AI vs Machine Learning vs Deep Learning – What’s the Difference ?
AI vs Machine Learning vs Deep Learning – What’s the Difference ?As we sit on the cusp of the 4th Industrial Revolution and Web 3.0, business leaders and consumers cannot afford to be Luddites. However, what deters most people from learning about new technology is the bombardment of jargon. In turn, it makes these tools inaccessible to people outside of technological science. Case and point, the topic of this guide. If industry leaders want to address the disconnection between technology and the people it’s aimed towards, they must address the knowledge gap first.
Hence AI, machine learning, and deep learning are three concepts that are often confused with each other. Whilst they’re related, they aren’t essentially the same thing. The following guide explores what the differences are and helps you in deciding which technology relates to your goals the most.
Shall we start with article AI vs Machine Learning vs Deep Learning – What’s the Difference ?
What is Artificial Intelligence (AI)
Firstly, Artificial Intelligence in computer science and technology is a field most concerned with giving computer systems and machines (agents) the ability to self-sufficiently cognize (think). Ultimately considered, as the next step in automation. So, artificial intelligence grants machines the ability to predict and optimize their tasks regardless of changing situations.
There are three levels of AI:
- Artificial Narrow Intelligence (ANI): The current stage of AI. This describes AI that is only capable of completing specific but simple tasks on its own. Examples include chatbots and enemies in games. It is considered weak AI.
- Artificial General Intelligence (AGI): The second stage of AI. This describes AI that displays the ability to mimic rudimentary human cognition/intelligence. Ultimately, computer systems with this type of strong intelligence can address a variety of different tasks.
- Artificial superintelligence (ASI): The final phase of AI. Computer systems that display this type of AI can surpass human cognition. In many cases, machines with ASI are sentient. In truth, we may never achieve this level of AI.
What is Machine Learning
Fundamentally, people acquire skills and knowledge through learning and practice. This ability is what makes us intelligent – our adaptability. Thus, if we hope to create a computer system that self sufficiently thinks on its own, we must teach it how to learn first.
This is the goal of machine learning. A field that is often closely associated with data science as data ultimately informs machine learning models. We’ve seen ML used in a variety of fields including medicine, agriculture, sports, etc.
Additionally, Machine Learning models are famously used in data labeling, either visually (computer vision), textually, or phonically (Natural Language Processing and audio processing). Fundamentally, machine learning hinges on data structures and algorithms.
A machine learning algorithm can be condensed into three parts:
- A decision process: Where the algorithm receives input data and acts upon it based on patterns in the data. These patterns can then be used to make a prediction or classification.
- An Error Function: This evaluates the accuracy of the prediction/classification created during the decision process using known examples.
- A Model Optimization Process: Determines if the model can meet the data points more accurately. If so, it adjusts the parameters (weight) to ensure a more accurate connection between the known example and the model estimate. The algorithm repeatedly performs this evaluation until the model meets the uppermost standard of accuracy.
Generally, you have to follow a general workflow to solve a machine learning problem. You begin with an image and extract all the relevant features from it. Next, you create a model that describes or predicts the object.
Nevertheless, this is a simplistic reductionist explanation of one approach. The next section will highlight a few others.
Machine Learning Approaches
There are three machine learning categories:
- Supervised Machine Learning: Uses labeled data to train machine learning models. The use of labeled data means both the input and output are known to the operator/programmer. Ultimately, all the model is required to do is map the input to the outputs. For instance, this could be used to train a machine learning model that identifies plants or animals from a set of images.
- Unsupervised Machine Learning: Uses unlabelled data to train machine learning models. Unlike supervised machine learning, the use of unlabelled data means there is no fixed or known output variable. Instead, the model learns from the data, finds patterns and features in the data, processes it, and then produces an output. A good example of this is a machine learning model that is tasked with categorizing vehicles as either trucks or buses. The model will be trained using the parts and features of the vehicle. This includes the type of wheels, hood, length and width, front and rear-end covers, etc.
- Reinforcement Machine Learning: Allows a machine learning model to learn from an agent and an environment. This often occurs in a trial-and-error-like fashion. The agent has a start stage and an end stage. There may be multiple routes for reaching the end stage/state. Furthermore, there are no predefined variables in this approach. A good example of reinforcement machine learning is in a model that is tasked to predict the shape of an object after it has been trained using a list of shapes.
What is Deep Learning?
Data and computer scientists believe that Deep Learning grants us the second stage/phase of AI (AGI) and beyond. Deep learning ultimately uses the brain as inspiration to form an artificial neural network that will be capable of displaying human like intelligence.
As previously mentioned, we use labeled data to train the most common machine learning models (supervised). The machine algorithm then produces an output based on this data. However, Deep Learning produces an output or performs a task without human intervention.
In the case of images, they’re fed directly into the deep learning algorithm which then predicts or identifies the object. No need to train or extract the feature from the images first. Ultimately, deep learning is an unsupervised form of machine learning. There are four types of deep learning architecture:
- Standard Neural Networks (SNNs)
- Recurrent Neural Networks (RNNs)
- Convulutional Neural Networks (CNNs)
- Genarative Adversarial Neural Networks (GANNs)
Hardware for Deep Learning
Here, the Deep Learning requires a high-performance Graphics Processing Unit (GPU/Graphics Card) and lots of labelled data. This is because deep learning is complex and you’ll require a lot of sample data (images) to get reliable results.
The high-performance GPU is an important requirement because a complex neural network requires a lot of processing power for images. The more powerful the GPU, the shorter the time it will take for the neural network to process all available images.
If you simply can’t supply the data or hardware required for deep learning, you can use a supervised form of machine learning. As there is a greater variety of classifiers to train your machine learning model. Nevertheless, promising developments have been made in generative deep learning – a process where models are instructed on how data is generated and how labels are assigned. This results in neural networks and machine learning models that require less labelled data and are far more accurate.
The Relationship Between AI, ML, and DL
Again, both machine learning and deep learning aim to progress artificial intelligence. Better neural networks in Deep Learning helps us to create better machine learning models, ultimately leading to improved AI. Here’s a comparison of these three concepts:
AI vs Machine Learning vs Deep Learning Tabular Comparison
| Attirbute | AI | Machine Learning | Deep Learning |
|---|---|---|---|
|
Definition |
A field of study concerned with developing autonomous computer systems and machines with human-like cogntive abilities. |
A sub-field of AI that teaches machines how to learn so they can achieve intelligence. |
A sub-field of machine learning that uses brain-like neural networks to achieve machine learning and ultimately, AI. |
|
Relationship |
AI is a complex field that consists of many aspects and subfields. These subfields include machine learning and deep learning. |
Machine Learning is a sub-field of AI. |
Deep Learning is a subtype of machine learning and AI. |
|
Approach |
AI uses a variety of tools and techniques to make autonomous intelligence for computer systems possible. |
Machine learning uses algorithms that give computer systems the ability to learn. |
Uses a brain like neural network to facilitate machine learning and AI |
|
Types |
There are three broad types of AI:
ANI, AGI, and ASI. |
There are three types of machine learning algorithms:
supervised, unsupervised and reinforcement learning |
There are four types of neural network architectures: standard neural networks, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and generative adversarial network (GAN) |
|
Year of Conception |
1956 |
1943 |
1943 |
|
Usage Examples |
Digital assistants, chatbots, AI art , and video game enemy bots. |
Image recognition, predictive analytics, speech-to-text, and social media algorithms. |
False news detection, music composition, face recognition, and data aggregation. |
Thank you for reading AI vs Machine Learning vs Deep Learning – What’s the Difference ? We shall conclude this article.
AI vs Machine Learning vs Deep Learning - What's the Difference ? Conclusion
Summing up, AI vs Machine Learning vs Deep Learning, these concepts are often used interchangeably because they are so closely interlinked and related. They aren’t the same thing. Deep learning and machine learning are basically the building blocks of Artificial Intelligence. Essentially, artificial intelligence is what we want machines or computer systems to have while machine learning and deep learning describe how we will get there.
