In recent years, artificial intelligence (AI) has revolutionised a wide range of sectors. Technology breakthroughs have made it possible for machines to carry out difficult activities that people previously did. Generative AI is one of the most fascinating subfields of AI.
The storm of generative AI systems such as ChatGPT, DALL-E, and Google’s Bard is transforming entire industries. According to the MarketAndMarket report, the generative AI market is expected to grow from $11.3 billion in 2023 to $51.8 billion in 2028.
This type of AI has the potential to revolutionize how we create and consume content. In this article, we’ll explore what generative AI is, how it works, the types of generative AI models, and its impact in the real world.
What is Generative AI?
Generative AI is a machine learning model that creates new data from scratch. Unlike traditional AI models that use existing data to make predictions, generative models can create entirely new content that has never existed before. Generative models work by learning the underlying patterns and structures within a dataset. Once it learns these patterns, it can then use them to create new data that is similar in style and content to the original dataset.
How Generative AI Works
Generative AI models work by utilizing a training dataset to create a model. The model then generates new data that is similar to the original dataset. The key to generative AI is the ability to generate new data that hasn’t been seen before, while also maintaining the structure and patterns of the original dataset.
To create these new data, generative models use a range of techniques. One popular technique is Variational Autoencoders (VAEs). VAEs work by encoding the input data into a lower-dimensional space, where it’s easier to model the underlying patterns. The model can then generate new data by sampling from the distribution learned by the model.
Another popular technique is Generative Adversarial Networks (GANs). GANs are made up of two neural networks that collaborate in order to generate fresh data. The first network generates samples, while the second network evaluates those samples and provides feedback to the generator. The feedback helps the generator to improve and generate more realistic data.
Autoregressive models are another type of generative model. These models generate new data one element at a time, using a probability distribution to determine the most likely value for each element given the values of the preceding elements.
Types of Generative AI Models
Now that you know how generative AI works, let’s dive into the several types of generative AI models. These include:
Variational Autoencoders (VAEs): VAEs are a generative model that encodes the input data into a lower-dimensional space. The model can then generate new data by sampling from the distribution learned by the model.
Generative Adversarial Networks (GANs): GANs are a generative model comprising two neural networks. One network generates samples, while the other network evaluates those samples and provides feedback to the generator.
Autoregressive Models: Autoregressive models generate new data one element at a time, using a probability distribution to determine the most likely value for each element given the values of the preceding elements.
Flow-Based Models: Flow-based models are generative models that use invertible functions to transform a simple distribution into a complex distribution that can generate new data.
Impact of Generative AI in the Real World
Generative AI has shown the capability to transform the face of various industries. One of the most significant areas where generative AI is impacting is art and music. Generative models can create unique pieces of art and music that have never existed before. For example, AIVA, an AI-based composer, creates unique pieces of music using a deep neural network.
Generative models are also making an impact in the advertising and marketing industry. Businesses can use generative models to create unique content for their websites, social media accounts, and other marketing materials. This allows businesses to automate their content creation process, saving time and effort.
Conclusion
Business executives need to create a convincing generative AI strategy to get a competitive edge. While enhancing the quality, effectiveness, and accessibility of digital products, generative AI will shorten the time to market. Executives in software engineering should seize this opportunity to adopt generative AI and take advantage of its competitive advantages.
Originally published at https://www.impactqa.com on April 19, 2023.
