Artificial intelligence (AI) has existed for over half a century. Today, it is a well-known and frequently used technology, the foundation for innovative “successors” such as deep learning and neural networks, and a reality that is here to stay for the foreseeable future.
But creating solutions powered with artificial intelligence is still challenging. We have prepared a guide to help you accomplish it and learn how to develop AI software.
What Is AI, and How Does It Work?
The concept of artificial intelligence applies to a set of technologies, a sphere of knowledge, and the properties of computer systems. In a broad sense, it refers to the ability of machines to mimic the cognitive and creative functionality traditionally considered the prerogative of the human brain and intellect. In computer science, AI is defined as studying devices or systems that interact with their environment and act to succeed at a specific goal.
The most promising artificial intelligence apps are based on machine learning (ML) and deep learning that operates based on neural networks similar to ones that exist in the human brain. They automatically build graphs representing programmatic interpretations of memory algorithms. Data scientists implement models and use algorithms to process what appear to be vast quantities of structured and unstructured data in various forms so engineers can identify patterns in datasets and develop and “train” AI via reinforcement learning.
What Business Problems Can Be Solved with AI?
One of the most challenging issues is fraud with payments and sensitive private data. AI-based systems can detect and prevent such fraud effectively. Financial institutions have already learned to appreciate it: in 2020, the number of organizations using artificial intelligence to combat fraud increased by almost 70 percent over the previous year.
AI software can also:
- Reduce the number of financial crimes, prevent them, and increase cybersecurity;
- Improve business decision-making quality and forecast accuracy;
- Optimize companies’ number of employees and remove routine workloads from staff;
- Minimize human error and improve workflows through automation; and
- Solve specific, narrower problems, such as filtering email spam, speech recognition, and translation into text.
What Does It Take to Get Started?
To get started building an AI app, we first have to identify the problem-solving idea, the pain points of the product’s intended users, and the value proposition. Next, we need a dataset — data for implementation. If the client does not have a ready-made one, the project must provide time for its creation, search, and sometimes labeling. To label the dataset, you need a labeling team, for which the development company selects experts individually.
After preparing the data, we can determine the platform and programming languages needed to create the software. Many AI platforms provide developers with ready-made tools for product building, such as Google, Microsoft Azure, and Amazon Machine Learning. They combine intelligent decision-making algorithms and data. The programming languages commonly used for AI apps are Python, Java, and C++, but developers can choose another one according to their needs.
Other valuable tools for creating AI solutions are:
- Frameworks like CNTK, Caffe, Keras, PyTorch, scikit-learn, or Spark MLlib;
- ML-as-a-Service platforms that allow developers to use graphical user interfaces such as IDEs and Jupyter Notebooks;
- APIs that get exposed as REST endpoints and return JSON with results (e.g., Azure Topic Detection API).
How to Build Software with AI
Creating AI/ML solutions is an iterative process. The development pipeline in its basic form can be represented as:
- Research, discovery, and team planning;
- Data mining;
- Modeling;
- Minimum viable product (MVP) and product-with-improvements development; and
- Launching and support.
The base of any AI product is a model. Before launching it, we need to:
- Choose and train an algorithm and type of learning;
- Ensure that the data is organized, cleaned, and consistent;
- Define chronological order, add labels, and so on.
Most often, engineers use the Cross-Industry Standard Process for Data Mining (CRISP-DM) to collect and prepare data. It consists of a sequence of steps: business and data understanding, data preparation, modeling, evaluation, and deployment.
The next step is modeling. We use previously prepared data to train ML models via various methods. For instance, we at CHI Software apply deep learning or reinforcement learning techniques. When the model is trained, we have to test, evaluate, and deploy it.
In the development and support stages, you can also optimize performance, improve and expand functionality, adapt the product to updates of various operating systems, etc.
Conclusion
We have listed only the core issues related to artificial intelligence implementation in business solutions.
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