3. Video: How Generative AI Works

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Generative AI evolved as the digital world expanded. In this topic, you’ll discover the advances in technology that enabled generative AI, as well as gain some understanding of key AI techniques and models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs).

identify the three types of generative AI features and their role in generative AI disruption

[Video description begins] Topic title: How Generative AI Works. [Video description ends]

The origin of generative AI can be traced back to a significant shift in the field of artificial intelligence. Historically, AI was used for tasks associated with processing and analysis, such as data validation or data input. This pattern changed with the emergence of generative models. Instead, of merely processing and analyzing data, these models were trained how to create. This shift made a whole new world of innovation and solutions possible, turning AI from a simple tool into a creator. Let's trace this journey back to its roots. Generative AI had relatively humble beginnings, starting with the development of generative adversarial networks or GANs in 2014. In these early stages, generative AI's use cases were quite limited, often deemed experimental.

Most tasks involved generating images based on textual descriptions, essentially producing tangible visual outputs from abstract descriptive inputs. However, then computers improved and got more potent computing power that could handle larger and more varied sets of information. And as digital technologies improved, so did generative AI's potential along with them. A turning point in the development of generative AI was when researchers observed that GANs, when trained on suitably large and diverse datasets, could produce innovative, realistic, and novel outputs. This led to the development of more sophisticated models such as Variational AutoEncoders or VAEs and Large Language Models (LLMs). Let's now explore the structure and requirements of GANs, VAEs, and LLMs in more detail.

To understand how GANs work, you first need to understand the concept of neural networks which GANs employ. A neural network is a computing model resembling the way that the human brain works. Neural networks consist of interconnected layers of nodes similar to brain neurons that process information. Each node takes in data, performs calculations on it, and passes the results to the next layer. This structure allows the network to learn patterns and make decisions. GANs consist of two neural networks, a generator, and a discriminator. The generator produces new outputs such as images, while the discriminator evaluates these outputs' authenticity. This opposing or adversarial relationship between the two networks generates high quality, realistic results. VAEs follow a slightly different approach.

They encode input data to compress it into a smaller format referred to as a latent space. This encoded data is then decoded, essentially reversing the encoding process to generate new outputs. VAEs are able to maintain the original dataset's structure and diversity in what they output, making them ideal for tasks like image generation where the generated outputs resemble variations of whatever data the model was trained on. Moving to LLMs, which are designed to understand language and generate human-like text based on patterns, they learn from vast amounts of text data. LLMs are instrumental in giving models like GPT-4 their ability to predict or generate text sequences. Based on a transformer model, these LLMs can understand and retain context over long passages.

This is in turn incredibly useful for natural language processing or NLP tasks whereby a transformer model uses self-attention mechanisms to better understand the context of words in a sentence. That is, the model is able to focus on individual words in the input sequence and can tell from those words and the data it was trained on how to compile relevant output. These generative models differ from traditional AI models in significant ways. First, generative AI models focus on generating new data, not just analyzing existing data.

Second, due to their intricate architectures, generative AI models often require larger datasets for training, typically in the range of thousands to millions of data points. Lastly, generative AI models need substantial computational power as they perform complex tasks like producing new outputs that are intended to be authentic. The shift to GANs, VAEs, and LLMs involves moving from direct rule-based learning to an approach that feels more like coaching and imaginative and creative assistant. And while this brings its own set of challenges, including larger datasets and more computing power, there may be many rewards to unlocking generative AI's creative potential.