AI Basics: Understanding Deep Learning and Machine Learning (ML)

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There are new buzzwords on the net – AI, deep learning, and machine learning. Although sometimes they’re used interchangeably, deep learning and ML are just forms of AI. Today we’re going to specifically look at both of them, as we cover AI basics.

As a business owner, you’ve probably heard of these terms before but it sort of feels like a foreign language, unless you have a base understanding of the concepts.

ML technology enables engineers to create programs or systems that can learn on their own over time with minimal training or input data, to begin with. Some examples of this in action are Google searches suggest, video recommendations from Netflix, Amazon, YouTube, and the content on your Facebook feed.

The use of unsupervised machine learning is growing, meaning that the machine has to identify patterns on its own and improve itself. Some examples of this today include Siri, fraud detection on credit cards, computer gameplay, customer service chatbots, etc.

Deep learning and machine learning are very similar but unlike ML, deep learning requires large sets of training data. This allows it to be very accurate in the information that it returns but increases the training time needed.

Difference between Machine learning and deep learning AI — Source: MATLAB

What is Artificial Intelligence (AI)?

AI is the capability of machines to imitate human behavior and their capacity to handle tasks such as understanding human speeches or decision-making using a set of principles. In no specific order, let’s take a look at them.

Reasoning and Problem-solving

Researchers develop algorithms and a step-by-step guide on how a human being would solve particular problems. These methods are then fed into the system to enable it to solve the same kinds of situations. AI also has the ability to solve uncertain issues by employing probability and economics in its reasoning.

Knowledge Base

This is a central Issue in Artificial Intelligence research. Most problems that must be solved by machines require a lot of knowledge or data input (aka training data). This may include objects, categories, pictures, relationships between different things, concepts, etc.

Planning

Intelligence systems must be able to come up with goals and ways of achieving them. The system needs a way of visualizing the future, similar to a human, and come up with predictions about how different actions may change it.

Learning

Machine learning is the application of AI that can improve through the experience without being explicitly programmed. Unsupervised learning finds patterns in a long stream of input, while supervised learning focuses on using the dataset that it’s provided with.

Natural Language Processing

This enables machines to read and understand different human languages. A natural language system allows interface and acquisition of knowledge from human sources. Common challenges experienced in this area are speech recognition, natural-language understanding and generation.

Perception

Machine perception is the use of input from sensors such as microphones, cameras, sonar, tactile sensors, etc. Machine perception allows computers to interpret data in the same way a human being would. This has been made possible with the advancement in software technology where computers allow sensory input similar to humans.

Motion and Manipulation

This is the field of robotics that’s very close to AI. For robots to handle tasks such as objects manipulation and navigation, they require intelligence. The machines must recognize their surroundings, learn from the environment, be able to move from one point to another, and execute different actions.

Robotics deals with construction, design, operation, and use of robots for information processing plus sensory feedback.

AI develops machines that can substitute human beings and replicate their actions. Robots can be used in many different situations and purposes, but many are used in environments where human beings cannot survive like in manufacturing processes or very dangerous areas. Think of AI as the brain that we can implant into a robot.

What Does This All Mean For SEO?

I recently shared a post about why we need to rethink keyword research a little, where I also proposed a new framework for the process. But it all comes down to the user intent. Search engines are better and faster at understanding queries thanks to AI. With the implementation of ML by Google, their algorithms now think closer to how a human would.

Essentially, our content and searcher queries can now be understood at a deeper level, which means SEOs need to do a better job at serving searcher intent. Why?

Well, for one thing, there will be even more complex voice searches in the future and if AI can understand your content just like a human would, then there’s no real room for thin or low-quality content on the web.

But does this change SEO in any other way? Not really and here’s why…

I know that there’re a few people that think link building is going to be dead but in my opinion, that’s not going to happen anytime soon. And the reason is simply that even though search engines can understand web page content a whole lot better, they still need a final deciding factor.

They certainly can’t use content because there’s just too much out there today. They can’t completely rely on social signals because bots run rampant on social platforms, artificially manipulating likes, retweets, followers, etc. So they’re left with backlinks because not only is it the hardest thing to do in SEO, it’s also the surest way of knowing how good a web page really is.