Building an AI system

Artificial intelligence (AI) is a digital computer’s ability to conduct tasks that human beings perform. Examples of AI are self-driving cars, email junk filters and social media feeds.

According to information theory, intelligence is the transferring or accepting of information and keeping it as knowledge. The theory uses mathematics to demonstrate how data is processed and transmitted. Intelligence is the ability to have objectives and resolve problems in changing conditions. Humans have natural intelligence, while machines have artificial intelligence.

It is becoming cheaper and less complex to build AI systems. In order to train the AI model, it is necessary to have relevant data. The models are algorithms or programs that permit the AI to identify patterns in big data. If the AI technology is good quality, it will do better at analyzing the large quantity of data and master the performance of specific tasks. Performing tasks and analyzing data is called machine learning. The best AI apps use deep learning and machine learning. Deep learning uses similar processes as the human brain.

There are many AI system applications used in daily life, including:

  • Speech recognition
  • Customer service
  • Computer vision
  • Discovery of data trends
  • Fraud prevention
  • Automated stock trading

For those with an interest in this subject, it is possible to study an online MS data science program. The course from Baylor University in particular, teaches the efficient management and analysis of big data and includes advanced techniques of machine learning, data mining and visualization.

Building an AI

The process of making AI starts with problem identification and goes through several steps before the final one of deploying and monitoring. The following steps outline how an AI system is built.

Problem identification

A value proposition is the value promised to customers if they decide to buy a product. Decisions must be made on what is going to be solved and the preferred outcome. By selecting an idea to solve problems, a better product can be created that has more benefits for users.

The right data 

Choosing the correct data is essential to the process. Structured data has exact information that has patterns and searchable parameters. Examples are phone numbers, birth dates, names, and addresses. Unstructured data does not have uniformity, patterns, or consistency. It consists of emails, images, audio and infographics. The data needs to be cleaned, processed and stored before training the AI model. Cleaning data consists of correcting errors or omissions to ensure data quality.

Creating algorithms

Computer algorithms are used to select how the computer will operate. The AI system learns from the data file by using machine learning algorithms. Classification algorithms are used to put data into a category and can be employed on structured and unstructured data. Regression algorithms predict output responses or labels, which are numerical values for the input data.

Training the algorithms

The algorithm must be trained to use the accumulated data. The algorithm should be enhanced to ensure that the AI model is accurate. Additional data can mean that the model has more accuracy. Having a minimum threshold can ensure model accuracy.

An example is a logistic regression model that is used to identify spam emails. A result of 0.9996 for an email message predicts that it is most likely spam. In contrast, an email message with a result of 0.0005 on the same logistic regression model predicts that it is highly likely not to be spam. It is harder to make a decision about an email message with a prediction result of 0.6. To map a data analysis result to a twofold category, there must be a classification threshold. Values above the threshold mean that it is spam. A value below means that it is not spam. There can be an assumption that the classification threshold is always 0.5, but it can vary depending on the problem.

Select the platform

The next step is picking the right platform. This could be a cloud framework or an in-house framework. An advantage to the cloud is being able to grow and experiment when there is the production and increased demand. This makes faster training possible and enables the use of machine learning models. In-house frameworks include TensorFlow, PyTorch and scikit-learn. These are appropriate for models being developed internally. Cloud frameworks include Jupyter and Notebooks. Models can be trained and deployed quite quickly.

Language programs

There are numerous programming languages such as Python, R, Java and C++. R and Python are useful coding languages because they provide a toolset that includes numerous machine learning libraries. Goals and needs must be considered to help select the appropriate choice. Python can suit beginners as the syntax is simple and easily learnt. C++ has high efficiency and performance, making it a good match for games using AI. Java is often used to build desktop applications and is easy to use and debug. It is suitable for sizable projects and search engine algorithms. R is mainly used in data science, having been developed for statistics and predictive analysis.

Deploy and monitor

This step takes place when it is time to deploy the solution. It is necessary to monitor the operation continually to ensure that it keeps performing well. Monitoring the application could involve looking at traffic volume, app loading times, error rates, system metrics and infrastructure. Over time, machine learning models degrade as they respond to a changing environment. As soon as a model is deployed, it begins to deteriorate, so monitoring is essential.

The model can be maintained and improved in production. Monitoring can be used to identify problems with the model before there are negative consequences. Predictions and results must be reported with transparency to stakeholders.


AI is the ability of computers to carry out tasks that humans perform. Task performance and data analysis are called machine learning. Building AI systems has become more straightforward and cheaper. Making an AI involves identifying the problem that needs solving, collecting the correct data, choosing the best platform, picking a language program, and then deploying and monitoring the performance of the AI system.