How to Build an AI: A Step-By-Step Guide
Artificial intelligence (AI) has been a hot topic since the 1940s when the digital computer was first developed. Since then, computers have been programmed to complete extremely complex tasks, including discovering proofs for mathematical theorems or playing chess.
AI has become a branch of computer science that enables digital computers or computer-controlled robots to carry out tasks that were once thought to be only within the realm of intelligent beings.
In this article, we will provide a basic understanding of artificial intelligence, its application, and the steps necessary for building an AI.
Understanding Artificial Intelligence
Artificial intelligence represents the ability of a digital computer or computer-controlled robot to perform tasks that were once thought to be the exclusive domain of intelligent beings.
Siri, Alexa, and similar smart assistants, as well as self-driving cars, conversational bots, and email spam filters, are all examples of AI.
Mathematician Alan Turing’s paper “Computing Machinery and Intelligence” and the Turing Test express AI’s fundamental goal and vision. Turing argued that there is no convincing argument that machines cannot think intelligently like humans.
According to Shane Legg, co-founder of DeepMind Technologies, intelligence is the agent’s ability to set goals and solve different problems in a changing environment.
If the agent is a human, you deal with natural intelligence, and if the agent is a machine, you deal with artificial intelligence.
AI Operation and Application
Building AI systems is becoming increasingly less complex and cheaper. The principle behind building a good AI is collecting relevant data to train the AI model.
AI models are programs or algorithms that enable the AI to recognize specific patterns in large datasets.
The better the AI technology is, the more effectively it can analyze vast amounts of data to learn how to perform a particular task. The process of analyzing data and performing tasks is called machine learning (ML).
Natural language processing (NLP), for example, gives machines the ability to read, understand human languages, and mimic that behaviour. The most promising AI apps rely on ML and deep learning, which operate based on neural networks built similarly to those in the human brain.
Real-world applications of AI systems are wide-ranging. The most common examples of AI in daily life include speech recognition, customer service, computer vision, the discovery of data trends, fraud prevention, and automated stock trading.
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How to Build an AI: What Is Required to Build an AI System?
The global artificial intelligence (AI) software market size was evaluated at USD 138.4 billion in 2022 and is predicted to hit around USD 1,094.52 billion by 2032, growing at a CAGR of 22.97% during the forecast period from 2023 to 2032.
But how can you build an AI? Let’s go through the basic steps to help you understand how to create an AI from scratch.
Step 1: Problem Identification
Before developing a product or feature, it’s essential to focus on the user’s pain point and figure out the value proposition (value-prop) that users can get from your product. By identifying the problem-solving idea, you can create a more helpful product and offer more benefits to users.
After developing the first draft of the product or the minimum viable product (MVP), check for problems to eliminate them quickly.
Step 2: Data Collection and Cleaning
When you’ve framed the problem, you need to pick the right data sources. It’s more critical to get high-quality data than to spend time on improving the AI model itself. Data falls under two categories:
- Structured Data: This is clearly defined information that includes patterns and easily searchable parameters, such as names, addresses, birth dates, and phone numbers.
- Unstructured Data: This includes audio, images, infographics, and emails and doesn’t have patterns, consistency, or uniformity.
Next, you need to clean the data, process it, and store the cleaned data before you can use it to train the AI model. Data cleaning or cleansing is about fixing errors and omissions to improve data quality.
Step 3: Create Algorithms
When telling the computer what to do, you also need to choose how it will do it. That’s where computer algorithms step in.
Algorithms are mathematical instructions. It’s necessary to create prediction or classification machine learning algorithms so the AI model can learn from the dataset.
Step 4: Train the Algorithms
Moving forward with how to create an AI, you need to train the algorithm using the collected data. It would be best to optimize the algorithm to achieve an AI model with high accuracy during the training process.
However, you may need additional data to improve the accuracy of your model.
Model accuracy is the critical step to take. Therefore, you need to establish model accuracy by setting a minimum acceptable threshold.
For example, a social networking company working on deleting fake accounts can set a “fraud score” between zero and one for each account. After some research, the team can decide to send all the accounts with a score above 0.9 to the fraud team.
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Step 5: Choose the Right Platform
Apart from the data required to train your AI model, you need to pick the right platform for your needs. You can go for an in-house or cloud framework.
The cloud makes it easy for enterprises to experiment and grow as projects go into production and demand increases by allowing faster training and deployment of ML models.
In-house Frameworks, such as Scikit, Tensorflow, and Pytorch, are the most popular ones for developing models internally.
Cloud Frameworks, such as ML-as-a-Service platforms or ML in the cloud, can train and deploy your models faster.
You can use IDEs, Jupyter Notebooks, and other graphical user interfaces to build and deploy your models.
Step 6: Choose a Programming Language
There is more than one programming language to choose from, including the classic C++, Java, Python, and R. Python and R are more popular because they offer a robust set of tools, such as extensive ML libraries. Make the right choice by considering your goals and needs.
Step 7: Deploy and Monitor
After you’ve developed a sustainable and self-sufficient solution, it’s time to deploy it. By monitoring your models after deployment, you can ensure they’ll keep performing well. Don’t forget to monitor the operation constantly.
Final Thoughts
“How to build an AI” is a question many are interested in these days. To make an AI, you need to identify the problem you’re trying to solve, collect the right data, create algorithms, train the AI model, choose the right platform, pick a programming language, and, finally, deploy and monitor the operation of your AI system.
With these steps, you can create an AI that can perform complex tasks and make life easier for humans.
