In the world of Artificial Intelligence (AI), the biggest challenge is to understand the relationship between the different concepts and building models that can predict the future behaviour of complex systems. This article is part of a series that looks at the emerging trends in AI.
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are all part of the same category of “machine learning” technologies. While there’s some debate about whether “deep learning” should be a separate class of technology from AI, ML and DL, there’s a general consensus that they’re all related, albeit with different levels of complexity. Let’s define these terms and explain what they do.
AI has been an emerging technology that is reshaping and revolutionizing every industry today. At the time of writing, AI was amidst a major boom and had achieved unprecedented levels of popularity, ubiquity, and commercial application. To define AI, it is a program that allows machine to sense, reason, act and adapt. In simple terms, AI means getting a computer to mimic human behaviour.
As this article is not an exhaustive study of AI, check out the basics of AI and how it helps businesses right here: Is Investment in AI for Businesses Worthy?
I can’t really think of a simpler way to describe machine learning than to say it is the ability of a computer to learn. In other words, a ML system will take a set of input data and output an answer based on the given data that it determines to be correct. It does so through algorithms whose performance improve as they are exposed to more data over time.
A further illustration will be that ML is the study of algorithms and statistical models to teach computers how to learn from huge amounts of data and make predictions.
As for its algorithms, they can be very powerful. It can be trained to recognize patterns in data that humans cannot, and they can make future predictions based on what they already know about the past. Various industries have already implemented in their operations, including business analytics, marketing automation, customer service, fraud prevention, and security.
The main idea behind deep learning is the human brain, the neural network. Similarly to ML, DL is a more advanced form of ML where the computer learns to think using structures modeled after how the human’s brain works.
How this is done is through a layered structure of algorithms instead of a basic one. DL analyses a data in more ways than one, coming up with multiple conclusions on its own. This is unlike ML which is only able to analyse within the structured set of data it was taught to.
Due to this, DL do not require much human intervention at all due to it’s self reliability and learning capabilities. For it does not need to be retrained to draw new outcomes. ML algorithms, on the other hand, are only able to learn through pre-programmed defined criteria. So, if you want a new outcome for ML to draw out, you will have to retrain it!
To sum up this blog, AI, ML, and DL are all technologies that use computer algorithms to understand the meaning of data. They are often used together in the context of Artificial Intelligence.
They are similar in that they all rely on the same basic techniques such as Neural Networks, Reinforcement Learning, etc. But there are many other distinctions among these various technologies. Hence, you need to understand the differences and relationships to make impactful decisions when selecting the right technology for your needs.