What is AI ML?

Marketers everywhere boast the use of AI ML in their systems and processes – but what exactly does that mean? This guide will help you understand artificial intelligence and machine learning, so you will know exactly what to look for when analyzing systems.

The Difference Between Artificial Intelligence & Machine Learning

Although the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably, they are not the same thing. This can lead to a lot of misleading advertising, and studies have found that almost 40% of firms that claim that they use AI or ML do not even use the technology.

So, what is the difference between the two?

According to Carnegie Mellon University, artificial intelligence is defined as the science and engineering that allows us to make computers behave in human-like ways. In other words, it is what allows computer systems to learn, make predictions, and interpret natural language.

AI technology is what allows us to enjoy tools like Amazon Alexa and Siri – they can respond and to you and answer questions as if they were human!

Machine learning, on the other hand, is a subset of artificial intelligence. This narrow field is the study of computer algorithms that can automatically improve through learning and experience. These algorithms follow a set of instructions and parameters, then analyze large amounts of data to recognize underlying patterns.

For example, you can use a machine-learning algorithm to analyze customer behavior. Once it learns from the data you provide, it can connect previously unseen relationships.

What does AI ML Mean?

If you have ever heard the term AI ML, you may be wondering what it means. The acronym simply means artificial intelligence machine learning. However, as we mentioned, these are two separate technologies and should be addressed as such.

Machine learning is a narrow subset of AI. It is the most effective way to build an AI model that can do everything from categorizing data to making predictions and recognizing images.

AI is much broader, though, and more difficult to describe as a result. As technology improves and adapts, so does the definition of artificial intelligence. This is different from machine learning, which is a definable science.

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Defining Artificial Intelligence

Artificial intelligence is a technology often associated with science-fiction and the distant future. However, these tend to represent AI programs that can exceed human performance in many ways and are not like anything we have available today.

Current artificial intelligence focuses on one or two tasks, such as giving you recommendations for movies you might like, identifying your photos on social media, or even protecting your company from fraud. In other words, it can do some of these things but not all of them!

However, AI platforms can perform these tasks much quicker and more accurately than a human could. It may take a team of individuals to comb through transactions and flag suspicious activity, but an AI program can do it in the blink of an eye.

Defining Machine Learning

Let’s dive into machine learning a bit deeper.

As we mentioned, machine learning is an easy way to develop a narrow AI that serves a specific purpose. These algorithms work by analyzing a pre-labeled set of data. The computer program learns through trial and error and eventually figures out how to make accurate predictions and connections.

How exactly does it work?

The key is giving the software the answer to what you are looking for. If you want an ML algorithm that can predict identify various types of animals, then you need to teach the model what they are! For example, you can show it a picture of a dog and label it as such.

Over time, it will learn how to identify dogs on its own without needing the label. The more data that you provide, the faster and more accurate the algorithm will be. This narrow type of AI will only be able to perform that task, but it can do it well.

Early Days of AI and Machine Learning

Artificial intelligence emerged back in 1956, when Herbert Simon, Allen Newell, and other researchers created a computer program that could “think.” Their program was able to copy the problem-solving skills that a person would use, and it is generally recognized as the first AI program!

From that point through the 1970s, this technology advanced rapidly. Computer scientists were able to improve their understanding of AI to develop better algorithms and systems. Eventually, these programs could even understand spoken language.

The Rise of Machine Learning

By 2012, terms like machine learning, big data, and predictive analytics gained traction and popularity. Companies ran with this trend and made impressive strides with deep learning neural networks and other types of complex AI.

Deep learning has capabilities that far exceed what traditional rules-based programming can do. It uses neural networks that work like our brains to make connections that aren’t overly obvious.

Today, machine learning has become a tool that businesses everywhere rely on to understand their customers and personalize their services. It allows our favorite tv subscriptions like Hulu and Netflix to give us recommendations on what to watch next. Machine learning and AI power tools like Google Home and Siri are essential to our daily lives and allow us to be more productive!
Key Differences Between AI and Machine Learning

Now that you understand AI and machine learning, let’s recap the key differences between the two.

AI is a broad term that includes technology that can think or behave as the human brain does. Advancements in this field make this definition a moving target since they continue to develop and change rapidly.

Things that may seem innovative and advanced now could be considered the minimum standard in a few years!

Machine learning is a more narrowed field that creates a specific AI. ML algorithms have a particular goal that they are working towards, whether it is categorizing data or helping your business predict when customers will churn.

Using the term AI ML is ineffective because machine learning is a type of artificial intelligence – but they are not the same thing!

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