An Introduction to Generative AI
- Generative AI involves training models to generate novel outputs from learned patterns in input data. In other words, it’s all about creating new data from the patterns learned during the training process.
- The main idea of generative AI is not to predict outcomes or classify data, but to learn the true data distribution of the training set to generate new data points with some variations. These models are commonly used in unsupervised learning tasks to create complex, structured outputs.
- Generative AI can generate data in several modalities, including text, speech, image, and video, and we will see these in more detail. In the image below [(source)], we can see the key ingredients needed for a strong Generative AI model:
Popular Techniques in Generative AI
- Generative Adversarial Networks (GANs): GANs consist of two models - a generator and a discriminator - that are trained together. The generator learns to produce data that resemble the real data, while the discriminator learns to distinguish real data from the ones generated. This interplay leads the generator to produce increasingly realistic data. GANs have been used to generate realistic images, music, speech, and text.
- Variational Autoencoders (VAEs): VAEs are another popular method for generative tasks. They work by encoding input data into a latent space, and then decoding from this space to generate new data. The model is trained to ensure that the generated data is as close as possible to the original data. VAEs have been used for tasks such as generating new molecules for drug discovery.
- Transformer models: Transformer models, particularly in the field of Natural Language Processing (NLP), have been leveraged for generative tasks. These models learn to generate text that is similar to the training data. Examples of this include OpenAI’s GPT-3, which can generate impressively human-like text.
- Diffusion model: Diffusion models fundamentally work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process.
- Transformer-Decoders: We would be remiss if we were to forget mentioning how GPT has changed the game with Generative models such as ChatGPT. In these models, the input text is processed in a way that each token (word or piece of a word) can see all the previous tokens in the input, but not the ones ahead. It’s generating the next token based on the previous ones, which can be seen as a form of “decoding”.
Applications of Generative AI
- Generative AI has a wide array of applications across various domains. Some notable applications include:
- Art and Design: Generative AI has been used to create original pieces of art and design. For example, GANs have been used to generate unique and realistic-looking images, from faces of non-existent people to artwork that has been auctioned for high prices.
- Text Generation: In NLP, generative models have been used to create human-like text. This can be used for tasks like automated storytelling, dialogue generation for chatbots, or even writing code.
- Drug Discovery: In the field of drug discovery, generative models like VAEs have been used to generate new molecular structures that could potentially be used as new drugs.
- Synthetic Data Generation: Generative AI can be used to create synthetic datasets that mimic real-world data, which can be useful in scenarios where data collection is costly, time-consuming, or privacy-invading.
- Generative AI is a rapidly advancing field that holds great promise for a variety of applications. As the techniques and technologies continue to improve, we can expect to see even more innovative and creative uses of these models to generate novel and high-quality outputs. However, as with all powerful tools, it’s also important to consider the ethical implications and potential misuse of such technology, and to develop appropriate safeguards and regulations to guide its use.
Generative Models in Text Generation
- Natural Language Processing (NLP), a subset of AI that focuses on interactions between computers and humans through language, makes extensive use of generative models, particularly for tasks such as machine translation, text summarization, and dialogue systems.
- Recurrent Neural Networks (RNNs): Traditional models for text generation often used RNNs and their variants (LSTM, GRU) due to their ability to handle sequential data. RNNs learn to predict the next word in a sequence based on the context provided by the preceding words.
- Transformers: Transformers, introduced in the seminal paper “Attention is All You Need”, have now become a standard for many NLP tasks. For generative tasks, Transformer models, like GPT-3, generate text by learning to predict the next word in a sequence in an autoregressive manner, with the advantage of attending to all previous words in the sequence simultaneously thanks to the self-attention mechanism.
- Variational Autoencoders (VAEs) for Text: VAEs have been adapted to handle discrete data like text, and learn a continuous latent representation of the input text data. These models can generate diverse and relevant text but face challenges with training stability and latent space usage.
Generative Models in Image Generation
- Generative AI has also seen remarkable progress in image generation tasks, with the ability to generate strikingly realistic images.
- Generative Adversarial Networks (GANs): GANs have been highly successful for image generation tasks. GANs work by training two networks concurrently: a Generator network that generates images from random noise, and a Discriminator network that tries to distinguish these generated images from real ones. This adversarial process results in the Generator producing increasingly realistic images. Examples of GAN variants include DCGAN, StyleGAN, CycleGAN, etc.
- Variational Autoencoders (VAEs) for Images: VAEs are also used for image generation. Similar to their use in text generation, they learn a continuous latent representation of the input image data and can generate new images by sampling from this latent space. They are known to produce images with high diversity but lower quality than GANs.
Technicalities and Challenges
- While generative models have shown impressive results, they are not without their challenges. Training these models involves optimizing complex objective functions. For example, GAN training involves a min-max game between the Generator and the Discriminator, which can lead to issues such as mode collapse, where the Generator fails to generate diverse images.
- Generative models for text also need to handle the discrete nature of language, which makes the optimization landscape non-differentiable. Transformer models, while effective, are also known for their substantial computational requirements.
- Generative AI has shown impressive results in both text and image generation. As advancements continue to be made, these models will become more efficient, robust, and capable of generating even more realistic and diverse outputs. As with all AI technologies, it’s important to use these powerful tools responsibly, considering the ethical implications and potential misuse.