Read on to understand the differences between Artificial Intelligence and Machine Learning so you can choose the right technology for your company as it adapts to a new world.
Can you remember the last time people were as excited by a technological development as they are about Artificial Intelligence (AI)? Although the idea of simulating human intelligence has existed for decades, this technology has only recently started to develop fully. This has allowed for AI programs that can process language, recognize patterns, and interpret data.
With the growing use of AI tools such as ChatGPT, more businesses are considering how they can use AI to increase their business productivity. By 2025, 80% of companies are expected to have adopted some form of intelligent automation. However, this doesn’t mean that most companies have a strong understanding of what AI can achieve.
One of the most widespread uncertainties is over the differences between AI and machine learning. People have often used these terms interchangeably, but machine learning actually refers to a specific form of AI. Understanding these differences will allow you to choose the right technology for your company as it adapts to a new world with AI at its center.
What is Artificial Intelligence?
Unless you’ve been living under a rock, you’ll have heard a lot about AI over the last year. But before you start delving into complex questions like “What is a convolutional layer?” or “How does natural language processing work?” you need to know exactly what everyone is getting so excited about.
Technology that uses AI aims to perform a task that would have historically required human intelligence. AI technologies are designed to analyze and evaluate very large datasets and can make decisions based on the patterns in these datasets. This allows them to perform tasks such as translation or voice recognition.
All of our lives have already been significantly impacted by AI. It is the technology that allows virtual assistants like Siri and Alexa or online chatbots to operate. It is also integral to search engines such as Google or Bing and is used to recommend new series on platforms like Netflix. Aside from internet applications, self-driving cars are also reliant on AI.
However, all of these examples are forms of “narrow AI.” This is because they can only carry out tasks they have been programmed to do. Once the AI has completed this task, it cannot apply anything learned from it to other duties. It can’t “learn” in the way humans do.
Almost all AI that exists today falls under the category of narrow AI. “General AI” is a broader understanding of AI that is more able to adapt and mimic the complexities of the human brain and is currently at the cutting edge of research into AI.
What is Machine Learning?
Machine learning is a specific part of AI. Programs that run on machine learning technology learn from datasets and make predictions from this, allowing them to improve accuracy and adapt to new tasks. This makes machine learning especially effective when traditional programming is unable to account for unexpected inputs.
- Supervised machine learning —which works with labeled data—can be used to predict future outcomes.
- Unsupervised machine learning allows programs to complete more complex tasks. This is where the algorithm is able to recognize patterns in unlabeled data.
- Reinforcement machine learning trains an algorithm to perform tasks in an unfamiliar environment through feedback.
These different types of machine learning show the potential of this form of AI. An example of this is in healthcare, where machine learning analyzes patient data before predicting probable health outcomes. As well as this, the marketing industry has started to use machine learning to create targeted advertising campaigns that suit trends identified by the AI program.
Artificial Intelligence vs. Machine Learning: The Key Differences
As we’ve seen, AI and machine learning refer to slightly different technologies. There are many ways in which they differ from each other, but we’ll discuss the most important differences. This will allow you to distinguish between AI and machine learning when choosing AI technology for your company.
- Range
The main difference between AI and machine learning is the scope of each term. AI refers to all technologies that aim to replicate forms of human intelligence, while machine learning refers only to a specific part of AI.
Machine learning is, therefore, more limited than AI in general. It focuses on specific tasks in which the AI program learns from a dataset and applies this knowledge to the task.
- Dependence on Data
Similarly, machine learning depends on large amounts of data to make decisions. For instance, if you want to use a machine learning system to predict financial markets, you’ll need to provide it with sufficient financial data from the past. This extract transform load example will explain in-depth how data is prepared and fed into these programs.
However, AI is not necessarily reliant on existing datasets. AI systems can be programmed to use logical decision-making processes, which can lead to creative outcomes rather than the outcome predetermined by the dataset used in machine learning.
- Flexibility
The dependence on data in machine learning contributes to another key difference between AI and machine learning. AI is generally not reliant on datasets; it can be more flexible than machine learning.
While machine learning models are typically used in predictive analytics, AI is far more flexible. Qualities such as problem-solving and decision-making —traditionally restricted to humans—can be adopted by the most complex AI systems, enabling engineers and programmers to overcome a far wider range of problems.
- Success and accuracy
The accuracy of machine learning is its main benefit. As machine learning systems analyze very large datasets and identify patterns, the results that they produce are often incredibly accurate.
On the other hand, AI is more helpful if you want to achieve broader success in completing a specific task or reaching a defined outcome. While machine learning will produce results that are measured effectively, these results are not necessarily helpful for your overall aim. As AI systems are designed to perform more complex tasks, they can adapt to suit your aims and find overall success.
Artificial Intelligence vs. Machine Learning: The Future of Business
The effect of AI is already becoming clear. Through online chatbots and applications such as Alexa, it has begun to transform our lives, and their potential is only growing. This means that it’s vital to know the differences between AI and machine learning.
Machine learning is a specific part of AI that analyses large datasets before it creates accurate predictions based on those datasets. On the other hand, AI is a more general description that can include a wider variety of intelligent systems.
To make the most out of the exciting new opportunities AI technology offers, you’ll need to understand how to use both machine learning and AI. You should aim to use the accurate results produced by machine learning alongside an AI system that can use these results to succeed at a wider business objective.
In 2023 and beyond, AI and machine learning will only become more popular. As you integrate these technologies into your business model, it’s crucial that you can identify the differences between AI and machine learning
