Post by Admin on Jan 4, 2021 9:57:53 GMT -5
What’s the Difference between AI, Machine Learning and Deep Learning?
lionbridge.ai/articles/whats-the-difference-between-ai-machine-learning-and-deep-learning/
0) traditional computer algorithm
In a traditional algorithm, a developer will set specific rules that define an output for each type of input that the software receives.
1) AI algorithm
AI algorithms are designed to build out their own system of rules, rather than have those rules defined for them by a developer.
2) Machine Learning
Machine learning is generally considered to be a subset of AI. However, it’s pretty common to hear the two terms used interchangeably. The reason for this is simple. Almost all AI applications that exist today have been built through machine learning. AI is the grand vision of intelligent machines, while machine learning is the models, processes, and supporting technology that we’ve been using to try to get there.
While the concept of AI is fuzzy enough to leave room for them, the real progress we are seeing today is the product of machine-learning processes.
This field has some key characteristics which differentiate it from more traditional areas of computer science. The most important of these lies in the way that machine-learning algorithms are trained.
3) Deep Learning?
In a nutshell, deep learning is all about scale. As computing power and the availability of training data has increased, researchers have been able to take machine learning processes further than ever before. Eventually, this led to the use of a new term: deep learning. To fully understand its meaning, it’s essential to first know a little bit about neural networks.
lionbridge.ai/articles/whats-the-difference-between-ai-machine-learning-and-deep-learning/
0) traditional computer algorithm
In a traditional algorithm, a developer will set specific rules that define an output for each type of input that the software receives.
1) AI algorithm
AI algorithms are designed to build out their own system of rules, rather than have those rules defined for them by a developer.
2) Machine Learning
Machine learning is generally considered to be a subset of AI. However, it’s pretty common to hear the two terms used interchangeably. The reason for this is simple. Almost all AI applications that exist today have been built through machine learning. AI is the grand vision of intelligent machines, while machine learning is the models, processes, and supporting technology that we’ve been using to try to get there.
While the concept of AI is fuzzy enough to leave room for them, the real progress we are seeing today is the product of machine-learning processes.
This field has some key characteristics which differentiate it from more traditional areas of computer science. The most important of these lies in the way that machine-learning algorithms are trained.
3) Deep Learning?
In a nutshell, deep learning is all about scale. As computing power and the availability of training data has increased, researchers have been able to take machine learning processes further than ever before. Eventually, this led to the use of a new term: deep learning. To fully understand its meaning, it’s essential to first know a little bit about neural networks.