Generative AI is a type of AI (such as ChatGPT) that can generate new forms of creative content, such as audio, code, images, text, simulations and videos.
Generative AI is powered by foundation models (large AI models) that can multi-task and perform out-of-the-box tasks, including summarization, Q&A, classification, and more.
Generative AI works by using an ML model to learn the patterns and relationships in a dataset of human-created content. It then uses the learned patterns to generate new content. To work on AWS Generative AI, click the link AWS AI.
Generative AI vs. Traditional AI
Essentially, the relationship between artificial intelligence and generative AI is hierarchical.
AI refers to the development of computer systems that can perform tasks that previously required human intelligence. Typically, such tasks involve perception, logical reasoning, decision-making, and natural language understanding (NLU).
Machine learning is a subset of AI that focuses on discriminative tasks. It involves the development of algorithms that enable computers to make predictions or decisions based on data without being explicitly programmed how to do so.
Generative AI is a subset of machine learning (ML) that focuses on creating new data samples that resemble real-world data.
Types of GI:
Transformer-based models: For text generation, transformer-based models such as GPT-3 and GPT-4 have been instrumental. They use an architecture that allows them to consider the entire context of the input text, enabling them to generate highly coherent and contextually appropriate text.
Generative adversarial networks (GANs): GANs consist of two parts, a generator and a discriminator. The generator creates new data instances, while the discriminator evaluates these instances for authenticity. Essentially, the two parts engage in a game, with the generator striving to create data that the discriminator can’t distinguish from the real data, and the discriminator trying to get better at spotting the fake data.
Variational autoencoders (VAEs): VAEs represent another type of generative model that leverages the principles of statistical inference. They work by encoding input data into a latent space (a compressed representation of the data) and then decoding this latent representation to generate new data. The introduction of a randomness factor in the encoding process allows VAEs to generate diverse yet similar data instances.
Examples and Usecases of AI:
Arts and entertainment: Generative AI has been used to create unique pieces of art, compose music, and even generate scripts for movies. Specialised platforms have been created that use generative algorithms to turn user-submitted images into art pieces in the style of famous painters. Other platforms use convolutional neural networks to generate dream-like, highly intricate images. Deep learning models can generate musical compositions with multiple instruments, spanning a wide range of styles and genres. And with the proper prompts, generative AI can be used to generate films scripts, novels, poems, and virtually any kind of literature imaginable.
Technology and communications: In the realm of technology and communication, generative AI is used to produce human-like text responses, making the chatbot more engaging and capable of maintaining more natural and extended conversations. It has also been used to create more interactive and engaging virtual assistants. The model’s ability to generate human-like text makes these virtual assistants much more sophisticated and helpful than previous generations of virtual assistant technology.
Design and architecture: Generative AI is being used to generate design options and ideas to assist graphic designers in creating unique designs in less time. Generative AI has also been used by architects to generate unique and efficient floor plans based on relevant training data.
Science and medicine: In life sciences, generative AI is being used to design novel drug candidates, cutting the discovery phases to a matter of days instead of years. For medical imaging, GANs are now being used to generate synthetic brain MRI images for training AI.
E-commerce: Companies are using GANs to create hyper-realistic 3D models for advertising. These AI-generated models can be customised to fit the desired demographic and aesthetic. Generative algorithms are also being used to produce personalised marketing content, helping businesses communicate more effectively with their customers.
Conclusion
Generative AI is a fast-growing technology that holds promise for changing various industries and enhancing our lives in many ways. It can create fresh content, handle tasks automatically, tailor experiences, innovate products and services, and enhance our comprehension of the world.