Generative AI, a rapidly evolving subset of artificial intelligence, transforms how we create and interact with digital content.
This technology leverages machine learning models, particularly unsupervised and semi-supervised algorithms, to generate new content based on existing data. Generative AI understands the underlying patterns in the input data, enabling it to produce novel outputs that resemble the original data. This process is facilitated by neural networks, which can create a wide range of content, from images and videos to text and audio.
Generative Adversarial Networks (GANs) and transformer-based models such as Generative Pre-Trained (GPT) language models are two of the most widely used generative AI models. GANs can create visual and multimedia artifacts from imagery and textual input data, while transformer-based models can generate textual content, leveraging information from the internet.
Generative AI has a broad spectrum of applications, including (but not limited to) creating AI-generated art, enhancing data augmentation in computer vision, generating synthetic data for training other machine learning models, and even in natural language processing, where it powers LLMs (Large Language Models) such as GPT.
Healthcare solutions
Generative AI models can be used in healthcare to generate new drug molecules, predict disease progression, and more. These models can learn from existing medical data and generate new insights, speeding up the research and treatment process.
Research in this area is focused on improving the accuracy and relevance of the generated medical insights. For example, researchers are working on models to predict disease progression more accurately and generate more effective drug molecules. Companies like DeepMind are using AI to assist in healthcare research. DeepMind's tool, AlphaFold, uses AI to predict the 3D structure of proteins, which is crucial for understanding diseases and developing new drugs.