What’s the Difference between Machine Learning and Artificial Intelligence? | Software Advisory Service

What’s the Difference between Machine Learning and Artificial Intelligence?

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What’s the Difference between Machine Learning and Artificial Intelligence?


Let 2019 be the year where you understand the basics of artificial intelligence and machine learning.

Artificial intelligence and machine learning are two of the hottest things in the world of technology. Although they are two very different concepts, we see that plenty of people scramble the definitions and often use them interchangeably. In 2019, it’s essential to understand the basics of both machine learning (ML) and artificial intelligence (AI) because each one plays an integral role in computer science and – more importantly – in technology today.

The best way to fully grasp the value and purpose of both concepts is to determine their qualities, as well as their similarities and differences. Let’s take a closer look at the two concepts.


Understanding Artificial Intelligence


AI was invented by John McCarthy in 1956. However, years before, there were already concepts like mechanical brains and logical machines, which were favoured and used by engineers working with early European computers.

As technology is always improving and evolving, these concepts also started developing. And simultaneously as technology began to better understand the human mind and how it works, artificial intelligence started to change with it. But what is artificial intelligence really? What does it do? And, more importantly, what are its uses?

Put in the simplest of terms, artificial intelligence is a technology that allows a system to perform tasks in a nearly human-like way. AI pertains to machines that mimic human characteristics. The most common tasks performed in AI today, are recognition of voices and images, solving problems, learning, planning, and understanding different languages.

The goal of artificial intelligence is to increase and improve the chances of success. Likewise, it’s known for its decision-making capabilities, and it’s concerned with solving complicated problems through the help of simulated natural intelligence.

Apart from understanding what it does, it’s also important to know the two types of AI.

In short, general artificial intelligence is the less common type. This type can take care of, or handle, any kind of intellectual task that a human can. It’s capable of both adding new knowledge and developing capabilities. As such, the task it does may not even really exist yet.

This is, however, the area of artificial intelligence where most of the developments and advancements are currently taking place.


General AI is identified as one of those forces responsible for the further development of machine learning.


On the other hand, you have weak artificial intelligence – which is the one most people will be familiar with. This type focuses on one specific and pre-defined task. As such, it works with predicted responses. A good example of this is LinkedIn Messaging. Whenever you receive a message on the platform, the application has prepared a selection of answers for you. These are common answers to common messages. Essentially, the bases for these responses are usually how certain phrases and words are arranged.

Weak AI, also called applied AI, does not possess consciousness. This means that while it may be excellent in one area or capacity, it lacks value in other areas. Good examples of weak AI include Apple’s Siri, Netflix recommendations, and Spotify’s discovery mode.



Understanding Machine Learning


Artificial intelligence and computer gaming pioneer Arthur Samuel was the first to coin the term machine learning. This happened as early as in 1959, when the American described ML as the study that allows computers to learn without the need to be programmed explicitly.

In much simpler terms, ML is an application – an AI application – that gives machines the capability to improve and learn from experience.

It works on the concept that machines should have data access and thereby should be able to use these data to learn on their own. So, unlike artificial intelligence which works like a computer program and performs smart work, machine learning learns from the data it uses and takes. Instead of focusing on decision-making, such as in AI, machine learning is focused on learning new things from existing data. While AI has intelligence as its end goal, ML is concerned with gaining knowledge.

A good example of machine learning is how computers can recognise images through sheer ability. Even if there are different images—say that of food, clothes, and gadgets—ML is still able to distinguish one from the other. It does this with the help of machine learning algorithms, which have the goal of studying and scrutinising the images according to their tags.

Using these tags, the algorithm is then able to create a model for each image type. This is what helps classify the different tags. This means that tags for food will show only food images, those for clothes will show only clothes, and so on. This is a perfect illustration of how machines learn to identify specific images.


The bottom line


To help you better understand the differences between artificial intelligence and machine learning, there is only one thing you have to remember: AI is a broader concept than ML—and it embraces a general concept—one that says machines can perform smart tasks while ML is all about acquiring knowledge and learning via specific algorithms.

So while these two clearly have differences, both are essential factors that help shape technology—modern and advanced technology. Both are responsible for giving us things we never thought were possible and for providing us with technology we can all benefit from.

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