The terms AI, machine learning, and deep learning are often used interchangeably, which has resulted in a bit of confusion as to what each term actually means.
Generally speaking, deep learning is a subset of machine learning, which in turn is a subset of AI.
This means that AI is a field that encompasses both machine learning and deep learning. When someone talks about machine learning or deep learning, they inevitably talk about AI.
What does it mean to have AI capabilities
Simply put, AI is a science that is concerned with the simulation of human intelligence in machines. It’s about programming machines to think like humans, mimic their actions, and master cognitive abilities such as learning and problem-solving.
For example, AI work and research might entail making machine do the following actions:
- Reasoning. A machine solves a riddle by themselves.
- Learning and knowledge representation. A machine memorizes a text and can answer questions about it.
- Planning. A machine can optimize a process given appropriate data.
- See a picture or read a text. A machine can see what is depicted in a photo or read a document.
- Move and manipulate objects.
The field of AI is not only about computer science, although it often seems that way. The field also heavily draws upon other disciplines like mathematics, biology, information engineering, psychology, linguistics, and philosophy.
In practical terms, when a company says that their tech solution has AI capabilities, they are most likely referring to the fact that their tech can “learn” from the data it’s presented with.
While this might sound a bit sci-fi, many everyday products and services have AI capabilities. The most common ones are Spotify, Netflix, and Uber.
Where do machine learning and deep learning fit in
Machine learning and deep learning is about creating models and methods that can achieve specific cognitive capabilities described above.
While the field of AI draws on many different disciplines, machine learning focuses mostly on computer science and mathematics to create computer programs, or algorithms as they often are called.
Another term you will often hear, regardless if you are using machine learning consulting services and support or have an in-house team of data scientists, is “training the algorithm”. This means providing the algorithm with a dataset that represents the data needed to perform a specific task.
ML algorithms can be trained in four different ways.
- Supervised
- Unsupervised
- Semi-supervised
- Reinforcement learning
The amount of supervision refers to how much data scientists interpret and label the data before they train the algorithm on it. This is often referred to as feature extraction and classification.
While most ML algorithms need a data scientist to explicitly label data before it begins training, deep learning algorithms can “understand” data, label it themselves, and learn from it. They are often categorized under unsupervised learning.
Key takeaways
AI is an overarching field of simulating human intelligence in machines. The field draws upon many disciplines, such as mathematics, computer science, philosophy, etc. Machine learning and deep learning are subsets of AI, with a focus on creating algorithms that are focused on performing one task, whether it is identifying an object in an image, classifying customers into different categories, or putting together a list of songs.
Author | Emily Forbes
An Entrepreneur, Mother & A passionate tech writer in the technology industry!
Email:- forbesemily@yandex.com
