Step-by-Step Methods To Build Your Own AI System Today

Introduction

AI or Artificial Intelligence is a hot topic in the world of technology, especially considering all the hype surrounding it. Given all the hype, it becomes imperative to answer fundamental questions like how to create an AI? Or, how to build an AI system? We would be discussing and explaining both of these questions in a very non-technical, easy to understand language to help make a foundational understanding of the term Artificial Intelligence.

The steps to build an AI

Before we dive into the meat of the case in point, it is equally important to understand that building an AI system is very different from what the traditional programming is because AI tends to make improvements to the software automatically.

Also, it is imperative to grasp that making or building an AI system has not only gone down in cost but also in complexity. One example is Amazon Machine Learning of an easy to work with AI, which automatically classifies products in the catalog by making use of the description of the product as its dataset.

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Listed below are the steps on how to build an AI system:

1. Problem Identification

The very first step in creating a sound AI system is identifying the problem at hand. Ask questions like “what outcome is desired?” and “what is the problem that is being attempted to solve here?” Another thing that has to be kept in mind is that AI is not a panacea. It is merely a tool that could be used to solve the problems. Many different techniques could be used to solve a particular problem with AI.

2. Preparation of Data

One might think that the long lines of code corresponding to the algorithm used are the backbone of any sound AI system. In reality, it is not. Data is a crucial part of any AI toolkit. It is usual for the data scientist to spend over 80% of the time cleaning, checking, organizing, and making the data fit to be used before writing even a single line of code.

Thus, before any model is run, the data must be checked for inconsistencies, labels must be added, a chronological order must be defined, and so on. It is generally known that the more messages one gives to the data, the more likely it will solve the problem at hand.

There are mainly two kinds of data, namely structured and unstructured.

• Structured data: The data which has a fixed format to ensure that it remains consistent is called structured data.
• Unstructured data: Any form of data which does not have a fixed format, like images, audio files, etc. is classified as unstructured data.

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3. Choosing an Algorithm

Now comes the core or the best part of building an AI system. Without delving much into the technical details, there are still a few fundamental things that need to be known for building an AI system. Based on the type of learning, the algorithm can change the shape it takes. There are majorly two ways of learning, as listed below:

• Supervised Learning: As the name suggests, supervised learning involves the machine to be given a dataset on which it would train itself to provide the required results on the test dataset. Now, there are several supervised learning algorithms available, namely SVM (Support Vector Machine), Logistic Regression, Random Forest generation, naïve Bayes Classification, etc. An excellent way to understand the supervised learning of classification would be by knowing if our final goal was to gain insight on a particular loan, especially if the knowledge we seek is the likelihood for the loan to default. 

On the other hand, the regression type of supervised learning would be used if our goal was to get a value. The value, in this case, could be the amount that might be lost if the loan has defaulted. 

• Unsupervised Learning: This type of learning differs from supervised learning because of the types of algorithms. These categories can be classified as clustering, where the algorithm tries to group things; association, where it likes finding the links between the objects; and dimensionality reduction, where it reduces the number of variables to decrease the noise. 

4. Training the algorithms

A crucial step to ensure the accuracy of the model is training the chosen algorithm. So, after selecting an algorithm, training the algorithm is the next logical step in building the AI system. While there are no standard metrics or international thresholds of model accuracy, it is still essential to maintain a level of accuracy within the framework that has been selected.

Training and retraining is the key to build a working AI system because it is natural that one might have to retrain the algorithm in case the desired accuracy is not reached.

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5. Choosing the best language for AI

We have a variety of options to choose from when it comes to choosing the language; we decide to write the code and build our AI systems. There are many languages out there, like the classic C++, java and more modern languages like python and R. Python and R are by far the most popular choices for writing the code for building the AI systems.

The reasoning behind the choice is simple. Both R and python have extensive machine learning libraries that one can use to build their models. Having a good set of libraries means that one would spend less time writing the algorithms and more time in actually building the AI model. The NTLK or the natural language toolkit library in python is a useful library that gives users access to pre-written code instead of making them write everything from the ground up.

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6. Platform Selection

Choosing the platform which provides you with all the services needed to build your AI systems instead of making you buy everything you need separately is very crucial. Ready-made platforms like Machine learning as a service have been a very important and useful structure to help spread machine learning.

These platforms are built to help ease the machine learning process and facilitate in building the models. Popular platforms such as Microsoft Azure Machine Learning, Google Cloud Prediction API, TensorFlow, etc. help out the user with issues like data preprocessing, model training, and evaluation prediction. 

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Conclusion

The field of AI or artificial intelligence shows a lot of scope for many developers out there. However, this technology is still in its nascent stages. With that being said, the field of AI is developing at a very fast rate, and in the near future, it is a huge possibility that AI could go on to do very complex tasks. Thus, getting an answer to questions like how to create an AI?, and, how to build an AI system? becomes more important than ever.

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