What is Generative AI?
Generative AI refers to a branch of artificial intelligence that focuses on creating new content such as images, videos, text or audio rather than just performing tasks with existing data. Generative models are trained on large datasets to learn underlying patterns and relationships which allows them to generate new, realistic content that resembles the training data. Some common examples of generative AI include image generation with GANs, text generation with language models and music generation.
How do Generative Models work?
Generative AI models work by learning the underlying probability distribution of the training data. They use deep learning techniques like neural networks to model high dimensional, complex datasets. The goal of training is to learn statistical patterns and regularities in the data so it can generate new samples from that same distribution. For example, a generative text model trained on news articles would understand topics, writing style, grammar, word sequences etc. from millions of articles which enables it to generate new, never before seen articles that resemble real news content.
Key Generative Techniques
There are a few popular techniques used in generative models:
- Generative Adversarial Networks (GANs): GANs work by training two neural networks, a generator and a discriminator against each other. The generator tries to produce synthetic samples that resemble the real training data while the discriminator tries to distinguish between real and generated samples. Through this adversarial training process, the generator learns to produce highly realistic outputs.
- Variational Autoencoders (VAEs): VAEs are a type of deep generative model that learns the hidden representations or latent space of the training data. It compresses the inputs into a lower dimensional latent space from which it can reconstruct the inputs. New synthetic samples can be generated by sampling random points from the latent space.
- Recurrent Neural Networks (RNNs): RNNs are a class of neural networks that excel at modeling sequential data like text, audio or time series. They incorporate a notion of memory via hidden states and are widely used for text generation by learning language models from large text corpora. Transformers, a recent evolution of RNNs, have achieved state-of-the-art performance in language tasks.
- Flow-based models: These are a family of deep generative models based on the idea of invertible transformations. They learn the precise mappings between latent and data spaces allowing for efficient generation and inference. Normalizing flows are a popular flow-based approach used for density estimation and sampling of complex, high-dimensional distributions like images and audio.
Applications of Generative AI
Some real world applications of generative AI that are commercially available or being actively researched include:
Art/Design - Generative models have the potential to automate aspects of creative work like concept art, 3D modeling, music and video composition. Stable Diffusion and DALL-E are two prominent AI tools for image generation from text.
Healthcare - Medical image generation like MRI/CT scans from other modalities, simulation of disease progression, biomarker discovery from patient records, drug/vaccine design are areas under exploration.
Manufacturing - Generating synthetic 3D CAD models at scale for product design, simulation and validation. Optimizing engineering designs by searching generative design spaces.
Finance - Generation of text summaries from financial reports, simulation of conditions for risk analysis, predictive analytics from alt-data sources.
Education - Creation of personalized learning content including textbooks, questions for practise and assessments automatically tailored for each student. Intelligent tutoring assistants.
While generative AI has made substantial progress in narrow, well-defined domains with available data, broader, more flexible applications are still being developed and will require continued advances. Ethical application also requires careful oversight regarding bias, transparency, consent and appropriate use of sensitive personal information. However, generative models undeniably represent a powerful suite of techniques that have the potential to drive new levels of productivity, creativity and innovation across sectors if guided judiciously.
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