There is no doubt that artificial intelligence (AI) is a powerful force in the technology sector. Virtual assistants are being integrated into new products, chatbots are answering customer questions on websites. Tech giants like Google, Microsoft, and Salesforce are integrating AI as an intelligence layer throughout their entire tech stack. AI is undoubtedly having a moment in the spotlight.
It’s worth mentioning that over time developing AI systems have become less complex and significantly less expensive. However, to successfully get into AI, you need a good understanding of statistics.
How do you build an AI system for your business? Is it really as difficult as it seems? These questions come up as we examine AI and its use in the industry. In simple terms, we can describe the structure of an AI system, without diving deeply into the technical details.
The development of an AI system contains the following stages:
- Identifying problems, you would like AI to solve.
- Preparing data
- Choosing algorithms
- Training of algorithms
- Programming language selection
- Choosing platform
1. Identifying the Problem.
Always keep in mind that AI is only a tool, not a solution. Consider how you can incorporate AI capabilities into your existing products and services. More importantly, your organization should have specific use cases in mind where AI can assist with business problems or provide demonstrable value toward solutions. To better understand the aforementioned, consider the following analogy. One needs to know which dish is being prepared and precisely which ingredients are needed to make a delicious meal.
2. Preparing the Data.
Data is classified into two types: structured and unstructured. Structured data refers to all types of data that are structured or organized in some way to ensure consistency in processing and easy analysis. A simple example of structured data is a customer record that includes the first and last name, date of birth, address, and other information.
Unstructured data are available in a non-formalized structure. They can include audio, pictures, symbols, words, and infographics. One of the greatest benefits of AI has been to enable computers to analyze unstructured data. In fact, the most important components of AI are not complex algorithms, but a data cleansing toolkit.
Normally, data scientists spend 80% of their time cleaning, moving, reviewing, and organizing the data prior to using it. Before running the models, make sure the data has been properly organized and cleaned up. This includes ensuring consistency, establishing a chronological order, labeling data as needed, etc.
3. Choosing and training algorithms.
Once the problem has been identified, the next step can be taken – choosing the algorithm. We will just mention that the model must be trained, and the data must be entered into the model after the algorithms have been chosen, as technical details are not covered in this article. Here, model accuracy is crucial.
Although there aren’t any internationally recognized or widely accepted thresholds, it’s crucial to assess the model’s accuracy within the chosen framework. A key factor is the choice of an acceptable minimum threshold and the application of a broad statistical discipline. Since it goes without saying that the models may need to be adjusted, the model must be trained once more.
4. Programming language selection.
Data scientists now have access to a wide range of programming languages, and the final choice is determined by their needs and priorities. Python and R are the most popular and widely used programming languages in data science today. Both are extremely powerful for data analysis, thanks to their numerous packages and extensive machine-learning libraries. NLTK (the Natural Language Toolkit) is a very powerful library for computational linguistics. It is a set of libraries and programs written in the Python programming language.
5. Choosing a platform.
With a variety of pre-built platforms, you don’t need to buy your own service, database, etc. Pre-built platforms (machine learning as a service) were one of the most useful parts of the infrastructure, thanks to which machine learning has spread. Developed to facilitate and simplify machine learning, they often deliver advanced, cloud-based analytics that work with multiple algorithms and languages and can integrate them. Typically, platforms help solve problems such as data pre-processing, model training, and valuation prediction. The most popular ones are Microsoft Azure Machine Learning, the Google Cloud Prediction API, TensorFlow, Ayasdi, and others.