficiency in statistical analysis, data mining, and data processing. ML professionals should be adept in using ML frameworks and libraries such as TensorFlow, Keras, or scikit-learn. They also need to understand various ML algorithms and their appropriate applications. Practical experience with handling large datasets and conducting experiments to refine ML models is also crucial. An understanding of the ethical implications and biases inherent in ML data and algorithms is increasingly recognized as essential in this field.

How Do Generative AI and Large Language Models Fit In?

Generative AI is a category within AI focusing on creating new content, such as text, images, or music, that resembles human-generated content. Large Language Models (LLMs) like Google’s Bard or Gemini series are a prime example of generative AI. These models process vast amounts of text data and generate content that can mimic human writing style and respond to highly nuanced natural-language instructions. 

LLMs are now widely used for tasks like content creation, language translation, and generating realistic dialogue for chatbots. The effectiveness of LLMs lies in their ability to understand and generate text in a contextually appropriate manner, making them valuable for a wide range of applications, including education, customer service, and content moderation.

Generative AI extends beyond text generation. In visual arts, for instance, generative adversarial networks (GANs) can create highly realistic images and even art. In these systems, two neural networks compete with each other in a zero-sum game, where one agent’s gain is another agent’s loss, enabling the generation of new, synthetic instances of data that can pass for real data. 

This capability is crucial for applications like virtual reality, game development, and the generation of training data for other AI models. The adaptability and creative potential of generative AI make it a rapidly evolving frontier in the AI field, pushing the boundaries of what machines can create and how they can collaborate with humans in creative processes.

Common Examples of AI and ML

AI Systems Improving Human Life

• Manufacturing robots: These robots automate repetitive and physically demanding tasks in factories. Equipped with AI, they can optimize production processes, improve precision and quality, and adapt to different tasks through ML and sensor data analysis.
• Self-driving cars: AI enables these vehicles to interpret sensor data, make decisions in real time, and learn from new driving scenarios. This technology aims to reduce accidents, improve traffic flow, and provide mobility solutions for those unable to drive.
• Large language models (LLMs): LLMs like OpenAI’s ChatGPT and Google’s Bard process and generate human-like text, enabling applications like virtual assistants, content creation, language translation, and information retrieval. They learn from vast text datasets to understand context, answer queries, and even simulate conversation.
• AI-driven healthcare management: AI in healthcare includes predictive analytics for patient care, personalized medicine, and medical imaging analysis. These systems can identify patterns in patient data, assist in early diagnosis, and optimize treatment plans.
• Automated financial investing: AI systems analyze market trends, predict stock performance, and manage portfolios. They use historical data and machine learning to make informed investment decisions, offering personalized advice and automated trading for investors.

Common Machine Learning Algorithms

• Linear/logistic regression: Used for predicting a continuous outcome (linear) or a categorical outcome (logistic). These algorithms model the relationship between a dependent variable and one or more independent variables.
• Decision trees: A flowchart-like structure where each internal node represents a test on an attribute, each branch the outcome of the test, and each leaf node a class label. Useful for classification and regression tasks.
• Random forest: An ensemble learning method that constructs a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees.
• Convolutional Neural Networks (CNNs): Primarily used in image processing, these networks employ a mathematical operation called convolution and have been effective in image recognition, classification, and object detection tasks.
• Transformers: A deep learning architecture designed to handle sequential data, but unlike previous models, it doesn’t require data to be processed in order. Transformers have achieved state-of-the-art results in NLP tasks.

AI vs. Machine Learning: When to Use?

Choosing between a full AI system and an ML model depends on the specific requirements and objectives of a project. While AI encompasses a broader range of technologies and applications, ML provides specific techniques for data analysis and pattern recognition with probability calculations.

AI is best utilized when the objective is to create a system that can simulate human cognitive functions. It is ideal for tasks requiring complex decision making, problem solving, and learning abilities. For instance, full AI systems are needed to develop interactive chatbots that can understand and respond to human language, autonomous vehicles that need to navigate and adapt to dynamic environments, and intelligent recommendation systems that tailor content to individual users. AI is also used in creating systems that require a mix of different technologies like NLP, robotics, and computer vision to function in a way that resembles human intelligence.

ML models are more appropriate when the primary goal is to analyze large datasets and make predictions or classifications based on that data. ML is particularly effective in scenarios where it’s necessary to identify patterns or trends that are not immediately apparent to humans. This includes applications such as fraud detection in financial transactions, where ML algorithms can learn to identify suspicious patterns, or in healthcare, where ML models can predict patient outcomes based on historical data. ML is also extensively used in areas like speech recognition, image classification, and predictive analytics, where systems learn from a large amount of data to improve their accuracy over time.

AI and Machine Learning: How They Help Security 

AI and ML have significantly impacted the way organizations approach security. By using these technologies, you can proactively identify potential threats and mitigate them before they cause harm. AI and ML are helping to improve accuracy in threat detection, drive faster response times, and reduce reliance on human intervention.

Pattern Matching and ML: Core Technologies for UEBA

User and entity behavior analytics (UEBA) systems leverage pattern matching and ML to identify potential security threats. These systems continuously monitor user and entity behavior and use ML algorithms to establish a normal behavior pattern. A significant deviation from this established pattern is flagged as a potential security threat.

Pattern matching in UEBA involves comparing current user or entity behavior with the established normal pattern. This is a complex task as it involves dealing with a large amount of data. However, with the help of ML algorithms, this task becomes manageable and efficient. ML algorithms can process vast amounts of data in a short time and identify patterns that might be impossible for humans to spot.

NLP and Translation Engines

NLP, translation engines, and text pattern recognition play a crucial role in analyzing text-based data for potential security threats.

NLP allows machines to understand and interpret human language. In the context of security, it can be used to analyze text data, such as emails, data transmitted over networks, and log data, for potential threats. For instance, it can help identify phishing emails by analyzing the language used in the email.

Generative AI and LLMs

LLMs are the evolution of traditional NLP models, and have seen incredible advances in the past few years. LLMs are able to understand and replicate patterns in text at a level that matches human performance. This means they can also be used to analyze patterns in cyberthreats, read documentation of security tools, and generate advanced queries for security systems. 

An LLM trained on historical cybersecurity data can learn common patterns and trends, and become capable of predicting threats from unseen data. This can help security analysts anticipate threats in advance and maximize the value of security tools.

AI in Exabeam Security Solutions

The Exabeam Security Operations Platform applies AI and automation to security operations workflows for a holistic approach to combating cyberthreats, delivering the most effective threat detection, investigation, and response (TDIR). AI-driven detections pinpoint high-risk threats by learning normal behavior of users and entities and prioritizing threats with context-aware risk scoring for faster, more accurate, and consistent TDIR.

AI and ML tools within the Exabeam platform offer faster, more accurate investigation and response for security operations teams. Exabeam complements TDIR workflows with AI in a way that helps analysts uplevel their skills and automates tasks for focused and consistent investigation and response.