Mastering the Art of Making an AI: A Practical Guide

Introduction 

Developing an AI model from scratch is a major undertaking, even for experienced developers. The process is long and involves many different skill sets and knowledge of machine learning, artificial intelligence, and fundamental math and coding. This guide will provide all of the basic steps for developing your own AI and the different approaches currently being used by developers.

What is Artificial Intelligence?

Artificial Intelligence, or AI, is the technology that makes it possible for computers and machines to simulate human intelligence and problem-solving abilities. Either on its own or combined with other technology, like robotics or sensors, AI can perform tasks that would typically require some manual human intervention or intelligence. 

Many examples of AI at work are present in daily life including digital assistants, self-driving cars, GPS guides, and AI tools like ChatGPT. Artificial intelligence is a field of computer science that encompasses machine learning and deep learning. 

These disciplines cover the development of AI algorithms that are modeled after the human brain’s decision-making process. Using this approach, AI can learn from available data and make more accurate classifications and predictions as time goes on. 

The idea of artificial intelligence has been around for a while but only with the release of ChatGPT did people realize how powerful the technology could be in practice. Breakthroughs in computer vision were the last big advancement in AI but a new leap forward has occurred with the development of natural language processing. 

Advancements in AI have made it possible for the technology to learn and synthesize not only human language but also other data types like images and videos. 

Why Build Your Own AI? 

It’s becoming more common for businesses and organizations to build their own AI from scratch rather than using a pre-existing tool. Building your own AI has several benefits including:

• Tailored solutions to specific requirements
• Improved customer satisfaction
• Improved employee productivity
• Data-driven decision making

The use cases for AI are almost endless, depending on specific goals and industries. Many companies are already testing existing AI tools and discovering how the technology improves their workflows and operations. 

Machine learning and automation are now being leveraged to match specific internal requirements. Having access to custom tools means that companies have the power to design solutions tailored to their audience and pain points. Off-the-shelf AI tools are a more generic solution that may not meet specific requirements. A custom AI solution is also a good way to increase overall business resilience by utilizing custom solutions to complex internal problems.

Ultimately, the goal of any business is to satisfy customers. Custom AI solutions offer a competitive edge by allowing businesses to personalize the customer experience. AI chatbots, predictive analysis, and recommendation engines all utilize the power of AI to enhance customer interactions.

New technology can contribute to user-friendly customer experiences by making information more accessible. Each of these factors plays a major role in influencing customer loyalty and retention, which can lead to boosted ROI.

AI tools can also be used to optimize internal processes. Repetitive and time-consuming tasks can take up a big part of an employee’s work day. AI is extremely capable of automating these types of tasks, allowing employees to focus on more valuable tasks. Using AI automation can improve productivity while increasing employee satisfaction. 

Custom AI tools also allow businesses to utilize data to the fullest potential. AI can often give deep insight that may have otherwise been overlooked. By collecting, processing, and analyzing vast amounts of real-time data, AI can provide businesses with fast and accurate insights. 

Understanding the Basics 

AI is a complex field that contains different types and key concepts that encompass the basics. The following sections will break down the different types of AI and key concepts applied to make it all work. 

Types of Artificial Intelligence 

AI can be categorized into three main categories: Narrow, General, or Super. The AI has been categorized based on capabilities, with each having unique features and abilities. The three types of AI are discussed in more detail in the sections below. 

Narrow AI 

Narrow AI, also known as Weak AI, is the artificial intelligence that people have access to today. Narrow AI is the only form of AI that currently exists, with the other types being theoretical in nature. Narrow AI can be trained to perform single tasks better and faster than a human can. 

This type of AI is not capable of performing outside of its defined tasks. It instead targets single subsets of cognitive abilities and advances inside of that spectrum. ChatGPT is an example of a narrow AI, that is limited to single tasks of text-based chat.

General AI 

General AI, also known as strong AI, is a theoretical concept. General AI, in theory, could use previous knowledge and skills to accomplish new tasks in a different context without human intervention. This would allow General AI to learn and perform any intellectual tasks that a human being could.

Super AI

Super AI, commonly referred to as artificial superintelligence, is another theoretic concept of AI. If realized, Super AI would be capable of thinking, reasoning, learning, and making judgments using cognitive abilities that surpass humans.

Key Concepts in AI 

To better understand AI before starting development, it’s important to be familiar with the key concepts behind the technology. This includes understanding the mechanics behind machine learning, deep learning, and neural networks. These concepts have been explained in detail in the sections below. 

Machine Learning 

Machine learning is the subset of AI that involves training the algorithm to make predictions and decisions based on the data provided. Machine learning algorithms are typically trained using big datasets to learn patterns and train decision-making. 

This approach is a stark contrast to traditional programming, where the rules and logic are often explicitly defined. There are many different types of machine learning algorithms. These include supervised, unsupervised, and reinforced learning models. 

Supervised learning trains the algorithm on labeled data, while unsupervised trains on unlabeled data. Reinforcement learning trains the algorithm to make decisions based on reward and punishment. Machine learning is currently being used in industries to develop self-driving cars, as virtual assistant software, and for medical data analysis.

Deep Learning 

Deep learning is the subset of machine learning that involves neural network training using multiple layers to help with pattern recognition in data. Deep learning models are usually used for more complex tasks involving vast amounts of data. 

The model for deep learning consists of layers of artificial neurons that each process input data before passing it on to the next layer, resulting in predictions and decisions based on discovered patterns. Deep learning can be seen in facial recognition software, speech recognition software, and even natural language processing.

Neural Networks 

A neural network is a method used in AI to teach computers to process data in a way more similar to the human brain. This is another type of machine learning that is directly tied to deep learning, using interconnected nodes in layers to resemble the human brain. This creates an adaptive system that computers can use to learn from past mistakes and make improvements. 

Preparing for AI Development 

There are several steps involved when preparing for AI development. Most importantly, an understanding of basic math, and statistics, and a willingness to learn are invaluable during the early stages of development. It helps to start with a team of experienced developers who have worked on similar projects. 

The actual depth of understanding and mastery required in these areas will depend on the type of AI being developed. The key is to align the learning path with the end goal and adjust the depth of learning accordingly. Some of the key steps in preparing for AI development have been detailed in the sections below.

Setting Goals and Objectives 

Goal and objective definition is one of the most challenging aspects of AI development. Having a clear goal is important to the overall success of the project. AI developers can define the overall goals for an AI by writing algorithms and setting the parameters. 

Determining how the AI will be trained is another important consideration that should be agreed upon before development begins. It’s common for teams of developers to have colliding goals, so being sure of what to prioritize from the beginning will help streamline development. 

There are also ethical and social aspects that should be considered when setting goals and objectives for AI development. In the end, goal setting is an iterative process that requires constant analysis and refinement. AI development cannot truly begin until the goals and objectives are set, so it’s an important first step in the process. A diverse development team is vital during the goal and objective planning process, as the more viewpoints available, the more inclusive and well rounded the model will be.

Gathering the Necessary Tools 

There are several tools required for AI development. Some of the key requirements have been detailed in the sections below.

Hardware Requirements 

To make machine learning possible, there are certain hardware requirements that must be met. The processor, GPU, RAM, and storage must be able to support complex machine learning that could be taxing on lower-end systems. 

Intel and AMD are two reputable brands to look for when choosing a CPU for machine learning. Single-socket CPU workstations are generally recommended to reduce memory mapping issues across multi-CPU interconnects. 

The number of cores the CPU should have depends on the expected load for tasks not handled by the GPU. A good rule of thumb is at least four cores for each GPU accelerator. In most cases, 32 or 64 cores are ideal, with 16 being the minimum recommended. 

There is no doubt that NVIDIA currently dominates the GPU market when it comes to machine learning. These GPUs are likely the easiest to work with, though AMD also has several high-quality GPUs capable of compute acceleration. 

The amount of VRAM space needed will depend on the feature space of the model training, with multiple GPUs being more effective. When it comes to RAM, it is recommended to have at least double the amount of CPU memory as there is GPU memory total. When it comes to storage, more is better. An NVMe drive with at least 4TB capacity should be more than enough to get started. 

Software Requirements 

The software requirements needed for AI development will vary based on the specific goals and expectations. At the very least, an IDE for coding will be required as well as any cloud services or SaaS applications that may be needed during the development process. 

Learning Resources 

There are many different learning resources available for AI development. These include online courses, books and journals, and community and forums. Each of these resources has been discussed in more detail in the sections below.

Online Courses 

Online courses are a great way to get more familiar with the fundamentals of AI development. Udemy, IBM, and Udacity all offer full courses that cover the basics of AI and machine learning. These courses typically cover all of the fundamentals of AI development and how machine learning works.

Books and Journals 

Books and journals are another good way of learning about AI before development starts. Books like Artificial Intelligence: A Modern Approach by Stuart Russel and Peter Norvig offer a series of books that cover all of the basic AI concepts that are tailored for beginners and non-programmers. 

Other books like Driven: The Race to Create the Autonomous Car by Alex Davies and New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale offer insight into specific industries where AI is being utilized for unique goals. 

Community and Forums 

The internet is full of information on AI development and is a great tool for both beginners and professional AI developers. Communities and forums focused on the subject may be utilized to gather new knowledge that can be applied to the current development process. 

Data Collection and Preprocessing 

Data collection and preprocessing are two basic steps in the machine-learning process.  The predictions made by machine learning systems depend on quality data for training. Real-world data in its rawest form is often incomplete or inconsistent, lacking certain behaviors and trends needed for efficient training. 

Because of this, once data has been collected, it must be preprocessed into a format that a machine learning algorithm can understand.

Importance of Quality Data 

The quality of the data used to train AI is measured based on accuracy, completeness, relevance, and reliability. When AI systems are trained using high-quality data, they are more capable of learning effectively and making accurate decisions. 

AI models rely on patterns found in the data they are trained on. If the data has flaws, the AI will have issues that lead to poor decision-making and a low-quality product. Sourcing high-quality data is a big part of developing an AI and is not just a technical requirement but a strategic one. 

Sources of Data 

Because of the importance of high-quality data to the successful development of AI, being aware of the different options is vital. Typically, developers utilize public datasets or gather their own data and use it to train their algorithms. Both of the options have been explained in more detail in the sections below.

Public Datasets

Public datasets are commonly used in AI development. Many are free and include datasets of varying sizes. These datasets have already been accumulated and are usually being used by many different developers to train AI. 

Public datasets are convenient to use because they may contain unique datasets relevant to unique machine learning specifications. Public datasets are available online and come in a wide variety of different sizes and specifications. 

The downside of using these public datasets is the fact that they are not uniquely tailored to any specific needs that developers may require. For tailored datasets, it’s best to generate unique datasets.

Generating Your Own Data

Creating unique datasets is a more tailored way to train a machine learning algorithm. This allows developers to pick and choose exactly what data the algorithm is trained on to try and achieve very specific results. 

The process of creating a dataset for training AI involves collecting raw data, identifying features and label sources, selecting a sampling strategy, and splitting the data. Using a custom dataset will give an AI the edge over the competition in unique markets compared to an AI that was trained using generic public datasets.

Data Cleaning and Preparation 

Data cleaning and preparation is a major step in managing the quality of the data used for algorithm training. There are a few fundamental steps that shouldn’t be overlooked to help deal with outliers, data scrubbing, data transformation, and duplicate removal. 

Python and Excel are efficient data cleaning tools but more advanced approaches may be needed based on the condition of the dataset. 

Handling Missing Data 

Missing data can significantly lower the effectiveness of a dataset when training an algorithm. Missing values are a common issue that occurs when variables lack data points, resulting in incomplete information. 

This missing information can harm the accuracy and dependability of the model, making it essential to address missing data before training begins. Missing data can be resolved by creating a data frame and removing the rows with missing data. 

There are also strategies that involve replacing missing values with previous or next non-missing values in the same variable. This is a type of fill-in-the-blank method that is simple but varies in accuracy.

Normalization and Scaling 

Scaling refers to the act of transforming a dataset so that it fits within a specific scale. This changes only the range of the data, while normalization is a more significant transformation. During the normalization process, observations are changed so they can be described as a normal distribution. 

Normal distribution, also known as a bell curve, is a distribution method where equal observations fall above and below the mean.  Normalization is typically used when a machine learning algorithm assumes that the data is normally distributed.

Feature Engineering 

Feature engineering is the act of tailoring a dataset to better suit the needs and requirements of an AI in development. This process can improve a model’s predictive performance, reduce computational requirements, and improve the interpretability of the results. 

Building Your First AI Model 

Creating an AI model from scratch is a daunting task. It involves choosing an algorithm, a good framework, and an implementation plan to ensure that the development process goes smoothly. Continuous fine-tuning and optimization are required during and after development to keep the AI functioning properly after release.

Choosing the Right Algorithm

Creating or choosing an algorithm for the AI is the first step of the process. Which approach is best depends on the problem’s complexity, data volume, and developer experience. Choosing to create an algorithm from scratch will involve proficiency in programming languages and a good understanding of machine learning. 

Choosing an algorithm is often easier and more accessible, as they come pre-trained and can be fine-tuned to suit specific needs. Once a model has been selected, it’s time to decide how to train it using the datasets. 

Supervised Learning 

Supervised learning uses labeled training data to train algorithms. This gives the algorithm a baseline understanding of what the correct output should be. An algorithm under supervised learning will use a sample dataset to train itself and adjust itself to minimize errors. 

The datasets have been labeled for context and provide the desired output values. This allows the model to give the correct answer. Supervised learning is best if the focus is learning the relationships between input and output data.  

Unsupervised Learning 

Unsupervised learning uses data that is not labeled. This is more useful for discovering new patterns and relationships in raw data. This learning method is deployed to solve unique problems that supervised learning may not be capable of. Exploratory data analysis and anomaly detection are some circumstances where unsupervised learning is effective. 

Reinforcement Learning 

Reinforcement learning is a machine learning technique that involves training algorithms to make decisions that always achieve optimal results. This approach uses trial and error to train algorithms to reinforce actions and decisions that meet expectations. Reinforcement learning is a good way to get consistent results from a model that has had previous supervised or unsupervised learning. 

Frameworks and Libraries

The frameworks and libraries used in AI development can significantly impact the success of the model. An AI framework is an integrated suite of libraries designed to help with the development and deployment of AI models. Some of the popular AI frameworks include TensorFlow, PyTorch, and Scikit-Learn. These frameworks have been discussed in more detail in the sections below.

TensorFlow 

TensorFlow is an end-to-end framework for machine learning created by Google. This framework allows developers to perform a wide variety of different downstream tasks. It utilizes tensors as the basic data structure with operations performed by building stateful dataflow graphs. 

PyTorch 

PyTorch is a popular alternative to TensorFlow, developed by Meta. PyTorch is commonly used in research projects and is customizable and adapted to running tensor operations on GPUs. It also uses tensors as the basic data structure but is generally considered easier to use than TensorFlow.

Scikit-Learn 

Scikit-Learn is a user-friendly framework that contains many useful tools for classification, clustering, and regression. It can also handle preprocessing very well and includes many different evaluation tools. This framework may be more suited for new developers. 

Implementing the Model

Once a framework has been decided on, it is time to implement the model. This involves data splitting, training the model, and evaluating the progress.

Data Splitting 

Data splitting is an important part of machine learning. This process involves partitioning datasets into subsets like training and test sets. Data splitting is a vital part of the training process required for tuning parameters and assessing performance. 

Random splitting is commonly used to randomly dive the datasets into different training, validation, and test sets. Stratified splitting is another approach that attempts to distribute the data consistently across the different subsets. 

Time Series splitting ensures that the order of the data is preserved during partitioning. This method is designed to handle time series data where order is important. Once the data has been split, the training can begin.

Training the Model 

Before beginning model training, the algorithm should be optimized to achieve high accuracy during the training process. More data may be needed for a significant increase in accuracy. Accuracy is the most important aspect of model training, so setting up a minimally acceptable threshold is vital to the process. 

Once the data is ready, it must be fed into the model so it can learn from the patterns. Training requires constant monitoring to ensure that accuracy levels remain above the threshold.

Evaluating the Model 

Once the model has been trained, it’s important to evaluate its performance. Accuracy, precision, and recall metrics should be analyzed to identify areas where adjustments can be made to improve accuracy.

Fine-Tuning and Optimization 

Once a model has been deployed, there is still fine-tuning and optimizations that can be done. This is important for constantly improving the model and making sure it is functional and matches current industry standards.

Hyperparameter Tuning 

Hyperparameter tuning involves selecting optimal sets of hyperparameters for a model. This is an important step in model development, as the hyperparameters can have a major impact on performance. 

There are several different ways that machine learning optimization is implemented. Most are model and data-centric approaches. Both approaches involve searching for the optimal combination of hyperparameters with a predefined set of values.

Regularization Techniques 

Regularization is another major optimization that can reduce overfitting and enhance model generalization and model complexity. There are several different regularization techniques used during machine learning development. 

L1 Regularization encourages sparsity in the parameters, causing some coefficients to shrink to zero, causing feature selection in the process. L2 Regularization shrinks the coefficients evenly but will not bring them down to zero. This approach helps with multicollinearity and stability. 

Elastic net regularization is useful when correlations exist among features or to balance out feature selection with coefficient shrinkage.

Advanced Techniques 

Some advanced techniques involve diving deeper into the way AI and machine learning really work. A deeper understanding of neural networks, natural language processing, and deep learning may be necessary to take models to the next level.

Deep Learning and Neural Networks

Deep learning refers to the act of teaching computers to process data in a way that is inspired by the human brain. Deep learning models recognize data patterns and are able to produce accurate predictions and insights. Neural networks are the foundational technology used in deep learning. 

Understanding Neural Networks 

A neural network works by using interconnected nodes and neurons layered in a way that closely resembles the human brain. This approach creates an adaptive system that computers can use to learn and improve. Artificial neural networks are used to solve increasingly complex problems like summarizing documents or face recognition with much greater accuracy.

Training Deep Learning Models 

Training deep learning models involves feeding the model data so that it can use trial and error to increase its accuracy and improve decision-making. The neural network is laid out like the human brain and is able to constantly learn from the datasets. Training a deep learning model typically requires huge datasets, so it may take a while to prepare the data for training. 

Natural Language Processing (NLP) 

Natural Language Processing is a field of computer science and AI that focuses on using human language to communicate with computers. 

Basic Concepts in NLP 

There are three basic concepts of NLP. These include data cleaning, tokenization, and vectorization. Data cleaning is necessary to remove stop words, make letters lowercase to improve formatting, reduce words to a single form, and remove patterns that are not required in the text. 

Tokenization is used to split phrases, sentences, or paragraphs into smaller units. Vectorization is done after cleaning and tokenization. During vectorization, words are mapped to vectors to aid with prediction accuracy. 

Implementing NLP Tasks 

There are a few different approaches to implementing NLP tasks. These include supervised NLP, where models are trained with sets of labeled data with known input and output. Unsupervised NLP uses non-labeled data and statistical language models to predict patterns. 

Natural language understanding is a subset of NLP that handles sentence analysis. This allows the model to find similar meanings in different sentences. 

Computer Vision 

Computer vision is a field of AI that utilizes machine learning and neural networks to teach computers to extract meaningful information from images and videos. This allows AI to effectively see and obverse data visually. 

Basic Concepts in Computer Vision 

The basic concept behind computer vision is digital image processing. Digital image processing deals with enhancing and understanding images using different algorithms. One operation commonly used in computer vision is convolution, which is an operation that convolves a learnable kernel with an image. 

Implementing Vision Tasks 

To implement vision tasks, first images must be acquired. These images must then be processed, usually by a deep learning model that has been fed thousands of labeled and pre-defined images. Finally, the image must be interpreted, classified, and identified. 

Deployment and Maintenance 

Once an AI model has been completed, it’s time for deployment and maintenance. Choosing a deployment strategy is important for the overall success of the model, so being aware of the different options is important.

Choosing a Deployment Strategy 

The most common deployment strategies for an AI model include cloud-based solutions and on-premises deployment. Both have their strengths and weaknesses, which are discussed in the sections below. 

Cloud-Based Solutions 

Cloud services are a popular deployment solution. Cloud-based deployment ensures resources can be rapidly adjusted to accommodate demands and increase the scalability of the model. It is more convenient to use cloud-based solutions for AI deployment since the infrastructures are maintained by the service provider. 

There are plenty of plug-and-play AI cloud services that make deployment quicker and easier than on-premises. Cloud-based solutions can be expensive, especially for models with large storage requirements or a higher GPU count. 

On-Premises Deployment 

On-premise deployment means that the hosting and infrastructure will be handled manually. This is typically the cheaper option after initial deployment and is often considered more secure than storing data in the cloud. It is also generally easier to invest in on-premise infrastructure than cloud-based solutions.

Monitoring and Updating AI Models 

Once an AI model has been deployed, it should be continually monitored and assessed for performance metrics. These metrics can be used to make continuous improvements and ensure the model is functioning as intended. 

Tracking Performance 

Tracking performance is an important part of the post-deployment process. The accuracy of the model should be constantly evaluated so areas of improvement can be identified. In some cases, a model might benefit from reinforcement learning or retraining to improve results. Without a deep insight into the performance of a model, there is no way to be sure what areas need improvement.

Continuous Improvement 

A good AI model should be constantly improving. As technology advances, AI models should follow suit. To ensure continuous improvement, deployed AI models should be routinely monitored and tested for performance and improved when possible. 

New techniques and technologies should be introduced to keep up with advancements in the industry. It is important for AI developers to stay on top of the newest trends to ensure their model is not left behind, and increase overall scalability.

Conclusion 

Developing an AI from scratch is a big task that requires many different skills and a deep understanding of machine learning and AI. Many developers find custom AI solutions more effective at solving unique problems, which is why developing unique AI models has become popular. 

Utilizing the current development tools and approaches to AI development can make this process much easier. If you are planning on developing your own AI, ParallelStaff can match you up with developers and IT specialists with expertise in the field of AI. Schedule a call now to get started!