AI vs. machine learning

What is machine learning (ML)?

Machine learning is a subcategory of artificial intelligence that enables machines to learn from past data or experiences. They aren’t explicitly programmed to produce algorithms for repeatedly accurate outcomes.

For example, speed-to-text search uses ML to translate spoken words into text on your phone. In the medical world, doctors use machine learning to speed up imaging analysis, and in banking, ML speeds up fraud detection.

There are three types of machine learning models.

Supervised learning

This type of ML is trained using labelled training data. Data scientists train the model using very clear, descriptive data. Once it’s trained, this type of machine learning model can classify data into categories and use that information to spot patterns and trends.

Reinforcement learning

With reinforcement learning, the ML model learns through trial and error. The model will generate an output, and it’s up to the data scientist to offer feedback on whether the output is correct or not. This allows the model to gather feedback from its past experiences to determine the correct actions to perform a task.

Unsupervised learning

Unlike supervised learning, which provides labelled data, unsupervised learning provides unlabeled datasets to an ML algorithm. This means the algorithm is able to spot patterns and groupings on its own without the need for human intervention.

ML models can be complex, but these algorithms can have a big impact on the quality and usefulness of their outputs. Learn more about how machine learning models work.

The difference between machine learning and AI

Machine learning and AI are closely related because ML is a subset of AI. However, ML has a different objective than AI, so it’s important not to mix up the two technologies.

Let’s look at the major differences between AI and machine learning.

Understanding AI

With AI, data scientists create intelligent systems that can perform tasks like a human. The objective of AI is to make a smart computer system with human-like intelligence that can solve complex problems without direct human input.

AI has a wide range of applications, but it’s primarily focused on maximizing opportunities and success. Because it’s more advanced, AI can deal with unstructured data. This means data scientists can upload an unlabeled dataset and still glean strong insights.

The three types of AI are weak AI, general AI, and super AI.

Understanding machine learning

The goal of machine learning is to allow machines to learn from data so that they can produce accurate outputs. With ML models, data scientists teach machines with data as a way to perform specific tasks and produce accurate results.

ML has a limited scope of applications, and it primarily focuses on accuracy and pattern identification. It can’t make sense of unstructured data — it needs structured or semi-structured data to generate accurate outputs.

The three types of ML are supervised learning, reinforcement learning, and unsupervised learning.

How AI and machine learning work together

AI and ML work together to provide innovative solutions to real-world tasks. In fact, AI systems are built using ML.

While AI and ML are different concepts, together they produce superior results.

How this works

Machine learning is less complex than AI, but it’s a driving force for AI systems. AI uses ML to complete tasks such as:

1. Data preparation. This involves selecting, cleaning, and transforming data into a format that can be used by an ML algorithm.
2. Model training. An ML algorithm is used to build a model that can make predictions and decisions based on the input data. The algorithm “learns” from the data by identifying patterns and relationships and adjusting its parameters to minimize errors. This process is repeated several times and continued until a high level of accuracy is achieved consistently.
3. Model deployment. Once the model is trained, it can be deployed in a real-world setting to make predictions or classifications based on new data as a form of artificial intelligence.

Some people assume that AI and machine learning are limited only to big companies, but these technologies are prevalent in day-to-day life. In fact, 77% of people use devices or services powered by AI — and most don’t realize it.

Deep learning is a subset of ML. It uses neural networks with three or more layers to simulate the learning abilities of the human brain. Many AI tools use deep learning to speed up automation and perform higher level tasks. Learn more about how deep learning differs from machine learning.

Benefits of AI and machine learning

AI and ML give businesses more opportunities to automate tasks and speed up their workflows. Together, AI and ML deliver benefits such as:

1. More sources of data input. Businesses are awash in data. Instead of shelving this information, AI and ML allow organizations to gather and process a greater amount of data inputs. This mobilizes data at scale automatically.
2. Better, faster decision-making. AI and ML allow organizations to leverage higher-quality data in large volumes. In practice, this means businesses can pull data-driven insights more regularly from unstructured data. This helps them be more agile and data-driven, which leads to improved decision-making across an enterprise.
3. Increased operational efficiency. AI and ML allow businesses to automate tedious tasks while reducing errors, which makes businesses more efficient and cost-effective. In fact, AI approaches can reduce errors by as much as 50% for some industries, such as retail.

Application of artificial intelligence and machine learning

Humanity has barely scratched the surface of what AI and ML can do. However, these technologies are already growing in popularity. In fact, 35% of organizations used AI in 2022, which was a 4% increase over 2021.

In 2023, we see more businesses using artificial intelligence and machine learning in these applications:

• Customer service. Organizations are relying on AI and ML to answer customer queries more quickly — and more accurately — with AI-powered chatbots. They’re also using solutions like Adobe Real-Time Customer Data Platform (CDP) to collect deeper insights on their customers to deliver impeccable service.
• Banking and finance. Financial companies use AI and ML for everything from compliance to investment management to document processing.
• Customer insights. More businesses are able to personalize the customer experience through AI-driven insights. For example, Spotify and Amazon recommendation engines use AI to gather data and automatically deliver personalized experiences.
• Cybersecurity. IT providers are increasingly using AI and ML for detecting and mitigating security threats and preventing human errors.
• Transportation. Self-driving cars and cargo ships are just a few examples of how AI and ML streamline transportation.
• Marketing strategy. Marketers use solutions like Adobe Experience Platform to generate AI-driven, real-time customer intelligence to enhance personalization.
• Healthcare. AI and ML help healthcare providers improve diagnostic accuracy, offer personalized care via virtual assistants, and detect fraud.

The future of AI and machine learning

Going forward, we expect to see an increase in demand for AI and ML. By 2029, the AI and ML industry will be worth more than $209 billion.

In the future, AI will revolutionize medicine and research, in addition to creating hyper-personalized consumer experiences. For example, Adobe Real-Time CDP generates insights and actionable steps to scale audiences through artificial intelligence and machine learning. Demand will also increase for STEM jobs to manage AI and ML solutions. In fact, computer and data scientist jobs are predicted to grow by 21% through 2031.

It’s clear that AI and ML will continue to revolutionize business as we know it. This technology often operates in the background, but going forward, it will likely play a more active role in both our work and our daily lives.

Are you ready for the next stage of AI evolution? See how machine learning powers Adobe Sensei and all Adobe Experience Cloud products.