Machine learning helps create artificial intelligence 🤖

Artificial Intelligence (AI) and Machine Learning (ML) continue to be hot topics in many industries and popular news stories. Given they are commonly seen together, AI and ML are two terms that people often confuse with one another, however there are important distinctions between the terms. In this post we’ll explain the key differences between artificial intelligence and machine learning.

What is Artificial Intelligence?

Artificial intelligence is the science of creating machines that operate in ways that resemble human behavior. The concept has been around for some time – John McCarthy is credited with founding the discipline of artificial intelligence in 1956 when he and other researchers first built machines that performed tasks similar human cognition. Today, AI is a discipline that covers a wide variety of cognitive tasks performed by computers such as decision making, image recognition and language processing.

One example of artificial intelligence in the modern world is predictive models – programs which automatically analyze information and make decisions, without any need for human intervention. Predictive models have become increasingly popular, initially because they helped companies accelerate processing and reduce costs, and now because the AI is often able to make more accurate decisions than humans would.

What is Machine Learning?

Machine learning is a process that trains computers to execute tasks without explicit programming by humans. Machine learning does so by using statistical methods to process data and generate algorithms that make predictions. This training often involves ingesting large quantities of data and then iteratively processing to maximize the accuracy of the model.

Similar to the way humans learn by taking in information and recognizing patterns from which we induce general rules, machine learning enables computers to “learn” by processing data to create predictive models. This contrasts with traditional computer programing, which relies on human programmers to code the processing rules.

Examples of machine learning include analyzing images to recognize objects, processing audio recordings to translate speech into text and examining customer information to make product recommendations. The automatic suggestions we see everyday on sites such as Amazon, Google or Netflix are practical applications of machine learning.

How Do AI & ML Interact?

Artificial intelligence and machine learning are both geared towards helping computers to function autonomously, however AI generally refers to the ability of computers to perform cognitive tasks, whereas ML is a specific method for training them to do so. Thus, machine learning is often considered a subset of artificial intelligence.

To understand this distinction, compare human intelligence and human learning. Human intelligence is what broadly allows us to function intellectually, whereas human learning is the underlying method that allows us to classify, categorize, and analyze information in order to become smarter.

It’s important to note that artificial intelligence does not require machine learning – early AI systems were actuallyexplicitly programmed to perform cognitive tasks. However, it would be necessary to write huge amounts of complex code to achieve an acceptable outcome for many cognitive tasks, whereas ML can often accomplish the same result in a tiny fraction of the time by generating accurate models from large amounts of data.

The Power of AI & ML Combined

The rise of the Internet together with the exponentially increasing amount of new data that becomes available every day have greatly increased the potential of AI and ML. Many computer scientists are leveraging these innovations to design machines that can learn like humans but at a much, much larger scale.

While artificial intelligence and machine learning have historically been largely academic pursuits, we’re now seeing the emergence of many practical applications for business. For example, in the manufacturing industry, ML can be leveraged to predict the points at which equipment will require maintenance or continuously monitor processes to improve efficiency and reduce costs.

In the consumer products industry, ML is making technology more accessible and being used by businesses to create revolutionary products that help consumers in ways that haven’t been possible before. Digital personal assistants such as Siri and Alexa now exist to help people communicate verbally with their computers and smartphones. Self-driving car technology, such as Waymo, is enabling people to travel without actually driving their cars themselves.  All of these technologies are heavily reliant on ML and AI.

Final Thoughts

Artificial intelligence and machine learning are closely related to each other, however there are some subtle but important differences. Artificial intelligence is a broad term that encompasses anything related to machines carrying out cognitive functions. Machine learning is a more specific term and the most common way that machines learn these cognitive functions.

AI and ML are evolving at an accelerating rate. Over the past decade we’ve seen them begin to impact the lives of everyday people, and they will continue to do so in more impressive ways in the future with highly autonomous machines that will mimic increasingly sophisticated elements of human cognition and task execution.

As AI becomes more prevalent, businesses are will increasingly need to leverage their data through techniques like machine learning in order to stay competitive.


About DigiFi

DigiFi is a technology company that helps businesses make better automated decisions.

Our platform lets businesses easily use automated machine learning and rules management to optimize critical decisions with no coding or technical expertise required. Repetitive work that used to take hours can now be completed in minutes, letting your team focus on what matters most.

Learn more at digifi.io