The Difference Between AI, ML and DL
Artificial Intelligence, Machine Learning, and Deep Learning
In today’s ever-evolving world, new technologies designed to make our lives easier are constantly being introduced. One of these critical pieces of technology is AI or artificial intelligence. You may be familiar with AI’s most common form, digital assistants, which you can find on your phone (ex., Siri) and in your home (ex., Amazon Alexa).
But what is artificial intelligence? How does artificial intelligence relate to machine learning and deep learning? Are they the same? Are the terms used interchangeably or unrelated?
Artificial Intelligence is often used as a catch-all term for machine learning and deep learning. However, there are many differences between these types of AI, so it’s essential to learn what each term represents and the differences/relationships they share.
Let’s start by looking at some basic definitions of these terms:
Artificial Intelligence (AI): Developing machines to mimic human intelligence and behaviour.
Machine Learning (ML): Algorithms that learn from structured data to predict outputs and discover patterns in that data.
Deep Learning (DL): Algorithms based on highly complex neural networks that mimic the way a human brain works to detect patterns in large unstructured data sets.
This article breaks down the differences and relationships between artificial intelligence, machine learning and deep learning. We’ll also discuss examples regarding which type of situations to use ML or DL and the benefits of using one over the other.
The Relationship Between AI, ML and DL
To begin, we’re going to start with the relationship between Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). Since these three elements are interconnected (meaning you can’t have one without the other), exploring their relationship is vital before we break down each factor individually.
Machine Learning is a sub-category of AI, and Deep Learning is a sub-category of ML, meaning they are both forms of AI.
Artificial intelligence is the broad idea that machines can intelligently execute tasks by mimicking human behaviours and thought processes.
Machine learning, a subset of AI, revolves around the idea that machines can learn and adapt through experiences and data to complete specific tasks (Source: sas). An example would be predicting the weather forecast for the next seven days based on data from the previous year and the previous week. Every day, the data from the previous year/week changes, so the ML model must adapt to the new data.
Deep learning is a subset of ML. DL models are based on highly complex neural networks that mimic how the brain works. With many layers of processing units, deep learning takes it a step further to learn complex patterns in large amounts of data. For example, deep learning (combined with computer vision) in a driverless car can identify a person crossing the road.
Now that we know more about the relationship between AI, ML and DL, let’s learn about each of these elements individually.
What is Artificial Intelligence?
Artificial intelligence or AI recreates human intelligence and behaviour using algorithms, data, and models. AI predicts, automates, and completes tasks typically done by humans with greater accuracy and precision, reduced bias, cost, and timesaving.
When is Artificial Intelligence Used?
Artificial intelligence is used when a machine completes a task using human intellect and behaviours. For example, Roomba, the smart robotic vacuum, uses AI to analyze the size of the room, obstacles, and pathways. Just like a human being taking this information into account, Roomba then retains this information and creates the most efficient route for vacuuming (Source: BuiltIn).
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Now that we understand more about artificial intelligence, let’s look at machine learning and deep learning.
What is Machine Learning?
Machine learning, or ML, is a type of AI that uses algorithms to learn from data to make sense of it or predict a pattern. Machine learning uses methods from neural networks, statistics, operations research, and physics to find hidden insights within data without being programmed where to look or what to conclude (Source: sas). For example, machine learning is used to develop self-learning processing where software is given instructions on accomplishing a specific task. The machine is then trained and learns how to perform the job by analyzing relevant data and algorithms, allowing it to understand how to accomplish the task and then evolve its performance.
Regularization and Machine Learning
Regularization is a form of regression in which the coefficient estimates are constrained or shrunk toward zero (Source: Towards Data Science). It helps the machine avoid the risk of overfitting, which can cause low accuracy in predictions. If the machine is overfitting, the model exerts too much effort in understanding the extra noise (or irrelevant data points) in the data set. Machine learning models often work with large data groups; therefore, regularization is used to help eliminate noise in the dataset and produce more accurate responses/answers.
When to Use Machine Learning
Use machine learning when you’re looking to teach a model how to perform a task, such as predicting an output or discovering a pattern using structured data (see Structured vs. Unstructured Data for definitions). For example, Spotify builds you a customized playlist based on your favourite songs and the data from other users who share your likes and dislikes.
Structured vs. Unstructured Data
Structured data (quantitative data) is organized data that is decipherable by ML algorithms, easily used by businesses and accessible by more tools than unstructured data. This type of data has a predefined format, which limits its flexibility and use cases (Source: Scion Analytics). Examples of structured data include dates, phone numbers, customer names, and product names.
Unstructured data (qualitative data) is typically easy and inexpensive to store and can be used across different formats as it does not have a defined purpose (Source: Scion Analytics). However, since this type of data is available in other forms, it isn’t easy to analyze and leverage. DL is commonly used for unstructured data and is the best option for the most challenging use cases. Examples of unstructured data include photos, audio, and video files.
The Benefits of Machine Learning
Companies gain significant and precise insights when integrating machine learning with their data analytics to forecast factors such as market trends and consumer buying habits. This helps companies save on costs and better manage their inventory. ML can also indicate other items, such as transportation costs, future demand, and delivery lead times. Machine learning is used in this scenario over deep learning as ML models are better equipped to handle structured data, which is used in forecasting, and are better at predicting trends.
Using machine learning, businesses can reduce the time spent analyzing complicated data sets. The results and tasks accomplished by machine learning models are often very reliable and well done. This is because the model can learn from itself by making its predictions and improving its algorithms, meaning that no human intervention is needed. Meanwhile, a deep learning model requires human intervention during its early stages as someone needs to review its results since it works with unstructured data.
Trend and Pattern Recognition
Machine learning models are designed to handle large sets of structured data and analyze them to discover patterns and trends humans wouldn’t identify. A deep learning model is not recommended as it’s not designed to recognize trends and patterns within structured data.
Machine Learning Applications
Chatbots are conversational artificial intelligence systems trained using machine learning to provide the appropriate response or assistance based on inputs. These systems learn from past experiences, such as questions asked by previous visitors/responses given and from datasets containing possible future queries/appropriate answers. While deep learning plays a role in chatbots, this specific feature of providing proper responses to questions is unique to machine learning since it requires structured data analysis.
Educational tools, such as apps that teach you different languages, also use machine learning. By analyzing the data you provided from completing sections of the course, ML uses that knowledge to adjust the educational system to meet your needs. Deep learning does not apply to this function, as educational apps primarily use structured data.
Recommendations on streaming platforms are another form of machine learning. The ML model integrated into these platforms analyzes songs, movies or shows you have engaged with in the past, compares it with other data from customers with similar consumer behaviours and then suggests additional content you may enjoy. Once again, this function uses structured data instead of unstructured data, so deep learning models cannot be applied.
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Now that we’ve grasped a basic understanding of machine learning let’s explore further and learn about deep learning.
What is Deep Learning?
Deep learning is the evolution of machine learning and neural networks, which uses advanced computer programming and training to understand complex patterns hidden in large data sets (Source: sas). DL is about understanding how the human brain works in different situations and then trying to recreate its behaviour (Source: IBM). Deep learning is used for complicated problems such as facial recognition, defect detection and image processing.
When to Use Deep Learning?
Deep learning is used to complete complex tasks and train models using unstructured data (see Structured vs. Unstructured Data for definitions). For example, deep learning is commonly used in image classification tasks like facial recognition. Although machine learning models can also identify faces, deep learning models are more accurate. In this case, it takes the unstructured data (images of faces) and extracts factors such as the various facial features. The extracted features are then matched to those stored in a database.
The Benefits of Deep Learning
Efficiently Handles Unstructured Data
While machine learning models can handle various types of data, they are limited when understanding unstructured data (such as handwriting, images and voices). This means that the knowledge hidden in this data may go unnoticed, and it is where deep learning fills the gap. When businesses train their deep learning models, they must do so with unstructured data, as it can help the company optimize many of their business-related functions.
Deep learning’s ability to process massive amounts of data simultaneously and perform analyses quickly makes this approach highly scalable. A company can improve its productivity, modularity, and portability by using deep learning. For example, Google’s Cloud AI platform can run deep neural networks at scale on their cloud, leveraging their infrastructure to scale batch prediction, improving efficiency by scaling the number of nodes based on traffic requests (Source: Width.ai).
Parallel and Distributed Algorithms
Since deep learning models are better at supporting parallel and distributed algorithms, the amount of time it would take for a DL model to learn the relevant parameters are significantly reduced. The models can be trained locally (only using one machine); however, working with massive data sets becomes challenging. Parallel and distributed algorithms allow the data (or model) to be distributed across multiple machines, making the training more effective (Source: Width.ai). Also, parallel and distributed algorithms speed up the time the model needs to learn and train, saving the company time and money.
Deep Learning Applications
Deep learning is used in virtual assistants such as Alexa and Siri, which use Natural Language Processing (NLP). NLP analyzes and understands unstructured data, such as forms of human language (written and verbal). It also analyzes factors such as language recognition, sentiment analysis and text classification and then creates the appropriate response to your input. When using NLP, it’s recommended to use deep learning as it better understands unstructured data, such as written and verbal language, which helps in scenarios of recognizing sentiment analysis.
Self-driving cars are autonomous decision-making systems that process data from multiple sensors such as cameras, LiDAR, RADAR and GPS. The data collected is then analyzed using deep learning algorithms to produce relevant decisions depending on the car’s environment. Deep learning plays a role in a self-driving car’s perception as it helps the car recognize and classify objects, buildings, beings, road signs, traffic lights, etc., picked up by its sensors and cameras. DL is also used to improve the visual odometry of the car, which helps the car calculate its position and orientation while navigating (Source: Neptune.ai).
Deep learning is also used in manufacturing to improve quality. A significant expense the manufacturing industry faces is equipment and machinery maintenance. Deep learning models decrease the time a piece is out of commission as it helps identify quality problems using process monitoring and anomaly detection. This saves the company money from unscheduled repairs, helps them better design their equipment, improves employee safety and product quality, and increases productivity. Only deep learning can be used for this function, as ML models are limited in handling the unstructured data involved in process monitoring and anomaly detection.
A Summary of Artificial Intelligence, Machine Learning, and Deep Learning
Artificial Intelligence recreates human intelligence and behaviours using algorithms, data, and models. AI is implemented when using a machine to complete a task using human behaviours.
Machine Learning or ML, a subset of AI, uses algorithms to learn from data and makes sense of the data or predict patterns. ML is used when you’re looking to teach a model how to predict an output or discover a trend using structured data.
Deep Learning, or DL, a subset of ML, is the evolution of machine learning and neural networks, which uses advanced computer programming and training to understand complex patterns hidden in large data sets, similar to a human brain (Source: sas).
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