Machine learning vs. AI: Understanding the differences

It might feel like machine learning is only a recent concept, but the term was actually coined over 70 years ago by computer scientist Arthur Samuel. He defined it as “the field of study that gives computers the ability to learn without explicitly being programmed,” which is still a very apt and accurate definition.

In more modern terms, machine learning is a subset of AI that uses advanced algorithms to process large amounts of data to imitate how humans learn. This essentially means that the more information it processes, the more accurate it can become, and the better it is at solving problems. It does this by analyzing the data to identify relationships and patterns. There are four different types of machine learning: supervised machine learning, unsupervised machine learning, semi-supervised learning, and reinforcement learning.

The reason machine learning is so useful is that it can quickly learn to perform complex activities, without needing algorithms bespoke to the problem it's solving. This makes it great at predicting trends, quickly automating complex tasks, and identifying patterns or anomalies in data.

Read our guide What is machine learning? for a more comprehensive overview of machine learning and its capabilities.

Unlike machine learning, artificial intelligence isn’t one specific technology. It’s actually a broad field of approaches aimed at performing tasks and solving problems that typically require human intelligence. This includes machine learning, as well as things like deep learning, natural language processing, and computer vision.

The applications for AI are endless, but common uses include things like problem-solving, learning, perception, communication, decision-making, and creativity. Currently, the most popular type of artificial intelligence is generative AI. This form of AI can create things like written content, music, computer code, and art. Generative AI apps like ChatGPT, DALL·E, and Midjourney have all rocketed in popularity because of the impressive nature of their output.

But a lot of controversy swirls around generative AI, especially about plagiarism concerns and hallucinations. This stems from the technology using existing content to inform how it creates its own “original” content. As the AI field continues to grow, questions will continue to be asked about its ethics, and it will be a challenge in its own right to decide on and enforce ways to keep everyone safe. 

Despite the terms often being used interchangeably, machine learning and AI are separate and distinct concepts. As we’ve already mentioned, machine learning is a type of AI, but not all AI is, or uses, machine learning. Even though there is a large amount of overlap (more on that later), they often have different capabilities, objectives, and scope.

The broader aim of AI is to create applications and machines that can simulate human intelligence to perform tasks, whereas machine learning focuses on the ability to learn from existing data using algorithms as part of the wider AI goal.

AI can solve a diverse range of problems across various industries — from self-driving cars to medical diagnosis to creative writing. Sometimes these problems are similar, but often they are wildly different.

Machine learning, on the other hand, is much more limited in its capabilities. The algorithms are great at analyzing data to identify patterns and make predictions. But it can’t solve broader problems or be adapted in the same way as AI.

The best way to look at the difference between them is that machine learning is a single (but important) cog in the bigger AI machine. That machine might be a push-bike, or it could be a space rocket. It might not be as dynamic, but it’s a vital part that can’t be overlooked or taken for granted.

When we talk about machine learning and AI, the term “overlap” is slightly misleading. It’s not quite that they overlap, but that machine learning is often a large and integral part of the AI application itself — much like how your ability to learn as a human isn’t separate from your intelligence.

The best way to understand this is by looking at some of the key ways machine learning powers AI:

AI’s primary goal is to mimic human intelligence and abilities, such as reasoning, decision-making, and adaptability. It achieves this with a combination of techniques, but the most critical method is almost always machine learning. That’s because these machine learning algorithms make it possible for the AI to analyze information, identify patterns, and adapt its behavior. 

Similarly, decision-making and predictions are both key parts of nearly all AI tools. This is because assessing information, weighing up options, and deciding the best next step is an integral part of any intelligence. Machine learning is how AI tools can make these data-driven decisions. The machine learning algorithms analyze huge amounts of data to identify the patterns that facilitate this decision-making.

Even though we talked about machine learning being more limited in scope, it does make it possible for AI tools to solve and address varied problems across different sectors. Machine learning is behind many of these applications, making it possible for AI to be so dynamic. 

AI, powered by machine learning, has the potential to solve endless problems across multiple fields. But what does that really look like? Here are a few ways AI is already automating tasks and simplifying complex problems:

• Generative AI: Creativity is no longer a trait exclusive to humanity. AI and machine learning have made it possible for machines to genuinely beautify art, generate songs, and even write poetry. It can also write code and documentation, as well as create ad-hoc training materials.

• Process automation: AI will not only automate tedious and repetitive processes, but because of machine learning, it can learn to improve and optimize them. This can range from streamlining customer service communications to analyzing complex financial data.

• Data-driven insights: Decision-making is a key part of both work and life. But sometimes it’s impossible to take in all the data you need to make the best decision. AI can analyze vast amounts of data in a short amount of time, identifying the best decision based on the relevant data. 

• Personalization and recommendation: Because of its ability to learn and adapt, AI with machine learning can create genuinely personal experiences. Whether it’s streaming TV shows or shopping for insurance, these systems can learn our behavior and preferences to make sure we’re only shown what we want to see. 

Here at Elastic®, we’ve worked hard to make it as simple as possible to harness the power of AI and machine learning within your own application. To achieve this, we built the Elasticsearch Relevance Engine (ESRE). ESRE is a set of developer tools designed to help you build search powered AI applications quickly and easily. With ESRE, you can build:

• Semantic search: In addition to Elastic’s keyword matching capabilities, ESRE allows you to use vector embeddings and transformer models to understand the deeper meaning behind user requests.

• Relevance ranking: Industry-leading ranking features, such as traditional keyword search and hybrid search (combining text and vector search), can be used for all types of information domains.

• Vector database: ESRE’s full capabilities include creating embeddings and the storage and retrieval of vectors. 

• Data ingestion tools: This toolset includes a web crawler, database connectors, third-party data integrations, and custom connectors with APIs.

• Elastic Learned Sparse EncodeR (ELSER): A sparse vector retrieval model, trained by Elastic, enables you to perform semantic search for more relevant search results. It’s an out-of-domain model, which means it does not require fine-tuning on your own data, making it adaptable for various use cases out of the box.

• Bring your own model: Use whatever AI platforms and models you want using our third-party integration or third-party models (like GPT-3 and 4).

Last year, we also launched the Elastic AI Assistant for Security and Observability. The AI Assistant is a generative AI sidekick that bridges the gap between you and our search analytics platform. This means you can ask natural language questions about the state or security posture of your app, and the assistant will respond with answers based on what it finds within your company’s private data.

No longer reserved for sci-fi, AI and machine learning are now revolutionizing everything from art to healthcare. But while they might seem interchangeable, there’s a clear and distinct difference between the two technologies. AI is a big, ambitious technology, powered by machine learning behind the scenes.

As both technologies continue to develop, the possibilities are truly endless. And at Elastic, we’re committed to making these tools as accessible as possible. From the powerful capabilities of ESRE, to the AI assistants that make the lives of DevOps and security analysts a bit easier, we hope we can contribute to the growing world of artificial intelligence, machine learning, and all the problems they will solve.

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