Generative AI refers to a class of [[artificial intelligence (AI)]] models that are capable of generating new content, such as images, text, audio, or other forms of data, particularly [[Big Data]]. These models learn patterns, structures, and relationships in the training data and can then use this knowledge to generate original content that resembles the input data. There are several types of generative AI models and techniques, but some of the most prominent ones include: 1. **Generative Adversarial Networks (GANs)**: GANs consist of two neural networks, a generator and a discriminator, that are trained together in a process called adversarial training. The generator creates fake samples while the discriminator tries to distinguish between real and fake samples. Through this process, the generator learns to produce more realistic outputs. 2. **Variational Autoencoders (VAEs)**: VAEs are a type of autoencoder that learns to generate new data by modeling the underlying probability distribution of the input data. VAEs have an encoder and a decoder network, which work together to create a compressed representation of the input data and then generate new samples by sampling from this compressed space. 3. **Autoregressive models**: Autoregressive models generate new content by predicting the next element in a sequence given the previous elements. This process is repeated to create an entire output sequence. Prominent examples include the GPT (Generative Pre-trained Transformer) series, such as [[ChatGPT]] and GPT-4, and RNNs (Recurrent Neural Networks). 4. **Transformer models**: Transformer models, like BERT (Bidirectional Encoder Representations from Transformers) and GPT, have become popular due to their ability to handle large-scale language modeling tasks with [[large language models (LLM)]]s. These models use self-attention mechanisms to capture long-range dependencies in the input data and can generate high-quality text. ## Applications of generative AI The potential uses of generative AI models span a wide range of domains, including: - **Art and design**: Generating new images, artwork, or design elements. - **Text generation**: Writing articles, creating dialogue for chatbots, or generating code. - **Music and audio**: Composing new music or generating realistic speech. - **Drug discovery**: Generating new molecular structures for potential pharmaceuticals. - **Video and animation**: Generating realistic video frames or creating animations. Generative AI has shown tremendous potential in various fields but also poses challenges, such as ethical considerations around content creation and intellectual property, as well as the potential for generating misleading or malicious content and proliferating [disinformation](https://doctorparadox.net/dictionaries/disinformation-dictionary/). Nonetheless, these models continue to advance and are likely to play an increasingly significant role in numerous industries. ## Generative AI tools ### Imagery * [[Dall-E 2]] -- see also: [[Bing Image Creator]] * [[Midjourney]] * Stable Diffusion