Artificial intelligence and machine learning are very closely related. And it’s this connection that makes you really measure how they interact when you look at the differences between AI and machine learning.
What is Artificial Intelligence (AI)?
Artificial intelligence is the ability of a computer system to mimic human cognitive functions such as learning and problem solving. Through AI, a computer system uses mathematical functions and logic to simulate thought processes that enable people to learn from new information and make decisions.
What is Machine Learning?
Machine learning is one of the applications of AI. It uses mathematical data models to help a computer learn without direct instructions. This allows computer systems to learn and improve on their own through experience.
One way to train a computer to mimic human thinking is to use a neural network. It is a series of algorithms modeled on the way the human brain works. A neural network helps a computer system create artificial intelligence based on deep learning. This close relationship explains why it is useful to consider how they work together when comparing AI and machine learning.
Neural networks and deep learning
Any neural network is a set of neurons (functions) and connections between them. The task of the neuron is to take the input numbers, perform certain actions on them and return the result. An example of a useful neuron: sum up all the numbers from the inputs and, if their sum is greater than N, send one to the output, otherwise - zero.
Connections are channels through which neurons send numbers to each other. Each bond has its own score - a parameter that can be conventionally represented as bond strength. When the number 10 passes through the connection with an estimate of 0.5, it turns into 5. The neuron itself does not understand what has come to it, and sums everything up. It turns out that the estimate is needed to control which inputs the neuron should respond to and which not.
Neurons and connections is a shorthand, in real programming a neural network is a matrix and everything is considered matrix representations, since it is efficient in terms of speed.
What are neural networks for?
- definition of objects on video and photos;
- Photo processing;
- speech recognition;
- Machine translate.
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