Artificial Intelligence vs Machine Learning

Introduction

The term “Artificial Intelligence (AI)” entered common usage in 1956. There was a time when people became disillusioned with AI and companies even started to claim they did not use AI to avoid negative connotation. And then Machine Learning (ML) rose in popularity, which like AI was not well-defined and in fact had a similar definition to AI.

You may wonder: how does Artificial Intelligence and Machine Learning compare? Although making a distinction between these two subject matters can be hard, there are key differences to note. In this article we will discuss marked differences between AI and ML.

What is Artificial Intelligence?

The end goal of AI technology is an intelligent machine that can “think” for itself — referred to as “Strong AI” or “Artificial General Intelligence (NGI)”. An example of Strong AI would be the character Data from the TV show Star Trek. He’s often seen as a humanoid, and explores his own humanity throughout the series.

AI programs currently being built are considered to belong to the “Narrow/Weak AI” category. An example of Weak AI is an AI that is trained to do just one specific task, such as Deep Blue or AlphaGo. The latter learned to play and beat others on Go, a complicated board game considered more complex than chess due to the amount of possible board configurations. During AlphaGo’s game against a reigning champion, the AI came up with several innovative moves of its own.

Behind the scenes, AI can use algorithms, such as reinforcement learning, to make “decisions” on its own or even a group of if-then statements. These are known as Good, Old-Fashioned AI (GOFAI).

AI is a wide-encompassing, interdisciplinary science with the end goal of building a smart machine that can accomplish tasks usually designated to humans. There are debates on what is meant by “smart” or “thinking” machines, but the overall idea is that of creating a machine that closely imitates a human.

What is Machine Learning?

Machine Learning involves algorithms to help sort or rank information. ML programs can learn to work with certain data. Oftentimes, ML will use historical data to inform its decision making. An algorithm is just static — it does its job, but ML is when given a set of algorithms and data, and it can alter itself and train to make progressively better decisions. 

Machine learning can be categorised into three types: supervised and unsupervised learning. In a supervised learning model, the algorithm, or the “machine”, is given a labeled dataset and can evaluate its accuracy level with an answer key. 

In an unsupervised model, data fed to the model is unlabeled and the algorithm tries to make sense of it by extrapolating patterns and features. Reinforcement learning is when finding a more streamlined or effective way to accomplish a goal is rewarded. This reward system makes the algorithm strive for the most points, hence increasing optimization. 

A subset of ML, and by extension of AI, is deep learning (DL), usually referred to as deep artificial neural networks. Deep learning algorithms are inspired by our own processing patterns. DL strives to learn to accurately label items and assign them to the appropriate categories by comparing them to items in the various categories.

Some ML application examples are: recommendation systems for shopping sites, events sites, and Google’s search algorithm.

AI vs. Machine Learning

While both AI and ML can include “learning” and a certain level of self-correction, AI would have an added layer of reasoning which ML would not have. This means that AI can handle even unstructured data, whereas an ML program must be fed structured data as well as clear approach instructions.

We’re going to compare AI based on three factors: approach, the processes used in each field, and use cases of both AI and ML.

Approach

The main distinction is that AI is meant to aim for imitating a human as closely as possible — at least in regards to the “thinking” process. Machine learning, on the other hand, is more focused on learning without continued instruction.

ML can be seen as a subset of AI. While AI seeks to create an intelligent machine that can for example, fool the Turing test. ML is a means that can be used to train such an intelligent machine. ML’s main aim is to get an output with a high level of accuracy, while AI’s main goal is to learn and come up with creative solutions. 

Processes

Data preparation and cleaning is a crucial first step in ML and AI processes. 

Machine Learning can be divided into two main  categories: supervised and unsupervised. In supervised learning, regression and classification are used. In unsupervised learning, clustering and artificial neural networks are used. ML algorithms must be trained on data. 

The next step after data cleaning for an AI project would be modeling. During this process, data is used as input for the model to learn from, not just solve a particular problem based on historical data and some level of instruction. For this to work, engineers decide which implementation is best — such as deep learning and machine learning models. The next step after this would be simulation and testing.

For example, training a self-driving car requires that a model learns to accurately detect objects and work with other systems, which help it plan a path and know the location. This testing phase should ensure that the model will work cohesively with other systems.

Use Cases 

Machine learning is a subset of AI, and AI is a bit more complex. Voice assistants such as Siri or Bixby are examples of AI applications. The intent to mimic a human process can be seen by the assignment of a human name and the mimicking of regional accents. These technologies are complex so they are meant to handle a myriad of questions worded in many types of ways.

Meanwhile, machine learning applications are trained for much more specific, finite possibilities. Movie recommendation algorithms on streaming sites are an example of a machine learning application. ML can be used for computations, in pattern recognition, and anomaly detection.

Conclusion

While AI is a technology meant to reason and adapt to different situations, ML generally learns over time and with the more data it’s exposed to. ML is more specific in its scope.

Generally, Machine Learning is a field that requires extensive math and statistical knowledge with programming skills and knowing how to work with data. If you are interested in advanced computer vision or Natural Language Processing (NLP) applications, you can dive into AI with some programming experience. You don’t have to know ML to go into AI. ML is a subset of AI, which means any ML can be considered AI but not all AI is ML.

It can be hard to spot the differences between AI and ML, but something to look at is a project’s purpose. Knowing what the goal of the program is intended to be and comparing it with what is defined as ML or AI can help you differentiate which discipline a project belongs to.