Your business-first guide to differentiating artificial intelligence and machine learning

Emerging technologies, such as Big Data, data science, artificial intelligence, and machine learning have captured the attention and imagination of the various industries. Unfortunately, not everyone has a full grasp of what these new systems mean or what they are capable of. That’s because they appear extremely complex to the average business pro. Without the proper technical background, these terms appear interchangeable. But the truth is far from that. Let’s differentiate between artificial intelligence and machine learning to provide a better understanding of their use in the business world.

What does artificial intelligence mean?

artificial intelligence and machine learning

Artificial intelligence is nothing more than the functions of the human brain simulated by machines. An artificial neural network is created that is capable of projecting signs of human-like intelligence. AI is primarily concerned with performing human functions such as learning, logical reasoning, and self-correction.

AI is a vast and evolving field with numerous vital applications, and though much has been said and written about AI in business, it is more complicated than it appears at first glance. The truth is, machines do not possess any smarts on their own; they require plenty of data and computing capabilities to simulate the process of human thought.

Artificial intelligence may be divided into two distinct categories — general AI and narrow AI. While general AI pertains to making technology more intelligent for use in different thinking- and reasoning-related activities, narrow AI employs artificial intelligence for specific types of tasks.

Still confused? Well, think of it this way — general AI refers to an algorithm that is programmed to play various types of board games while narrow AI’s machine abilities are limited to a certain kind of game, like Scrabble or chess.

At the moment, researchers and developers in the field are in the process of perfecting narrow AI technology. There is still a long road ahead for general AI to be available to the public.

Understanding the concept of machine learning

Machine learning may be defined as the capacity of a computer system to gather knowledge from the environment and process the experience to improve future interactions. No explicit programming is necessary for ML.

This technology is more concerned with allowing algorithms to glean information from the given data, collect insights, and provide predictions on earlier unanalyzed data using the available information.

There are various approaches available for researchers and developers to harness the power of machine learning. There are three key ML models available, including reinforcement, supervised, and unsupervised learning.

As far as reinforcement machine learning is concerned, algorithms will interact with the surrounding environment. They will produce actions and further analyze rewards or errors. Continuing the board game analogy, a game of chess will not be analyzed by the ML algorithm according to the individual moves; rather it will try to understand and study the game as a whole.

Supervised learning, on the other hand, uses labeled data to help systems recognize traits and characteristics, and employ the same for the purpose of future data. For example, while classifying images of dogs and cats, certain labeled pictures can be fed to the algorithm, which will then classify the remaining images on your behalf.

When it comes to unsupervised learning, introducing unlabeled data is enough for the machine to pick on the defining characteristics and classify it accordingly.

So how do businesses differentiate between artificial intelligence and machine learning?

Machine Learning and Artificial Intelligence
Let’s look at some key differences between artificial intelligence and machine learning:

Structure: First things first, companies need to stop using the terms artificial intelligence and machine learning interchangeably. Artificial intelligence is a broader term that encompasses a host of applications, ranging from text analysis to robotics. It is still undergoing changes and arguments have cropped up regarding the decision to pursue high-level AI.

Machine learning is more of a subset of AI, concentrated on a select few tasks. In fact, it would not be wrong to classify ML as the actual artificial intelligence with many applications rooted in real-world issues.

Approach: The fundamental differences between artificial intelligence and machine learning have prompted developers and researchers to adopt two separate approaches. While ML attempts to locate a pattern that can be followed and met with success, there is no way to execute local decisions.

In the case of artificial intelligence, overall local decisions may be made successfully. In nature, many similarities to this kind of artificial intelligence approach towards problem-solving may be found.

Usage: Machine learning is a sub-field of AI (as already indicated). What this means is, machines collect data and then use the same to acquire knowledge for themselves. This makes AI one of the most promising tools for the business industry.

Machine learning technologies allow systems to apply training and knowledge quickly from big data sets for the purpose of acing tasks, such as object recognition, speech recognition, translation, facial recognition, and others.

While software programs must be hand-coded with specific sets of instructions for the purpose of completing one task, machine learning enables a system to learn the skill of recognizing patterns by itself and making proper predictions.

Connections: It slowly becomes obvious that though artificial intelligence and machine learning are two separate technologies, they share a lot of things in common. This is because machine learning is one of the methods of achieving artificial intelligence.

Application: Contemporary businesses are gradually increasing the usage of machine learning and AI to enhance the customer experience and acquire intelligent inputs from a wide section of customers.

AI is finding use in a wide array of everyday activities, and it is helping create products from self-driving cars to the Google supercomputer, AlphaGo. On the other hand, various companies are finding innovative uses for ML, like Yelp that uses machine learning algorithms to help employees compile, organize, and label pictures with greater efficiency.

Now you know the differences

The term “machine learning” is commonly used in the business world as a synonym for “artificial intelligence.” However, the truth is, it’s a lot different, albeit connected discipline. You will find several points above that help make the distinction between artificial intelligence and machine learning clearer and allow you to understand which one is better suited for your organization.

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