What is generative AI, and how does it work?

Generative AI, a subset of artificial intelligence (AI), has witnessed remarkable advancements in recent years, transforming various industries and opening new frontiers for creativity and innovation. 

According to IBM, generative AI “refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.”

A popular example of generative AI is the chatbot “ChatGPT”, which has surpassed 1 million users in just 5 days of its launch.

Unlike traditional AI systems that are primarily focused on recognizing and classifying existing data, generative AI models aim to generate new data that is similar to the training data they have been exposed to.

How does generative AI work?

Generative AI works by learning from a large dataset of existing content and then using that knowledge to generate new, similar content. For example, a generative AI model trained on a dataset of images of cats could be used to generate new images of cats.

To generate fresh content, generative AI uses generative adversarial networks (GANs), a kind of deep learning. According to a Deep Learning research paper: “A GAN is a type of neural network architecture for generative modelling to create new plausible samples on demand. It involves automatically discovering and learning regularities or patterns in input data so that the model may be used to generate or output new examples from the original dataset.”

  A GAN consists of two neural networks: 

1. A generator (G) that creates new data and
2. A discriminator (D) evaluates the data.

Thus, in GAN modelling, both the generator and discriminator are trained to compete with each other. While the generator tries to fool and confuse the discriminator by creating more realistic data, the discriminator tries to distinguish genuine data from the fake data generated by G.

At the core of generative AI are advanced machine learning algorithms, particularly deep learning and neural networks. These algorithms enable the creation of models that can learn patterns and relationships within vast amounts of data, allowing them to generate content that exhibits similar characteristics.

Generative models work by learning the underlying probability distribution of the training data. Once the model has learned this distribution, it can generate new data samples that have similar statistical properties. The generated content may not be an exact replica of any specific data sample in the training set, but it captures the essence and style of the training data.

Thus, deep learning (DL) and machine learning (ML) are the core technologies used for building smart intelligence systems.

This can be easily understood by the below-mentioned figure:

Another widely used generative model is the Variational Autoencoder (VAE). VAEs learn to encode data into a lower-dimensional latent space and decode it back into the original data format. This latent space can then be sampled to generate new data samples that are similar to the training data.

What is the historical context of Generative AI?

The roots of generative AI can be traced back to the early days of artificial intelligence research and the exploration of computational creativity. While the field has seen significant advancements in recent years, its foundations were laid several decades ago. Let’s delve into the historical context of generative AI.

• Early AI and Expert Systems: In the 1950s and 1960s, the field of AI emerged, focusing on problem-solving and rule-based systems. Expert systems, developed in the 1970s and 1980s, were among the earliest attempts to encode human knowledge and generate intelligent outputs. These systems could provide rule-based responses or recommendations based on a set of predefined rules and patterns.
• Evolution of Machine Learning: The rise of machine learning in the 1980s and 1990s paved the way for generative AI. Researchers explored techniques such as Bayesian networks, hidden Markov models (HMMs), and decision trees for learning probabilistic models and generating data. These methods laid the groundwork for more advanced generative models.
• Markov Chain Models and Language Generation: Markov chain models, developed in the mid-20th century, played a significant role in generative AI. Markov models, based on stochastic processes, allow the generation of sequences based on transition probabilities between states. Language generation using Markov models became popular, generating text that resembled the patterns observed in the training data.
• Deep Learning and Neural Networks: The resurgence of neural networks and the development of deep learning algorithms in the late 2000s revolutionized generative AI. Deep neural networks, with their ability to learn hierarchical representations from data, proved instrumental in generating complex and high-dimensional content. The advancements in computational power and the availability of large datasets contributed to the success of deep generative models.
• Generative Adversarial Networks (GANs): In 2014, Ian Goodfellow introduced Generative Adversarial Networks (GANs), a groundbreaking approach to generative AI. GANs introduced a game-theoretic framework where a generator network competes against a discriminator network. The generator learns to generate realistic samples, while the discriminator learns to distinguish between real and generated data. This adversarial training process leads to the generation of increasingly realistic content.
• Variational Autoencoders (VAEs): Variational Autoencoders (VAEs) emerged as another significant development in generative AI. VAEs combine ideas from deep learning and variational inference to learn a latent representation of data. By sampling from this latent space, VAEs generate new data samples that resemble the training data while allowing controlled exploration of the latent space.
• Recent Advancements and Applications: In recent years, generative AI has witnessed rapid progress. Notable advancements include OpenAI’s GPT (Generative Pre-trained Transformer) models, capable of generating coherent and contextually relevant text, and NVIDIA’s StyleGAN, enabling high-quality image synthesis with fine-grained control over generated images. These developments have propelled generative AI into domains such as creative arts, content creation, healthcare, finance, and more.

In conclusion, Generative AI is an expanding field that holds immense potential across diverse industries, offering a plethora of exciting and innovative applications. While already being employed in various sectors, we can anticipate even more remarkable advancements in the future. Nevertheless, the wide adoption of generative AI is accompanied by significant challenges, including data prerequisites, the need to mitigate bias, and ensuring robust security measures. Addressing these challenges is crucial to ensure the responsible and widespread utilization of generative AI technology.