AI vs. Deep Learning vs. Machine Learning: Everything You’ve Ever Wanted to Know
If you’ve spent any time reading articles about tech in the last few years, you’ve likely bumped into some new terms such as artificial intelligence or AI, machine learning or ML, and deep learning. These trending tech terms are often used without explanation of what makes them different. And, to be clear, they’re not interchangeable.
As technology becomes more and more embedded in our daily lives, it’s important to understand some of the nuances that differentiate these different technologies and how they’re being used. When smart tech is on our phones and in our homes as Siri and Alexa, it’s important to know just what they are.
In this article, we’ll cover the differences between AI, ML, and deep learning. As well, we’ll go over some best practices for building these types of technology and what to look out for if you’re planning to implement them at your company.
Artificial Intelligence Vs. Machine Learning Vs. Deep Learning
The best way to kick off the conversation around the differences between AI, ML, and deep learning is to think of them as a gradient, each is a component of the prior term. Artificial intelligence or AI is the umbrella term that encompasses machine learning, while deep learning is a type of machine learning.
Now that you have a basic understanding of how they work together, let’s dive into the specifics of each.
Artificial intelligence is the umbrella term that encompasses a number of other technologies. AI, in the most simple terms, is a machine that can mimic or embody the characteristics of human intelligence. AI has been a theory and part of storytelling in movies and science fiction novels for decades.
AI has also now become a reality. AI is now being used by businesses across industries to automate, predict, and optimize tasks that have been historically done by humans. This is saving business money and time, while also making for happier employees who no longer must complete tedious, repetitive tasks.
There are three main categories or types of AI:
Artificial Narrow Intelligence (ANI)
Artificial General Intelligence (AGI)
Artificial Super Intelligence (ASI)
The first form of AI, Artificial Narrow Intelligence, is often referred to as “weak” AI, whereas the other two are referred to as “strong” AI. Weak AI or ANI is different from the other two types of AI in that it can only complete a very specific task. AGI and ASI, or strong AI, can complete multiple tasks.
Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence or ANI is what’s known as weak AI, meaning it can only complete one task. While weak AI is good at completing one type of task, it won’t pass for human in any other area or task.
An example of weak AI is Deep Blue, the computer that beat chess champion Garry Kasparov, in 1996. While Deep Blue could evaluate 200 million chess positions per second, that’s all it could do, making it weak AI.
ANI is used widely in science, business, and healthcare to create AI solutions that are good at one specific task. These types of weak AI programs are used to read medical images, detect manufacturing abnormalities, and find patterns in business data.
Artificial General Intelligence (AGI)
Artificial General Intelligence or AGI is one of two types of strong AI. AGI is able to do multiple tasks, crossing the line where machines become more human-like in their abilities. AGI models can make their own decisions and learn without human input. They’re logical and emotional.
Pure AGI models aren’t yet in existence, but we’re getting there. Chatbots and digital virtual assistants are getting good at maintaining conversations and can have emotional reactions to direct stimuli. Some researchers have also begun to train robots to read human emotions. While this is exciting, reading and producing emotional reactions doesn’t necessarily make AI emotional.
Artificial Super Intelligence (ASI)
When we talk about AI in science fiction, what we’re talking about is Artificial Super Intelligence or ASI, which is the second type of strong AI. ASI models will be machines that are smarter, wiser, and more creative than humans. This type of AI remains within sci-fi books. Scientists aren’t yet even dreaming of creating strong AI like this yet.
While this type of strong AI isn’t possible to create yet, scientists are making strides in a few different areas that will eventually lead to strong AI. Those include:
Robotics: building robots that are self-sufficient, such as Roombas.
Machine Reasoning: Training AI models to use deduction or induction based on a database or library to make decisions and come to conclusions.
Machine Learning: These are the algorithms and computer models that are used by machines to perform a specific task.
While the majority of AI remains weak or narrow AI and is only able to complete a single task, it’s still been revolutionary to businesses and individuals across the globe. The technology will continue to evolve and build upon itself getting smarter and smarter with each iteration.
Where does AI get its intelligence from? Machine Learning or ML. Machine learning is a subset of the larger field of AI. ML focuses on teaching computers how to learn without being programmed to do specific tasks. It can also refer to the algorithms and models that are used to teach and train AI to do specific tasks.
There are three key features in machine learning:
Datasets: A dataset is a collection of data points or samples. Each data point might be a number, image, word, audio file, or video. Datasets are used to train machine learning models.
Features: Features are data points that are the solution to the task and teach machine learning models what they’re looking for.
Algorithms: The algorithm is the process or set of rules the machine learning model uses to parse data and find a conclusion or answer.
At its most basic, a machine learning model uses an algorithm to read thousands or millions of data points and then come to a conclusion or prediction. To be able to read the data points correctly, the machine learning model needs an algorithm to tell it what to do. Before the machine learning model can parse data and reach conclusions, it must be trained. A dataset and features are used to train the machine learning model so that it can use its algorithm to reach conclusions based on real-world data.
One of the best applications of machine learning has turned out to be computer vision. Computer vision or CV is used in a number of different use cases, but one of the most exciting is self-driving, autonomous vehicles.
There are four different classifications of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
A supervised machine learning model is one that has a teacher which trains the model using a dataset with labeled training data. After training data is run through the machine learning model, the model is tested on new data. The researcher reviews the model’s results for accuracy. The machine learning model continues to be trained and retrained until the accuracy rate reaches a set threshold.
Supervised learning is often used for machine learning models that will be used for spam filtering, language detection, classification, and computer vision.
A machine learning model that is trained under unsupervised learning isn’t given any features or goals. This type of machine learning model is given data and then asked to search for patterns. Unsupervised machine learning models are good for clustering or categorizing data into groups and for analyzing data and providing insights. Unsupervised machine learning models are often used for data segmentation, anomaly detection, recommendation engines, and risk management.
Semi-supervised learning is a combination of supervised and unsupervised machine learning models. The machine learning model receives a dataset that is partially labeled and partially unlabeled. While the programmer may have a prediction for what patterns the machine learning model will find in the data, the model must find the structure in the data without any training.
A machine learning model that learns through reinforcement learning is in learning in a way that’s most similar to humans. Reinforcement learning is done through trial and error. The machine learning model learns from positive or negative reinforcement.
Reinforcement learning is one of the most exciting forms of machine learning models. It allows the programmers to step away from the model and to let the model learn on its own. It allows the model to learn dynamically from its imperfect environment. Reinforcement learning is used in games, robots, self-driving cars, and resource management.
Deep learning is a subset of machine learning. Deep learning is differentiated from other types of machine learning based on how the algorithm learns and how much data the algorithm uses. Deep learning requires large data sets, but it needs minimal manual human intervention.
Deep learning is intended to mimic the structure of a human brain, with complex, multi-layered neural networks. Data is transferred between neural networks through connecting channels. Deep machine learning models can use labeled data sets to learn, but they don’t necessarily need them. Deep learning models can be taught through supervised or unsupervised learning.
One of the most exciting aspects of deep learning for AI is that it can use unstructured or unlabeled data to learn. The ability to have a model which can learn unsupervised is the future of AI.
Key Differences Between AI, ML, and Deep Learning
AI, machine learning, and deep learning are all part of the same subject, but it’s important to understand the distinct differences.
AI is the overarching term for algorithms that examine data to find patterns and solutions. Artificial intelligence resembles the human ability to problem solve. Most AI projects use either machine learning or deep learning.
Machine learning is a type of artificial intelligence that uses data and an algorithm to solve one or more problems.
Deep learning is an advanced type of machine learning that uses neural networks to learn and make predictions using unstructured data.
Different Data Approaches to ML, AI, and Deep Learning
When it comes to AI, machine learning, and deep learning, there are a number of different approaches you can take to designing your project. The two most common approaches to designing and deploying an AI project are model-centric and data-centric.
The model-centric approach to AI projects is to spend the majority of your time working on developing the right AI or machine learning model. When the majority of your time is spent on the model, data can become an afterthought. In the model-centric approach, you collect the data you need but focus on creating a model that’s good enough to deal with the noise in your data. As you go, you improve and tweak the model and code to work even better.
In contrast, you have the data-centric model. This type of AI model focuses less on the model and more on the data. At Appen, our belief is that the data is the most important part of an AI project.
No matter what type of AI, ML, or deep learning project you’re designing, you’ll need high-quality data to start off. Data is food for AI. Data collection, cleaning, and labeling should be a large portion of your development process for your AI project. Even after launching your AI project, continue to label and improve your data, which will help you to get better and better results.
Machine learning and AI expert Andrew Ng suggests that data preparation should take 80% of your time and resources while the actual model training should only take 20%. While training has been the primary focus for most businesses that are launching AI projects, this sentiment is starting to shift. Research and data have begun to show that high-quality data makes for high-quality AI results.
How a Data-Centric Approach Improves Machine Learning Models
While it can seem contradictory to spend the majority of time collecting, cleaning, and labeling data instead of training your machine learning model, it can actually improve your project’s ROI and outcomes. Here’s how the data-centric approach improves machine learning models.
When it comes to data, consistency is key. Your data must be consistently labeled, even among different batches of data and from different data labelers. It’s critical that during the data labeling process you have high expectations and strong policies around quality assurance to ensure that your data is being properly labeled.
Using Noisy Labels
Another counterintuitive suggestion when building a data-centric machine learning model is to intentionally introduce some noise, or inconsistency, into your labeling process.
In smaller datasets, consistency is key. This helps to train the model and get high-quality results. But, when you start working with larger datasets a little bit of noise can actually be beneficial. When data is too perfect, your machine learning model won’t be able to function in the real world, where nothing is perfect. Adding some noise to your data can actually increase the accuracy and abilities of your machine learning model.
Create Systematic Solutions for Quality Assurance
One of the key steps towards a successful data-centric machine learning model is quality assurance. You need to have regular check-ins and check-ups throughout the entire lifecycle of your model and data.
During the data labeling process, it’s important to run quality assurance checks by making sure that all data labelers are labeling data points in the same way. If disparities are noticed during quality assurance, you can retrain labelers to correct the mislabeled data points. It’s also critical to continuously review your model and to check for drift. Machine learning models aren’t just trained and done. They must be checked, updated, and retrained regularly to make sure they’re still producing accurate results. Putting system-wide quality assurance policies into place can help to ensure that your machine learning model is always accurate.
While often used interchangeably, AI, machine learning, and deep learning are all slightly different parts of the same whole. Deep learning is a type of machine learning, while machine learning is a subset of AI. And, just like any other type of new technology, there are differing opinions on how to best implement and use this technology. Some think that the most important part of AI and machine learning models is the model itself. Others, ourselves included, know that the data is the critical component that makes a model work and give the highest return on investment. If you’re looking to launch a data-centric machine learning model at your company, you can learn more about how our data collection services can benefit your organization.