AI has many potential uses across industries worldwide. For people just starting out with AI, learning the basics of how it works can be challenging. This article is intended to teach the fundamentals of AI algorithms, show you the data side of things, and get you started on the exciting topic of machine learning. The goal of this article is to give you an easy-to-understand overview of what AI is, and how it is being used, so that you can understand its possible use cases and the potential impact of AI
Summary
The purpose of this guide is to provide a detailed overview of how Artificial Intelligence (AI) works, with a focus on the role of algorithms. An algorithm is an organized series of steps or procedures for processing data, enabling AI systems to learn from data and apply those learned experiences to make intelligent decisions.
The guide will go through three primary types of algorithms, each with distinct features, uses, and applications. Those types include: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
We will also examine how Data Science is key to increasing the functionality of Artificial Intelligence (AI) with several types of support, for example, data cleansing to ensure accuracy and reliability of data being utilized; data visualization to provide an easily understandable format of data that is complex; and statistical analysis to help identify trends and patterns within the data itself.
In addition, we will discuss three of the primary Machine Learning Models (Linear Regression, Decision Trees, and Neural Networks), which are instrumental in translating AI theory into practice.
When you understand each of the basic elements and principles involved with Artificial Intelligence (AI), you are then able to fully appreciate how AI takes unprocessed and raw data and converts it into valuable and meaningful information to be used in many areas of the real world and in many different types of industries.
What Are AI Algorithms?
At the core of Artificial Intelligence (commonly referred to as AI) is an algorithm. An algorithm is a comprehensive method, formula, or process that defines the step-by-step procedures to resolve a broad array of problems. Algorithms play a key role in AI because they provide an efficient means of analyzing data and have the unique ability to learn through their own processing.
Through this learning process, they can make meaningful decisions based on insights from their data analysis. Thus, algorithms can be considered the foundation, or backbone, of all AI systems, enabling them to mimic human thought, logic, and decision-making. As such, they are highly effective in resolving very complex and challenging problems and tasks.
Types of AI Algorithms
There are different ways to categorize AI algorithms, and each category has its own specific function in artificial intelligence.
- The most common category of machine learning (and artificial intelligence) algorithms is supervised learning. Supervised learning involves training an algorithm on a labeled dataset, i.e., a dataset containing both the input data and the corresponding output (or label) data. As a result, this category of algorithm is particularly well suited to tasks requiring exact results — e.g., classification (categorizing data points into predefined categories) and regression (predicting continuous values from input attributes).
- unsupervised learning: Unlike supervised learning, unsupervised learning uses unlabeled information. Therefore, there are no “right” or “wrong” answers for algorithms, as they attempt to find relationships and structure in the information. A good example of unsupervised learning is clustering, where, using the attributes of each piece of information (i.e., variables), the data can be organized into groups (i.e., clusters) regardless of the user’s prior knowledge of the information’s organization.
- Reinforcement Learning: Reinforcement learning is a method for training a model to make choices, with the model improving by receiving positive reinforcement (rewards) or negative reinforcement (penalties). Reinforcement learning is a method in which a model takes an action in an environment and receives feedback as a result. That information allows the model to improve and optimize its future decision-making, ultimately achieving better positive outcomes while minimizing negative ones in environments with many sequential decision-making opportunities.
The Role of Data Science in AI
What is Data Science?
Data Science is an emerging field that focuses on extracting knowledge from all kinds of Data. It has massive potential in the Artificial Intelligence space due to its ability to provide the necessary tools, methodologies, and approaches to collect, process, and analyze Data. The collection, processing, and analysis of Data are critical to allow AI Algorithms to gain experience, improve their capabilities over time, and deliver better results through more informed decisions. Data Science is an essential component in providing these capabilities.
Importance of Data Cleaning
Data cleaning is an essential part of the data preparation process, which involves examining and modifying the dataset before model training. The purpose of data cleaning is to identify all errors in the data before model development, ensuring the accuracy and reliability of the results are not compromised. The thorough identification and correction of data inaccuracies will result in higher data integrity, more reliable models, and better model results.
The Power of Data Visualization
Data visualization uses images or graphics to analyze data and display trends and patterns. Charts, graphs, etc., are used to help people understand data visually; they help them grasp it more quickly. By presenting raw data in an image format, we can better see underlying patterns and relationships that may not be obvious from the numbers alone.
Utilizing Statistical Analysis
Statistical analysis is a very effective method for analyzing data sets to discover relationships between variables and identify trends. Statistical methods are used to analyze data and identify patterns and associations. Data analysis also helps us understand interactions among variables, which assists in decision-making and provides a clearer understanding of their implications.
Understanding Machine Learning
Machine Learning is one of the many areas of Artificial Intelligence (AI), but it focuses primarily on developing systems that can self-improve through experience and learning. What is especially unique about Machine Learning is how well computers can perform multiple tasks independently.
Unlike traditional programming, where the programmer must create an instruction for every possibility, Machine Learning enables these computer systems to automatically adjust their actions and capabilities based on the data collected and the experiences encountered. Therefore, as each new piece of data is processed, the system’s decision-making ability improves, requiring less continuous human intervention.
Machine Learning Models
Linear Regression
Linear Regression is a statistical technique in which you use a simple model to make predictions on a continuous outcome variable (a variable with multiple possible values) using one or more predictor variables (the things that will predict or explain the outcome).
Linear Regression finds an equation that best describes the relationship between the predictor(s) and outcome variable(s), so you can make predictions for new data points. These equations allow you to estimate the value of a prediction and gain insight into how past data has influenced your prediction(s).
Understanding Linear Regression
Statistical analysis using linear regression allows you to assess how a variable may be related to other variables. Linear Regression is most often used to predict the value of one variable from another. Most simply put, linear regression involves finding a line that best fits all the data points to see how one variable relates to another.
Linear Regression assumes a linear relationship between two variables (e.g., when one variable changes, the other should change predictably), allowing the movement of both variables to be predictable.
For example, you could be using Linear Regression to study the relationship between a person’s height and their weight. By plotting all your individuals’ heights and weights and drawing a straight line through them, you would have created an equation to predict a person’s weight from their height.
Linear Regression has significant power and is widely used across many disciplines, including economics, biology, engineering, and the social sciences. Linear Regression provides researchers and analysts with valuable information necessary to make informed predictions and decisions. Analyzing how linear regression works also provides insight into the dynamics of the variables and their interactions.
Linear Regression: Applications and Limitations
Regression analysis is a statistical method used across disciplines and is useful for analyzing relationships between variables and for predicting future trends and outcomes based on prior information.
For example, many companies use regression analysis to predict future sales from historical sales data. Also, scientists use regression analysis to determine how weather conditions (such as high temperatures) will impact harvests. Regression analysis uses the most appropriate linear equation to describe the relationship among data points, enabling users to estimate future occurrences or to determine the extent of the relationship between variables.
While regression analysis can provide valuable insight into relationships between variables, several major limitations should be considered. The most significant limitation of regression analysis is that it makes assumptions regarding the relationship between the independent variable(s), and the dependent variable(s).
These assumptions are that the relationship between the independent variable(s) and the dependent variable(s) will be linear. If the proper relationship between the independent and dependent variable(s) is more complex, then the predictions provided by the linear regression analysis will not be reliable. Regression analysis is also significantly affected by outliers, which can substantially alter the results.
Lastly, regression analysis can only establish relationships between variables and cannot prove causality.
Even though two variables may show a relationship, this does not indicate that the independent variable(s) caused the dependent variable(s) to behave in such a manner. While regression analysis can provide significant value in identifying relationships between variables, it is essential to recognize its limitations and use it in conjunction with other statistical techniques as needed.
Decision Trees
Decision Trees are an adaptable model used in Machine Learning to solve Classification or Regression problems, using all of the input variables (features) to predict the Target Variable. Each Node represents an “if-then” type decision made with respect to the one feature being evaluated at that point; this process determines the path through the tree from root to leaf.
This enables the creation of complex decision paths using Decision Trees, thereby providing the versatility to support a wide variety of predictive models.
Structure and Functionality of Machine Learning Models
The primary goal of developing machine learning models is to analyze data, identify patterns, and use these findings to predict outcomes or take action. Machine learning models are developed by applying algorithms that allow them to “learn” from the data they review.
A machine learning model has several different parts including the input data (the base upon which the model will operate), features (the distinct, measurable attributes or qualities of the input data), the algorithms (the mathematical formulas and procedures the model uses to evaluate the input data and generate the output) and the output (the classification(s), prediction(s), recommendation(s), etc. generated by the model).
The input data provides the basis for the model to understand the context and specific details of the data being evaluated. The features are significant because each can enhance or diminish the model’s performance. The algorithms used determine how the model evaluates and interprets the data to produce the output.
Machine Learning (ML) model functions can differ significantly depending on the model type and application. Supervised Learning models can be developed using labeled training datasets, where the expected output value is known; therefore, the model can learn from the examples in the training set. Unsupervised Learning Models may analyze input data with no associated output values, with the ultimate goal of identifying patterns or grouping similar input data points.
In addition to Supervised & Unsupervised Learning models, several other ML models operate through different mechanisms (e.g., Reinforcement Learning), which utilize the model’s output to guide its decision-making by selecting actions within an environment to achieve a goal.
Factors affecting the success of many of these models include the quality of the input data, the complexity of the algorithm(s), and the characteristics of the problem(s) being solved. Understanding both the internal “structure” of ML models and how they function will help develop effective uses of ML across a wide variety of applications, including but not limited to Natural Language Processing (NLP) and Image Recognition.
Neural Networks
A “neural network” is a type of computational model developed to simulate how the human brain processes information. The model consists of layers of interconnected processing units (nodes), called neurons. The nodes or neurons process data from the input layer and learn from it to perform tasks such as classification, regression, and pattern recognition in the output layer.
Layers in a basic neural network can be broken down into three categories: input layer, hidden layers, and output layer. All layers transform the input data into the output.
In most cases, neural networks learn by being trained on a large set of examples (a “dataset”) during the training process. While the model learns from the training set, the network continuously adjusts the “weights” at each connection between neurons in response to the difference between the actual and correct predictions.
The continuous alteration of these weights is typically achieved using an optimization algorithm referred to as “backpropagation. Backpropagation continually reduces the network’s error and improves its prediction accuracy until it has learned enough to make accurate predictions on new, unseen data. Once the network has been adequately trained, it may be used for many different purposes, including recognizing images and speech, understanding written and spoken natural languages, and making strategic decisions in games.
A Machine Learning Model Based on the Structure of the Human Brain
Machine Learning is an ever-changing field of research; one of the most captivating areas is how the brain works. Some Machine Learning models are inspired by how the brain functions and is structured. Some machine learning models are developed to model the brain, with its many neural connections, synapses, and other cognitive processes. This gives us a more natural way to build our models and provides a method for developing new ones.
Neural networks are brain-inspired models that consist of layers of interconnected nodes, similar to how neurons connect to communicate. Neural networks use layered processing to analyze data, enabling them to recognize patterns and generate predictions from the input they receive.
Like people learning, neural networks can adjust their node-to-node connections (weights) to become better at what they do over time. These adjustments are similar to how we gain knowledge and experience through repeated exposure to various stimuli and respond to them in new ways.
Also, because of their ability to perform a variety of tasks, such as image recognition, natural language processing, and even playing games, machine learning is instrumental across many areas. These areas include Healthcare and Finance, where they will analyze datasets and assist in decision-making. The more we learn about how to develop the model, the more capable it becomes, and the more problems it may solve that could never have been solved.
Conclusion
It is essential to understand artificial intelligence algorithms, data science, and machine learning if you want to be involved in and enjoy the wonderful field of artificial intelligence. The three concepts described above serve as the basis for all AI systems.
AI systems can take unprocessed raw data, process the information, and convert it into valuable and actionable information; therefore, as technology continues to evolve and expand the uses and applications of the tools discussed, there is an increased need to understand the fundamental concepts of artificial intelligence to remain relevant in this rapidly evolving technological environment.
Whether you are a newcomer to the field of artificial intelligence or simply looking to update and improve your knowledge of its basic elements, we hope this guide provides a concise, easy-to-understand, and educational introduction to the vast and exciting field.
