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Artificial intelligence (AI) has taken the world by storm, and has disrupted many industries, such as business intelligence, fintech, science, and many more. As with any other emerging technology, there is considerable hype around AI.
In the process of using artificial intelligence as a marketing term, the difference between machine learning and deep learning has become unclear. Due to the similar nature of these terms, there is a lot of confusion surrounding their meaning. In this article, we’ll look into the definitions and uses of artificial intelligence, machine learning, and deep learning, as distinct from one another.
Table of Contents
What is Artificial Intelligence?
What is the Difference between Artificial Intelligence, Machine Learning and Deep Learning?
The Different Use Cases of Artificial Intelligence, Machine Learning and Deep Learning
Being branches of the same field, the terms artificial intelligence (AI), machine learning (ML), deep learning (DL), and natural language processing (NLP) are used interchangeably. However, they are quite distinct from one another – not only in their meaning, but also in their use cases and specific advantages and disadvantages.
The term “artificial intelligence†is the most widely used and is the broad term for a range of technologies and techniques. Machine learning, deep learning, natural language processing, neural networks, etc. can be considered subcategories of artificial intelligence.
Machine learning can even be looked upon as a specialization within artificial learning, with deep learning being a specialized skill within machine learning. Various applications combining ML and DL, such as NLP and neural networks are also categorized under AI.
What is Artificial Intelligence?
Artificial intelligence, at its most basic, is a machine which displays the characteristics exhibited by human cognition. AI is an umbrella term used to denote an artificial entity exhibiting the cognitive characteristics of intelligent humans such as learning and ‘thinking’.
According to Andrew Moore, the Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.â€
Owing to the quickly evolving nature of AI, the definition of the term has also evolved. For example, optical character recognition (OCR) was widely considered to be an AI-powered task. The task of recognizing written letters was generally thought to be something that required human intelligence. Today, OCR is barely considered under the umbrella of AI, as newer technologies have vied for the space. Currently, machine learning and deep learning occupy the spotlight of being ‘AI’, but could be replaced by the next generation of artificial intelligence.
Our interaction with AI has evolved from watching supercomputers play chess with reigning champions on television to AI being part of our everyday apps. Google Assistant, Amazon Alexa, and Apple Siri – all employ natural language processing to understand natural human speech. On the other hand, Netflix, Facebook, and Instagram employ neural networks to provide a more personalized experience to a user by recommending content.
This has given AI the reputation of being a constantly-evolving goal; one that gets farther away as the field advances. Today’s algorithms function at a relatively low cognitive level when compared to human beings, with more complex tasks still being unachievable for AI.
Another distinction to be made is that ML, DL, NLP, and all other AI technologies today are simply applications that exhibit artificial intelligence. Thus, by definition, all other subcategories fit neatly into artificial intelligence.
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What is Machine Learning?
Machine learning is a term used to denote computers ‘learning’ from large amounts of data. Machine learning is generally more accurate with large data sets, which is generally facilitated by big data. Big data refers to a large amount of user data and metadata that is collected by a company.
Deep down, this data contains a lot of valuable information about the user. Specific algorithms are written to extract this information from big data, and these algorithms are referred to as machine learning. Machine learning algorithms have to learn from these large sets of data and provide recommendations based on them.
Take the example of Netflix. From the company’s point of view, the most useful information that can be gleaned from user data is what can keep them on the platform for a longer time. Their vast array of machine learning algorithms are then given this data and are programmed to make predictions.
These predictions are indicative of what the algorithm thinks the user wants to watch next. To achieve this, data is collected from the user. This includes explicit actions, such as hitting a thumbs up or thumbs down on the content upon watching it, and implicit actions, such as clicking on the content or watching the trailer for a show or movie.
The algorithm then takes this data, along with Netflix’s existing database of content, and recommends something that the user is likely to prefer. This is then presented on the main page of the platform for the user to choose from under the label ‘Recommended for You’.
In addition to being used for recommendations, machine learning can also be used to make predictions in areas such as shipping and logistics. Considering past data from vendors, predictions can be made regarding the quantity of the shipment, thus allowing for lower waste levels while maintaining sufficient stock.
Machine learning as a field is generally focused on allowing machines to exhibit the cognitive process of learning. There are three types of machine learning. Let’s take a look at each one of them.
Supervised Learning
Supervised learning is the most basic type of machine learning. It involves training an algorithm, often referred to as a machine learning model, with a large dataset. The model looks for patterns in the dataset, which has both a ‘cause’ and an ‘effect’ variable attached to each entry.
The model then begins learning how to identify certain patterns with their respective outcomes. After training the model on the dataset once, it can then be used to improve itself or predict outcomes.
To test the model, the dataset is split into an 80/20 ratio, where a majority of the ratio is reserved for training the model. The algorithm’s predictions are then matched against the remaining 20% of the dataset to ensure accurate results.
Unsupervised Learning
Unsupervised learning was a big leap forward in machine learning, as it represented machines learning on their own without the need for labeled data.
Raw data is often unlabeled, and could not previously be read by machine learning algorithms. However, with the rise of unsupervised learning, algorithms can now learn to detect hidden patterns in data and comprehend them, themselves.
For example, a model trained on millions of pictures of kittens will begin to gain knowledge of the characteristics of what kittens look like. The hidden structure in the pixels of the picture is understood by the algorithm without the need for human labeling.
Unlike supervised learning, there is no prior training on the data. Instead, the algorithm has to derive knowledge from the data without any idea of what the data is or pertains to.
The reason unsupervised learning was an advancement over supervised learning is that machine learning was moving closer to what was considered autonomy. This also paved the way for self-improving algorithms.
Reinforcement Learning
As the name suggests, reinforcement learning is a type of machine learning wherein outputs are tweaked based on maximizing rewards. What this means is that the algorithm is built in such a way that it prioritizes the method that would net the highest amount of positive reinforcement.
In most systems, this would translate to arriving at the ‘right’ answer. The algorithm dynamically changes and improves upon itself to get the best possible solution to any given task, which includes a lot of variables.
One of the most common tasks given to reinforcement learning systems is mapping routes. Since there are many possible solutions to a simple point A to point B route on a map, the system has to find an optimal route. Hence, it will be geared towards finding a route with the least time taken and distance traveled.
This means that the system evaluates multiple options at once in order to arrive at the best solution. In the absence of a dataset, the algorithm learns from its own experiences.
Reinforcement learning is derived from the concept of positive reinforcement in human brains. Similar to this, another machine learning concept was derived from the anatomy of the human brain – deep learning.
What Is Deep Learning?
Deep learning is a subset of machine learning that is directly based on how the human brain is structured. The brain is a network of cells known as neurons, which communicate with each other to form connections and bonds with one another.
A specific series of neurons firing together or in series is how humans think. These neurons are also responsible for many of our cognitive processes and our intelligence.
Deep learning tries to replicate this architecture by simulating neurons and the layers of information present in the brain. Just as the brain is able to identify patterns and interpret perception, neural networks can label data without human supervision.
Neural networks can be used for a variety of tasks. Their main way of functioning to derive insights from data is to compare any new data with previous data that has been collected and processed in the model. This is very similar to the way the human brain processes information.
The system is autonomous and learns from itself, requiring minimal human intelligence to continue self-improvement. By clustering data together, the model constructs patterns which it then uses to identify future examples. Any examples are added to the cluster of data collected by the model.
A typical deep learning model features many ‘layers’ of information and criteria collected by the model. Any data that has to be evaluated passes through these layers, and is interpreted by the model in different ways.
Most deep learning systems function on structures known as artificial neural networks (ANN). As the name suggests, ANNs are deep learning systems with many individual nodes connected together. This is similar to the neural networks found in the human brain.
Deep learning is used for many applications in the real world, such as customer relationship management, mobile advertising, image restoration, financial fraud detection, and natural language processing.
What is Natural Language Processing?
Natural language processing (NLP) is a sector of deep learning that has recently come to the forefront. Commonly seen in mobile applications as digital assistants, NLP is a field that lies at the conjunction of machine learning and deep learning. It uses concepts from both fields with one goal – for the algorithm to understand language as it is spoken naturally.
Understanding language is a difficult task for a computer, owing to the various colloquialisms, slang, and syntax associated with grammar. This is where NLP comes in. Not only does the computer try to identify the language, but also the human voice. This is the reason NLP employs deep learning; by learning the specific nuances of a user’s voice, it is possible to increase accuracy.
NLP has many applications and is generally considered to be the face of AI to consumers. NLP is what many individuals interact with on a daily basis in the form of Google Assistant, Amazon Alexa, Apple’s Siri, and many more.
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What is the Difference between Artificial Intelligence, Machine Learning and Deep Learning?
The main difference between artificial intelligence, machine learning, and deep learning is that they are not the same, but nested inside each other, as shown in the above image. The fields of research often intersect with one another, and influence one another, with new advancements usually being placed in the deep learning category at this time.
The fundamental difference lies in a few factors. Primarily, the use of these terms and what they represent shows the progress of intelligence exhibited by machines. While it was initially referred to as artificial intelligence in a vague manner, more concrete fields, such as machine learning and deep learning began to emerge. With every iteration, machine intelligence continues to move closer toward human intelligence, slowly increasing in capability and proficiency.
In a nutshell, it could be considered that the term AI encompasses concepts in the sphere of machine learning and deep learning. Any machine that exhibits intelligence in any form can be considered artificially intelligent. Many systems that exhibit AI do not necessarily exhibit processes pertaining to machine learning, leading to the need to distinguish between the two.
While all machine learning processes are artificially intelligent in their characteristics, the reverse is not true. Only a subset of AI applications uses machine learning in order to exhibit artificial intelligence. It can be termed machine learning when AI is used to train a model to generate more accurate results from a large set of data.
Deep learning is an even more specialized form of machine learning, as it directly emulates the architecture of the human brain to learn from data. Structures such as artificial and convolutional neural networks are copies of how the brain is structured in a digital format, to replicate the patterns of neurons and the connections between them.
Deep learning and neural networks are a category of machine learning that uses this method of learning specifically. Similar to the relationship between ML and AI, all deep learning methods are machine learning, but not all ML models utilize deep learning techniques.
Therefore, the overall structure can be seen as artificial intelligence containing machine learning, which contains deep learning within it.
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The Different Use Cases of Artificial Intelligence, Machine Learning and Deep Learning
AI, ML and DL all have different applications in our everyday lives. The use cases of these technologies vary with what they are capable of. More modern technologies, mainly deep learning, has almost achieved parity with human capabilities. In addition to that, they can also process variables with an aspect of cognition, bringing them closer to human beings.
Use Cases of Artificial Intelligence
The term ‘AI-powered’ is usually used to denote that a product or a service utilizes ML or DL in some way. However, the use cases of AI, as separate from ML, are widespread today. For example, the autocorrect functionality in smartphone keyboards is considered to be artificial intelligence.
In addition to this, AI is also used in marketing to make use of real-time data. It is not physically possible to go through all the data that a given site collects in any meaningful amount of time. Instead, AI sorts through this data and provides information about the data in human-readable form. The algorithm can then be used to deliver targeted messaging depending on the user’s current data.
AI can also be used for robotics and sensor management. An artificial intelligence system can be implemented for proactive maintenance and functioning by using dynamic data from a variety of sensors. AI keeps the machines running if there is no problem and predicts when the next maintenance session is due by monitoring the data coming from the sensors.
Use Cases of Machine Learning
Machine learning is widely used in enterprise settings, with many companies observing a sizeable gain in performance metrics by implementing ML models in their business processes. Machine learning can be used to gain knowledge from data and provide personalized services for users of a service. ML solutions can also be used for prediction problems and problem statements with a large number of changing variables.
Machine learning is also widely used for a field that was previously known as business intelligence. Now termed data science, ML is used in conjunction with statistics and other approaches to gain insights from large amounts of data.
The insights gained can be used to find new markets for the company, as well as identifying pain points. Many companies today completely rely on insights from ML for executive-level decision-making regarding the company’s direction.
Other applications of ML include intelligent process automation, which is one step above existing rule-based automation algorithms. Instead of simply following a set of rules, ML-powered automation performs smart actions based on what the user has done before.
Use Cases of Deep Learning
Deep learning has seen a huge amount of adoption, especially by social media networks and Internet companies. Neural networks have the capability to provide users with exactly the kind of content they prefer, making them a natural fit for an Internet filled with feeds.
Facebook and Instagram use neural networks as a recommendation system. By comparing a user’s record of likes and dislikes against a database, neural networks can figure out what the user will like. This means that they can be recommended content which consistently elicits a reaction from them, thus increasing the amount of time spent on the platform.
Google also uses deep learning algorithms to determine how relevant a result is to a query. By comparing data on a site and the articles on the site, to relevant replies to similar queries, Google figures out the value of the content being provided. This is done through neural networks.
Any image recognition task, such as facial recognition for unlocking smartphones or Google Image Search, uses deep neural networks to match the search image and the database of images captured previously. In the case of the former, it is the primary picture captured of the user, and in the latter, it is a database of images on the Internet.
Key Takeaways
It’s important to understand the distinction between the various terms, as they are now becoming more and more commonplace, as well as ubiquitous in our tech-driven working and personal lives. One of the most easy-to-remember differences is the kind of data a model consumes.
If the model takes data where the cause and effect relationship is clearly defined, it is a machine learning model. If it takes unlabeled data and learns from itself while finding hidden patterns in the data, it is deep learning.
The fields of machine learning and deep learning are contained within AI as a whole by definition. Between machine learning and deep learning, the former contains the latter as it expands upon ML techniques. The specific terms are used for specific instances wherein certain characteristics of AI make themselves visible. While it is right to refer to both ML and DL as AI, it is wrong to use ML and DL instead of AI.
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