Generative AI has become a hot topic after the ChatGPT release. The reason is mainly because previous groundbreaking technological leaps, such as the wide distribution of personal computers or the Internet, could only help realize one’s potential. In their turn, generative platforms introduced a dramatic change to what a specific individual can do. Such solutions can not only improve the way you perform routine tasks but also allow you to do things beyond your skill set, such as drawing or writing code.

It’s no wonder that businesses quickly recognized the importance and potential of generative artificial intelligence. However, since this technology is relatively new and complex, it may be challenging to understand how it functions, which tool better suits specific needs, and its possible drawbacks. Today, we’ll try to fix that.

What Is Generative Artificial Intelligence?

Let’s start with the basics. In machine learning, you train models by feeding them with data so that they can make some valuable predictions or generate new data based on what they learn. Suppose your goal is to predict earthquakes more accurately. In that case, you can gather all the info you have on these natural phenomena, such as seismic data, geodetic data, geological data, and historical data, and give it to the ML model. It can use these data and indicators to learn the complex and nonlinear relationships between the input variables and the output targets. Also, it can be used to identify the anomalies, trends, and precursors that may signal an impending earthquake.

In turn, generative artificial intelligence is a particular class of models designed by AI developers to create content following the user’s requests. It can process many different inputs and generate different outputs. For example, there are text-to-text, text-to-image, text-to-video, text-to-code, image and text-to-image, and other types of generative models.

How to Train Your Generative AI

Naturally, the next big question arises. How exactly do developers train their generative AI systems and make them recognize and create data? There’s not one, not two, but three primary approaches we’ll consider today.

The unsupervised learning approach implies that the data you feed to the model does not include any info on its answer correctness. You don’t provide the model with hints on categorizing this or that piece of data. Here, developers are focused on processing data sets to identify patterns that have some meaning for the user. Clustering, or simply speaking, grouping related data sets, is the commonly used technique here. It’s an actual human being whose task is to provide meaning to the grouped examples. There are multiple clustering algorithms, such as K-means, DBSCAN, Gaussian Mixture Model, BIRCH, Affinity Propagation, and Mean-Shift. The K-means clustering algorithm, for example, focuses on the proximity of examples to a centroid:

Source: Google for Developers

A human researcher can then breathe meaning into this data by labeling these clusters as “tiny little trees” and “big trees.” Also, models can use this learning approach for processing weather data sets, and then researchers can label specific clusters that better correspond to particular weather patterns:

Source: Google for Developers

On the other hand, supervised learning processes large data sets and must be provided with correct answers to determine the connections between them and the data itself. Say you can use this technique to learn a model to recognize handwritten letters within a data set of images with labels indicating which letter is correct. “Supervised” means a real human being is required to provide these answers. There are two most common models used in supervised learning:

• The regression model is designed to predict continuous numerical values. For example, it can recognize the relationship between a dependent variable (rainfall volume) and one or more independent variables (temperature, humidity, and wind speed, affecting the rainfall);
• The classification model works with the probability of a specific entity belonging to a particular category. For example, it can predict whether a photo contains a tiger.

The reinforcement learning model will be the last one on our generative AI training techniques list. It works following the principle of rewards and penalties the system gets according to a specific action it performs to the environment. The system’s goal is to achieve maximum return. Therefore, it generates the most optimal policy as an embodiment of a strategy for getting the most rewards. For example, such systems can become expert players and achieve the maximum possible reward by learning all the previous sequences that lead to losses or wins.

Continue reading to learn about the types of Generative AI, GenAI apps in different industries, benefits and drawbacks of the technology, and tips on integrating GenAI into your workflow: https://xbsoftware.com/blog/generative-ai/