The current digital landscape is evolving rapidly, requiring businesses to seek innovative ways to stand out and engage with their customers. One technology that has piqued substantial interest among these businesses is Generative Artificial Intelligence (AI). This is because of its extraordinary ability to create new and original content in a blink of an eye – be it compelling text, music that mimics Mozart’s style, images that resemble masterpieces by accomplished artists, or even entire virtual environments. But what happens behind the curtain?
In this article, we aim to decode how generative AI works, the inner mechanics, and various types of generative AI models. We will also shed some light on how businesses can harness the potential of generative AI for organizational growth and customer satisfaction.
Generative AI is a particular type of artificial intelligence that creates unique and compelling content in the form of text, image, video, or audio by learning from existing data patterns. It is different from traditional AI systems in a way that it does not rely on pre-defined rules or structures and generates new and original outputs. It produces coherent and aesthetically pleasing content of various types by leveraging advanced deep-learning models that mimic human creativity.
We will not go further deeper into the concept, benefits, or applications of generative AI. If you want to know everything about generative AI in detail, we have covered it in a distinct all-inclusive article. As for now, let’s bring back the focus on our main question and dive deep into the workings of generative AI.
At the heart of generative AI lies machine learning, which in turn is based on neural network architecture. Neural networks consist of interconnected layers of artificial neurons and are designed to mimic the working of the human brain. These networks can be trained to perform a diverse range of tasks, which also include generative tasks.
Generative AI models, with these neural networks at their base, are trained on large datasets, which can include images, text, audio, or videos. These models analyze the intricate relationship within the data and sample a probability distribution they have learned and generate new content that is similar to the input examples. The probability of generating accurate output is maximized by continuously adjusting the parameters of these models. This ability to learn and mimic patterns provides generative AI with its creative edge.
To put it in simpler words, consider the following example. Consider a generative AI model trained on a dataset of handwritten digits. New and realistic-looking handwritten digits can be created using this model by sampling from the learned distribution and refining the output through the process of “inference”.
There are several prominent types of generative AI models, each with its pros and cons. We will discuss some of the most widely recognized ones below.
Generative Adversarial Networks or GANs as a type of neural network architecture have revolutionized generative AI. It consists of two primary components: the generator and the discriminator.
The generator creates new and original output, such as images, based on random input or a given condition. It trains on an existing dataset and learns to generate output that resembles real examples. Initially, the output may be random pixels, but as the training progresses, the generator produces a more realistic and coherent output.
The discriminator, on the other hand, acts as a critic. Taking input from both the generator and real examples from the training dataset, it attempts to differentiate between the real and generated content. It progressively learns to classify whether an input is real or fake.
The training of GANs as a whole involves a back-and-forth interplay between the generator and the discriminator. With consistent progress in training, the generator learns to produce outputs that are increasingly more difficult for the discriminator to classify, while the discriminator becomes more adept at distinguishing between real and generated examples. Essentially, both components improve their performance as the training progresses.
This iterative process carries on until the generator can consistently fool the discriminator by generating outputs that are indistinguishable from the real example. This way, both the components work as each other’s adversaries and hence the use of the term “adversarial” in the name. Thus, GANs generate novel and high-quality content by learning to capture the intrinsic patterns and details in the training data.
The Training Process of GANs
GANs training process involves the following steps:
Potential GAN Applications
One of the real-life examples based on GANs is NVIDIA’s StyleGAN. It is utilized in gaming, fashion, and art for generating realistic human faces. It exemplifies GAN’s potential to bridge real and synthetic imagery by enhancing gaming experiences, creating virtual models, and fueling artistic exploration.
Transformer-based models are another prominent type of neural network architecture with a self-attention mechanism at its base. They are particularly well-suited to perform tasks that involve sequential data. This includes tasks like natural language processing (NLP) and machine translation.
Transformer-based models learn the relationship between different parts of a sequence by using the attention mechanism. This enables them to capture long-range dependencies, essential for many NLP tasks. For instance, on receiving an input the model assigns weights to various parts of the input sequence in parallel. Once it identifies their relationship, it generates output particular to the specific input.
Training Process of Transformer-Based Models
The training process of transformer-based models involves the following steps:
Benefits of Transformer-Based Models:
Challenges of Transformer-Based Models:
Potential Application of Transformer-Based Models
Open AI’s GPT-3 is one of the largest language models based on transformers. It is capable of generating original text, translating languages, generating various forms of creative content, and answering your questions in an informative way.
Variational autoencoders or VAEs is a generative AI model well-known for its ability to offer variation in the data in a specific direction rather than just generating new content that resembles that training data.
VAEs are neural network architectures comprising an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, called the “latent space”, while the decoder reconstructs it and generates a new output.
VAEs are different from traditional autoencoders in a way that they use variational inference, a statistical method to approximate complex probability distributions. It enables them to capture the uncertainty and variability in data rather than just reconstructing the input data.
Training Process of VAEs
The VAEs training process involves the following steps:
Benefits of VAEs
Challenges of VAEs
Potential Applications of VAEs
Google’s DeepDream is a prominent example of a VAE. It is primarily used to generate psychedelic images. It was trained on a dataset of images edited to look psychedelic to produce similar effects.
Alongside the above-mentioned models, there are other popular generative AI models that are pushing the boundaries of AI. Here are a few of them:
Diffusion models work by iteratively adding noise to a base sample in the dataset and subsequently removing the noise, thus creating high-quality synthetic output. Dall-E, Stable Diffusion, Midjourney, and Google’s Imagen are popular applications based on diffusion models.
As the name suggests, multimodal models can take input data in multiple formats, including text, audio, and images. They create sophisticated outputs by combining different modalities. Dall-E 2 and OpenAI’s GPT-4 are popular examples of multimodal models.
Generative AI is transforming the way businesses optimize their organizational processes and approach customer engagement, content creation, and design. It helps individuals unlock their creativity and organizations deliver unique experiences to end consumers. While it is crucial to navigate the ethical considerations and biases, the potential benefits make generative AI an exciting frontier for businesses and consumers alike. Embracing this technology can be a catalyst for innovation, differentiation, and success in the digital age.
If you are curious about how generative AI can help you enhance your organizational processes and deliver optimal customer experiences, connect with our generative AI experts today!
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