Progress in the development of computer technology

Artificial Intelligence

In this article we will talk about machine learning. But since this is an extensive section of artificial intelligence, let’s start with it.

Experts of any field of activity at least once in their life heard such a thing as artificial intelligence, but did not delve into the work of this scientific field. AI (Artificial Intelligence) – refers to the modeling of human intelligence in machines that are programmed to think like humans and simulate their action.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

When most people hear the term artificial intelligence, the first thing they usually think about is robots. This is due to the fact that high-budget films and novels combine stories about machines similar to people who destroy the Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined so that a machine can easily imitate it and perform tasks from the simplest to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception.

Machine Learning

This is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Previously, complex problems existed only in leaf theory. But thanks to modern technologies and computing power, machine learning began to be used in real tasks. If we talk about the definition itself, then we need to touch on other concepts like neural networks and deep learning. 

To understand how machine learning methods work, consider a table that describes things that a machine can and cannot:

Machine ables Machine doesn’t able

Become smart smartly

Predict Create your own kind
Reproduce Go beyond the given
Choose the best Return From The Future To Kill Sarah Connor

The task of machine learning is to establish patterns on the basis of available data, so that in the future, building on these patterns with the intention of making a prediction. This is done by training the model. The model is based on Training Data. The machine learning algorithm also brings the coefficients of the model so that the sum of the errors of the trained data is minimal, that is, we differentiate the function. Further, there are many other methods for checking the adequacy of the model, where we can identify unnecessary features. For example, when our attributes will be very strongly correlated with each other, respectively, they are interdependent, and therefore they should not be included in the model.

Let’s imagine that there is a problem where we cannot immediately determine the answer using one model. In this case, we divide this task into parts where the answer of each of the models will affect the next. For example, you need to define the written input of a digit by the user. To recognize numbers, you need to divide each digit into fragments, and train the model in fragments. Here is such a formationlogical connection is called neural networks:

The input data layer is our fragments, then processed in the inner layer and the result is output.

These processes of developing machine learning methods require significant labor and technical resources. Also, complexity arises in time. For example, a financial specialist, first of all, needs to collect all the data and determine their dependence during the consolidation so as not to retrain the model, especially when it comes to the problem of clustering. In the future, it will be necessary to divide the data into parts in order to test them separately, in order to understand their nature more deeply. And at the final stage, create a well-trained model that will further solve the decision-making problems, and reduce the risks of the enterprise.

Nevertheless, there are many technical solutions that will help you achieve these calculations in a matter of days. Caseware IDEA software solution will help you do this quickly and efficiently. The methods built-in to create automated processes are designed in such a way that the auditor or other financial specialist does not waste time mastering the new profession of a programmer. The program interface is very convenient. To create a model with modern machine learning methods, use Python, the interpreter of which is built into Caseware IDEA. Use libraries like Scikit-learn, NumPy, Matplotlib. Train your model with new data with one button, and get the result with the second button.