Artificial intelligence (AI) and machine learning (ML) are two types of intelligent software solutions that influence how past, present, and future technology is designed to mimic the most human traits.
Basically, AI is a technical solution, system or device that aims to mimic human intelligence to perform tasks while iteratively improving itself based on the information it collects.
Machine learning is a subset of artificial intelligence that focuses on building a software system that can learn or improve performance based on the data it consumes. This means that every machine learning solution is an AI solution, but not all AI solutions are machine learning solutions.
Artificial intelligence vs machine learning vs deep learning?
artificial intelligence. machine learning. deep learning. Although these terms are becoming increasingly popular, many people still feel like they are the subject of a science fiction movie. Let's simplify things and try defining one line for each term:
Artificial Intelligence (AI): Computer procedures that simulate human decision-making based on experiences and learned data.
Machine Learning (ML): The processes that allow computers to draw inferences from data. Machine learning is a subset of artificial intelligence that enables computers to learn outside of their programming.
Deep Learning: The processes that enable computers to solve very complex problems. Deep learning is a subset of machine learning that makes computational operations in multi-layered neural networks possible.
History of artificial intelligence
The idea of artificial intelligence dates back to the 1950s with the advent of computing technologies and capabilities in machines. The goal was simple: to go beyond using the computer as a means of calculation and actually drive decision-making.
This means that computers need to bypass the process of calculating decisions based on existing data; And it needs to move forward with a greater look at the different options to reach a more calculated deductive reasoning. However, how to achieve this in practice required decades of research and innovation. One simple form of AI is the creation of rule-based or expert systems. However, the advent of increased computer power in the 1980s meant that machine learning would transform the possibilities of artificial intelligence.
The evolution of machine learning
Rule-based decisions worked for simpler cases with explicit variables. Even computer-simulated chess is based on a series of rule-based decisions that include variables such as which pieces are on the chessboard, what positions they are in, and what their roles are. The problem is that these situations all require some level of control. At a certain point, the ability to make decisions simply based on variables and if/then rules don't work.
Then the trick was in simulating how humans learn.
Machine learning was introduced in the 1980s with the idea that an algorithm could process large amounts of data, and then start making conclusions based on the results it was reaching. For example, if a machine learning algorithm is fed large amounts of credit card transactions with if/then rules to discern fraud, it can then begin to identify secondary factors that created a pattern, such as the account buying something at unusual hours or in stores at different geographical location.
This process requires large data sets to start identifying patterns. But while datasets that include clear alphanumeric characters, data formats, and syntax can help the algorithm in question, other, less obvious tasks such as identifying faces in the form of problems create problems.
In the year 2000, technology took another step forward and the solution to this was to create a learning methodology that mimics the human brain.
Deep Learning vs. Machine Learning
Deep learning works by breaking down information into associative relationships—essentially making deductions based on a series of observations. By managing data and patterns that are inferred through machine learning, deep learning creates a number of references that will be used to make decisions. As with standard machine learning, the more data set to learn, the better the results of deep learning.
A simple way to explain deep learning is that it allows the extraction of unexpected context keys in the decision-making process. Consider how a young child learns to read. If he sees a sentence that says "cars are going fast," he might recognize the words "cars" and "going," but not "fast." However, with some thought, they can deduce the entire sentence due to context clues. "Speed" is a word he's likely to have heard about cars before, the illustration might show lines indicating speed, and he might recognize how the letters b and also x work together. These represent each individual item, such as "Do I perceive this message and what it looks like?" But when put together, a child's mind can make a decision about how it should look and read the sentence. This, in turn, will reinforce how he says the word "quickly" the next time he sees it.
This is how deep learning works, by breaking down different elements to make machine learning decisions about, and then looking at how they connect to an end result.
artificial intelligence software
AI software can use machine learning and deep learning-enabled decision-making and automation to increase enterprise efficiency. From predictive modeling to reporting generation to process automation, AI can transform the way an organization operates, driving improvements in efficiency and accuracy. Oracle Cloud Infrastructure (OCI) provides the foundation for cloud-based data management powered by artificial intelligence and machine learning.