How to create an AI system in 5 steps

Artificial intelligence, machine learning, and deep learning: all of these terms have grown in popularity over the last ten years. The huge increase in processing power, as well as the widespread adoption of cloud computing, has given us the tools to build AI capable of performing some of the most incredible tasks imaginable.

From AIs writing articles about themselves to AIs winning art competitions, the limits of autonomous systems are tested daily. This has led many to wonder how to develop their own AI system. How can I improve my business with AI? It must be difficult, right?

In truth no. Starting from scratch can be extremely difficult (there's a reason these tools are developed by top-notch engineers). But there are hundreds of tools on the market, both commercial and open source, designed to make the process easier. With the right mental framework, some guidelines, and a solid plan, you'll be building an AI in no time.

Which programming language is used in AI?

Before we dive in, we need to talk a little about the basics of AI, including which programming languages ​​are best suited for creating your own.

Any robust programming language is perfectly capable of building AI systems , but a few of them stand out as the best languages ​​overall. In some cases, it's because the language has built-in AI-friendly functions, while in others, it's because the community has rallied around these languages, producing tools to facilitate AI systems. Here's a quick list.

Phyton

Break it down any way you want and Python will almost always be on top as one of the most popular programming languages. It is an interpreted and all-purpose programming language that has earned its place for its ease of use, readability, and large number of packages, libraries, and frameworks.

Python is a fantastic language for AI, with dozens of tools designed to make the process easier. PyTorch, for example, is a very powerful framework for machine learning that has a simple, user-friendly interface built in Python (or if you're up to the challenge, C++). It should come as no surprise that this language is a favorite, considering it has been adopted as a reference for the data science community.

Julia

Of all the options on this list, Julia is the youngest, and that's a good thing. Julia was built from the ground up to be a data science language – one that covers most of the limitations of the other languages ​​on this list, is less syntactically complex than Java or C++, and is faster than Python or R.

It is a language that is slowly gaining ground in the data science community. And you should pay attention if you're interested in AI and emerging technologies .

R

R was the king of data science until Python came along. This open source alternative to the S language has been an academic favorite for some time. It's not the easiest to use (or understand), but its plethora of libraries supported by the scientific community are hard to replace.

Other popular languages ​​include Scala, Java, and C++, due to their massive adoption and popularity within and outside the world of software engineering. Although sometimes dense, these three stand out for their performance and well-nourished ecosystem.

What does it take to build an AI system?

To build your AI system, there are a few steps to follow.

#1 Set a goal

Before writing your first line of code, you must define what problem you want to solve. AIs are trained to solve specific problems, and the less defined your problem is, the more difficult it will be to build your solution. At this stage, if you intend to use your AI as a product, you must define your value proposition: What is the problem and why is it a good idea to invest in your product to solve it?

#2: Gather and Clean Data

As I've always said, a model is only as good as the data it's created with, so having the right data to train your AI project is extremely important. What do we mean by correct data?

• The data is relevant to the problem you are trying to solve.
• There is enough data to adequately represent all possibilities and outcomes.
• The data is not biased.

Data comes in two broad types: structured and unstructured . Structured data is clearly defined information with simple search parameters – for example, the contents of a spreadsheet. Unstructured data, on the other hand, is complex and cannot be easily analyzed – for example, a transcript of a conversation.

As every data scientist knows, data is almost never structured. Most of the time we have to clean it and organize it to understand it. This same principle applies to AI. Preparing data by ordering it, deleting incomplete entries, and sorting it is called data cleaning.

#3 Create the Algorithm

No two AIs are the same. A language learning model is very different from a perceptual AI. Neural networks and deep learning, random forests, k-nearest neighbors (KNN) and symbolic regression are some of the mathematical foundations of AI, each serving its own function and solving a specific type of problem.

For example, neural networks are fantastic for predictive models, while KNN is built for classification. The nature of the task and the scope of your project will help you evaluate which algorithm is best for your project.

Some companies like Google offer pre-trained AI models, ready to be customized and deployed. They are built with millions of data inputs and are more robust than most of us are capable of realizing. Instead of training from scratch, you could use one of these services.

#4 Train the Algorithm

An AI needs to learn its task; This is what we call training. As a standard, most data scientists use 80% of their dataset to train their models, and the remaining 20% ​​is used to assert the model's predictive capabilities. Training means that AI identifies patterns in data and makes a prediction based on those patterns.

5# Deploy the final product

With the AI ​​trained, it's time to refine the final details and deploy the product. At this stage, we define the user interface and its scope, and if it is a service, we build the brand around it.

From the automobile industry to common everyday tasks, AI is becoming a core technology in almost every field, and with the sudden increase in interest and revenue potential, it is to be expected that new tools are emerging for developers and non-developers alike. can build smart solutions. systems. Remember, knowing how to make an AI is only half the battle, as they say, the devil is in the details.

If you liked this, be sure to check out our other articles on AI.

• Hyperautomation
• The Impact of AI on Software Testing: Challenges and Opportunities
• Is it really easier to implement AI today?
• How IoT, AI and Blockchain Lead the Way to a Smarter Energy Sector
• Is AGI even possible? What science fiction tells us about AI

Common questions

How can companies determine the ROI of implementing AI software development?

Companies can determine the ROI of AI systems by establishing KPIs for these systems depending on their main concerns. For example, they should assess what business problems they are trying to solve and consider how AI can help solve them.

What ethical considerations should companies consider when developing AI projects?

There are several ethical considerations that companies must consider when developing AI solutions, such as bias, discrimination, safety and security, transparency, privacy, and accountability.

What are some common challenges companies face when implementing an AI model?

Companies face a number of challenges when implementing AI systems, such as a lack of adequate skills and knowledge, security and privacy concerns, lack of available quality data, and resistance to change among team members.

How can companies ensure the security of their AI systems?

To ensure the security of Artificial Intelligence systems, companies must work with AI and cybersecurity experts to protect themselves and their data. They must also perform routine security assessments, use secure networks, update software regularly, train employees on how to use tools correctly, and implement disaster recovery protocols.

How can companies stay ahead of advances in AI technology?

Staying ahead of advances in AI technology requires a proactive and strategic approach. Companies can achieve this by investing in continuous learning and development programs for their teams, fostering a culture of innovation and experimentation. Furthermore, it is crucial to maintain close relationships with academia and leading AI research institutions, as these entities often lead the development of new methodologies and advances in AI. Finally, embracing open source AI projects and contributing to their communities can provide insights into new developments and best practices in the field of AI. By staying informed and adaptable, companies can not only leverage AI technologies to improve their operations, but also drive innovation in their respective industries.