How AI Machine Learning Works: AI vs. Machine Learning (2024)

Artificial intelligence (AI) is booming, rapidly transforming many industries and aspects of our personal and professional lives. 

And yet the field of AI is still in its infancy, and its capabilities are expected to soar in the coming years. Strong AI (human brain-level intelligence) is a possibility, albeit in the distant future. Nevertheless, AI will likely play an increasingly important role in various aspects of society in the near future.

What is artificial intelligence?

Artificial intelligence is a branch of computer science concerning the creation of systems that can reason, learn, and act without human guidance. The ultimate goal of AI research is to develop machines and algorithms that combine to form artificial neural networks, process data, recognize patterns, and react like the human brain would.

What is machine learning?

Machine learning is a subfield of AI in which machines learn from data without explicit programming. AI systems can analyze vast amounts of data to identify patterns and make predictions based on information that a human might miss, empowering an individual or business to pinpoint areas for improvement. 

Examples of machine learning

Businesses can turn to machine learning projects to streamline operations, improve customer service, and gain an edge over competitors. Machine learning has a range of applications that are already evident in many areas of life:

Image recognition

One example of machine learning is image recognition. AI systems can identify objects and scenes in images with high accuracy. This is used in facial recognition software, self-driving cars, and image tagging on social media platforms.

Natural language processing

Natural language processing is another example of machine learning in which AI can understand and process human language. Natural language processing is used with virtual assistants, translation tools, and social media sentiment analysis.

Recommendation engines

Machine learning can also be deployed for generating recommendations. AI algorithms power such systems on ecommerce platforms and streaming services, suggesting products or content you might be interested in based on your past behavior and preferences.

Machine learning vs. AI: What’s the difference?

Artificial intelligence and machine learning are data science terms that often get mixed up, but they have distinct meanings.

AI is the umbrella term and broader concept for any technology that enables machines to mimic the cognitive aspects of humans. This can include AI systems that learn from data or experience and apply that knowledge to solve problems.

Machine learning is a subfield of AI. Machine learning models rely on training data to learn and improve the algorithms’ performance of a specific task. Machine learningalgorithms don’t need explicit programming for every situation. Instead, they can learn from data to identify patterns, make predictions, and improve their accuracy over time.

Not all AI uses machine learning. There are different approaches to achieve AI. Some artificial intelligence techniques involve symbolic reasoning and logic without necessarily involving machine learning algorithms.

In a nutshell, all machine learning is a form of AI, but not all AI is machine learning. Machine learning is a tool that allows AI systems to learn and improve without needing direction from a human in every situation.

Types of machine learning

Machine learning generally falls into one of the following categories:

Supervised learning

In supervised machine learning, the algorithm is trained using labeled data sets, which consist of inputs and outputs, and the algorithm learns the relationship between the two in order to predict the outputs using future data. Common applications that end users might be familiar with include image recognition, spam filtering, and weather forecasts.

Unsupervised learning

Unsupervised machine learning deals with unlabeled, unstructured data, and the algorithm’s goal is to uncover hidden patterns. Common applications include market segmentation, anomaly detection (especially useful in cases of fraud detection), and dimensionality reduction (compressing large datasets for faster analysis).

Semi-supervised learning

This type of machine learning involves both labeled and unlabeled data—especially when labeled data sets are scarce and expensive to acquire. One example is the case of developing a machine learning model to diagnose rare diseases—such data is sensitive, expensive to obtain, and rare.

The algorithm leverages labeled data sets to learn a model, and then uses unlabeled data to refine and improve the model’s performance. Common applications include text classification (categorizing documents), image segmentation (dividing an image based on pixels that share similar characteristics, sometimes used for analyzing medical scans to identify tumors), and sentiment analysis (determining the emotional tone of text).

Reinforcement learning

With reinforcement learning, the algorithm learns through trial and error in a simulated environment, receiving rewards for desired actions as well as penalties for undesired actions. The point is that the algorithm learns to take actions that maximize its rewards. Common applications include training bots to play games, training self-learning robots, and optimizing resource allocation in complex systems (such as coordinating the flow of materials across multiple stages of supply chain production and distribution).

How businesses can use machine learning

Machine learning has many applications in business settings, including:

Data driven decision-making

Machine learning can analyze vast amounts of data from various sources to uncover hidden patterns and trends. This stream of information when fed into an artificial intelligence system can aid strategic, data driven decision-making across different departments.

Improving the customer experience

Machine learning algorithms can analyze customer data (such as purchase history and browsing behavior) to recommend products or services relevant to their interests, which could help increase customer satisfaction and sales. Machine learning-powered virtual assistants, or chatbots, can answer customer questions around the clock to provide support and resolve basic issues. This reduces reliance on human customer-service agents for simple inquiries, freeing them to perform tasks with greater complexity.

Improving business operations

Machine learning can help improve fraud detection and assess risk management by analyzing financial transactions to identify patterns indicative of unauthorized activity to help businesses protect themselves from financial losses. Machine learning can also improve supply chains, analyzing data to predict demand fluctuations and optimize inventory management. This helps businesses ensure they have the right products in stock at the right time, reducing costs and increasing efficiency.

Marketing and sales optimization

Machine learning can increase advertising efficacy by analyzing customer data and demographics to identify the ideal target audiences for marketing campaigns. This lets businesses make the most of their advertising budgets. Machine learning can also help with lead scoring and sales forecasting by analyzing customer interactions to predict which leads are more likely to convert into sales, helping sales teams prioritize their time and focus their efforts on more qualified leads.

Product development

Machine learning can also help improve new product designs and upgrades based on customer feedback and usage data to identify trends and inform product development strategies. This enables businesses to create products that better cater to customer needs. Machine learning is used in predictive maintenance by analyzing sensor data from machines, which helps prevent equipment failures before they occur. This proactive approach avoids costly downtime and emergency maintenance repairs.