Artificial intelligence (AI) and its subset of machine learning (ML) and deep learning (DL) play an important role in data science. Data science is a complex process that involves pre-processing, analysis, visualization, and forecasting. Let’s dive into AI and its subset.
Artificial Intelligence (AI) is a computing industry that involves the creation of intelligent machines capable of performing tasks that normally require human intelligence. AI is basically divided into three categories, as shown below.
Artificial Narrow Intelligence (ANI)
General Artificial Intelligence (AGI)
Super-artificial intelligence (ASI).
Narrow AI, also known as ‘weak AI’, certainly performs one task in a certain way. For example, the theft of an automatic coffee machine that performs a well-defined sequence of coffee preparation operations. While AGI, also called ‘strong AI’, performs a wide range of tasks requiring thinking and reasoning as a person. Examples include Google Assist, Alexa, chatbots that use natural language processing (NPL). Super-artificial intelligence (ASI) is an advanced version that transcends human capabilities. He can engage in creative activities such as art, decision-making and emotional relationships.
Now let’s look at machine learning (ML). It’s a subset of AI modeling algorithms that help make predictions based on recognizing complex patterns and datasets. Machine learning aims to allow algorithms to learn from the data provided, collect information, and make predictions based on previously un analyzed data using the information collected. Different machine learning methods are available
managed learning (weak AI – task-oriented)
unaccompanied training (strong AI – data-based)
semi-managed training (strong and profitable AI)
improved machine learning. (Strong AI – learn from mistakes)
Machine learning with a teacher uses historical data to understand behavior and make predictions for the future. Here the system consists of a special set of data. It is marked with input and output parameters. And when the new data arrives, the machine learning algorithm will analyze the new data and give an accurate result in accordance with the fixed parameters.
Machine learning with a teacher uses historical data to understand behavior and make predictions for the future. Here the system consists of a special set of data. It is marked with input and output parameters. And when the new data comes in, the machine learning algorithm will analyze the new data and give an accurate result in accordance with the fixed parameters. Learning with a teacher can perform classification or regression tasks. Examples of classification tasks: image classification, facial recognition, spam classification, fraud detection, etc.
Automatic machine learning does not use categorized or marked parameters. It focuses on detecting hidden structures of unmarked data to help systems get the function right. They use methods such as clustering or reducing dimension. Clustering groups data points with similar metrics. It’s based on data, and some examples of clustering – movie recommendations for Netflix users, customer segmentation, shopping habits, etc.
Semi-controlled machine learning works using both tagged and unmarked data to improve learning accuracy. Semi-managed learning can be a cost-effective solution when data labeling is expensive.
Training with reinforcement is very different from learning with a teacher and learning without a teacher. It can be defined as a trial and error process that ultimately yields results. t is achieved on the principle of an iterative cycle of improvement (to learn from past mistakes). Reinforcement training has also been used to train agents to drive autonomously in simulated environments. Training is an example of reinforcement learning algorithms.
Before Deep Learning (DL) is a subset of machine learning in which you build algorithms that follow a tiered architecture. DL uses multiple layers to gradually extract higher functions from raw input.