The artificial intelligence industry is booming at a tremendous rate and this expansion is difficult to capture if you are confused regarding the basic terminologies of AI and its various subsets. For common individuals, terms like AI, ML, and DL might sound somewhat similar, but if you are an AI enthusiast or someone looking to work in this field you definitely want to know the meaning and relationship between Artificial intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Computer Vision (CV). This blog will give you more clarity on these terms with their corresponding use cases in real-time.
The term artificial intelligence was first introduced by John McCarthy in 1956. AI simply means machines or computer systems created to mimic human behavior and perform tasks using human intelligence. Artificial intelligence can be developed using various technologies such as machine learning, deep learning, and robotics to perform specific tasks. The goal here is to create an optimum solution to solve a problem using a combination of technologies and components.
Everyday use cases of AI are chatbots, navigation apps, and facial recognition systems.
Arthur Samuel in 1959 first introduced the concept of Machine learning where computer models or sets of algorithms can learn from data by themselves without explicitly directing the systems. We can simply provide ML with data and directions to train the systems to perform specific tasks. This subset of AI works on structured data and demands manual feature extraction. ML only uses small amounts of data and can be done with minimal hardware configuration.
Machine learning is widely used in data security, medical diagnosis, and fraud detection.
The idea of Deep learning was first introduced by Igor Aizenberg in 2000. Deep Learning’s concept is to mimic the physical human brain and employ that functionality in the technology. DL is a subset of machine learning that works on unstructured data sets, which can extract features, and also classify them. DL requires large amounts of data sets and high-end machines for execution.
Deep learning algorithms are highly effective in self-driving vehicles, natural language processing, and virtual assistants like Siri and Alexa.
The image below signifies how machine learning and deep learning execute data sets.
In 1963 Larry Roberts first introduced the concept of Computer Vision. It can be defined as an interdisciplinary field of artificial intelligence that gives computer systems a high level of understanding from visual data such as images and videos. CV helps machines to retrieve meaningful information from images and videos just like human eyes. Computer vision algorithms depend on ML and DL algorithms to identify patterns and relationships in visual data.
Best use case examples of computer vision are autonomous vehicles, Facial recognition, and augmented reality.
There are various annotation types used in computer vision as displayed below. These labeling techniques are used for various image and video annotation services. You can learn more about computer vision and its different annotation techniques here.
I guess now you have a better clarity of AI and its various subsets. A lot of processes, data training, development, and testing go behind the development of Artificial intelligence.
Digital Divide Data is a company that is transforming technology while also implementing social services. They provide data preparation services to Fortune 500 companies and leading educational institutions for data labeling and data annotation for machine learning.