Artificial intelligence (AI), deep learning, and machine learning (ML) are all terms that have taken hold in the last ten years. The massive increase in processing power, combined with the widespread adoption of cloud computing, has provided us with the tools to create AI capable of performing some of the most incredible tasks imaginable.
The limits of autonomous systems are being tested daily, from AIs writing papers about themselves to AIs winning art contests. This has prompted many people to wonder how they can create their own AI system. How can artificial intelligence help my business? It must be difficult, right?
No, it does not. Starting over could be extremely difficult (there is a reason why top-tier engineers build this software). However, hundreds of commercial and open-source tools are available to help with the process.
You’ll be creating an AI in no time if you have the right mental framework, a few guidelines, and a solid plan.
What is the programming language used in AI
Before we go any further, we need to cover the fundamentals of AI, such as which programming languages are best for creating your own.
Any solid programming language can create AI systems, but some stand out as the best overall. In some cases, this is because the language includes AI-friendly functions, while in others, it is because a community has formed around these languages, producing tools to aid AI systems. Here’s a quick rundown.
Python: No matter how you slice it, Python will almost always come out on top as one of the most popular programming languages. It is an all-purpose programming and interpreted language that has risen to prominence due to its ease of use, readability, and a vast library of packages, libraries, and frameworks.
Python is an excellent AI programming language, with dozens of tools available to help with the process. PyTorch, for example, is a very powerful machine learning framework with a friendly and simple interface written in Python (or, if you’re feeling ambitious, C++). It should be no surprise that this language is popular, given that it has become the standard for the data science community.
Julia: Julia is the youngest on this list, which is good. Since Julia was built from the ground up to be a data science language, it addresses the majority of the drawbacks of the other languages on this list while being quicker than Python or R and less syntactically complex than Java or C++.
It is a language gradually gaining traction in the data science community. Keep an eye out if you are interested in emerging technologies.
R: Until Python came along, R was the undisputed king of data science. Researchers have long favoured this open-source S language substitute. Although it’s not the simplest to use (or comprehend), its abundance of scientifically backed libraries is hard to match.
Scala, Java, and C++ are popular languages owing to their widespread adoption and popularity in and outside the software engineering world. While dense at times, these three stand out due to their performance and well-managed ecosystem.
What Is Needed to Create an AI System?
- Establish a Goal
Before you write your first line of code, you must decide what problem you want to solve. AIs are trained to solve specific problems; the more vague your problem, the more difficult it is to develop a solution.
What is the issue, and why is purchasing your product a wise decision to address it? These are the questions you must answer if you intend to sell your AI as a product.
- Collect and Clean the Data
A model is only as good as the data that it was trained on, so having the right data to train your AI is critical.
What exactly do we mean by “the right data”?
- The information is important and relevant to the issue at hand.
- The data is not skewed. There is enough data to represent all possible outcomes and outcomes adequately.
Data is classified into two types: structured and unstructured. Structured data is clearly defined information and has simple search parameters, such as the contents of a spreadsheet.
Unstructured data, however, is complex and difficult to parse, such as a conversation transcript.
Data is rarely structured, as every data scientist knows. We usually have to clean and organize it to make sense of it. The same holds for artificial intelligence. Cleaning the data involves preparing it by organizing it, deleting duplicates, and categorizing it.
- Design the Algorithm
There are no two AIs alike. A language learning model is not the same as a perception AI.Neural networks and deep learning, random forests, k-nearest neighbours (KNN), and symbolic regression are some of the mathematical foundations of AI, with each serving a particular purpose and addressing a particular kind of issue.
Neural networks, for example, are excellent for predictive models, whereas KNN is designed for classification. The nature of the task and the project scope will help you determine which algorithm is best for your project.
Pre-trained models are offered by some businesses, including Google, ready for customization and deployment. These were created using millions of data entries and are much more powerful than the majority of us. Instead of starting from scratch, you could use one of these services.
- Algorithm Training
An AI must learn its task; this is referred to as training. Training entails the AI identifying patterns in data and making predictions based on those patterns. The majority of data scientists use 80% of their data set for model training and the remaining 20% for confirming the model’s predictive power.
- Release the Final Product
After the AI has been trained, it is time to finalize the details and release the product. We define the user interface and scope at this stage, and if it’s a service, we build the brand around it.
AI is becoming a core technology in almost every field, from the auto industry to common daily tasks. With the sudden increase in interest and revenue potential, it’s to be expected that new tools for developers and non-developers alike to build intelligent systems will emerge.
