Core AI: Understanding the Fundamentals of AI Algorithms and Models
This article focusses on the recommendations and explanations of GCore specialists, who are engaged in the development of such solutions and adaptation to the specifics and needs of each specific industry.
26/04/2023
Artificial intelligence (AI) is a branch of computer science that focuses on developing machines capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing. Algorithms and models are the core AI, its building blocks. Let's delve deeper into this topic, focusing on the recommendations and explanations of GCore specialists, who are engaged in the development of such solutions and adaptation to the specifics and needs of each specific industry.
What is an AI algorithm?
Artificial intelligence algorithms are rules or instructions a computer program follows to perform a specific task. AI algorithms can be classified into several categories, including:
- • controlled learning
- • unsupervised learning
- • reinforcement learning
- • deep learning
Supervised learning looks like a search for patterns in training data, and it is called unsupervised learning if the input data has no output labels. Such algorithms include k-means clustering and hierarchical clustering and dimensionality reduction algorithms such as principal component analysis (PCA) and t-SNE.
Q-learning, SARSA, and other algorithms belong to the cohort of learning algorithms with reinforcement in the form of rewards or penalties for actions performed.
The most advanced solution is neural networks based on deep learning. They can handle tasks such as image recognition, natural language processing, and speech recognition. Examples of deep learning algorithms include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
How do AI networks differ from algorithms?
Artificial intelligence models are created by
training an algorithm on a large amount of data
representing a problem. The resulting model can
then be used to make predictions or decisions
based on new data it hasn't seen before.
AI
models are suitable for predicting continuous or
discrete values and generating new data, such as
images or text, based on patterns. However,
there are several other types of AI models:
- Clustering. For example, it's excellent for grouping customers based on their buying behavior.
- Creating recommendations based on previous user behavior. For example, movie recommendations on Netflix.
- To analyze and understand visual information, such as object detection or face recognition.
- Speech recognition, machine translation, and sentiment analysis.
Artificial intelligence models are used in many industries, including healthcare, finance, transportation, and entertainment. The development of accurate and effective AI models continues so that the world may see even more impressive capabilities of intelligent machines in time.