AI can be used in many different ways to solve problems. There are many different kinds of AI, each with its own strengths and weaknesses. Generative Artificial Intelligence is a type of artificial intelligence that works by creating things instead of analyzing data. It uses algorithms to create new things like images, text, or audio. These programs often have input parameters that let the user control how much variation to expect from the final results. Typically, generative AI uses some sort of reward system to help it learn which outputs are desirable and which are not. This type of AI is different from other types because it focuses on creating something rather than solving a specific problem. Let’s take a look at some examples of generative AI used in real-world applications.
What is Generative AI Used for?
One of the most common applications of generative AI is in the creation of art. Artists have experimented with different ways to use machine learning and AI to generate new art for decades. This type of AI is increasingly used in collaborative art projects between artists and AI programs. A creative experiment using a generative AI program might look like this: The artist and the AI both create an input. The artist creates a painting, and the AI creates a piece of music. The AI analyzes the paintings and the music to create new variations of the inputs. The artist and AI then select the output that they like best from each set of variations and use those to create a new painting and new music. This type of collaborative art project can be easily explored with a program like AIVA or OpenAI’s Music Creation tool.
Games and Virtual Reality Applications of Generative AI
With advances in GPU development, AI developers are beginning to see real potential in generative AI for creating new virtual worlds and game environments. One of the most promising applications is machine learning applied to the creation of art in virtual reality (VR). This type of art is called real-time generative art, or R-GArtâ„¢. By using generative AI to create new and unique virtual worlds, VR developers can create an endless library of unique and immersive game environments. This can also be applied to create virtual human characters that are entirely unique and different each time. This could be helpful in both VR games and in VR training simulations.
Text and Language Application of Generative AI
Text and language applications of generative AI are likely to be more useful in business and industry than in creative applications. However, creative writing applications that use AI may be on the horizon. In fact, many major companies like Google, Microsoft, IBM, and Amazon have been investing in language and text applications of AI for years. There are many applications of generative AI in the field of natural language processing (NLP). One example is the training of a program to be able to read and understand financial markets and financial news. Finite-state machines and Markov chains are two types of AI used in financial news AI systems.
Conclusion
When we look at the current state of the generative AI industry, we see that it is still a long way from reaching its potential. While there are many exciting applications of this technology, each type of software tends to have its own unique limitations and weaknesses. It is likely that it will take a number of years for developers to create AI programs that are as diverse, creative, and artistic as human designers.