GenAI

Generative artificial intelligence (generative AI, GenAI, or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models often generate output in response to specific prompts. Generative AI systems learn the underlying patterns and structures of their training data, enabling them to create new data.
See:

LLMs

VLMs

GenAI for time series

GenAI for tabular data

GenAI for audio

AI for scientific discovery

Resources

Aplicationts per sector

Applications to science

See AI for scientific discovery

Generative modelling

Foundation models

A foundation model is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by AI/Self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks
https://en.wikipedia.org/wiki/Foundation_models
https://en.wikipedia.org/wiki/Large_language_model
Center for Research on Foundation Models (CRFM)
Foundation Models and the Future of Multi-Modal AI
Foundation models: 2022’s AI paradigm shift
Foundation Models: paradigm shift for AI or mere rebranding?
ChatGPT, LLMs, and Foundation models — a closer look into the hype and implications for startups

Benchmarks

https://artificialanalysis.ai/

Courses

Code

Books

#BOOK Generative Deep Learning (Foster 2023)
- #CODE https://github.com/davidADSP/GDL_code

Subtopics

Generative models for computer vision

See:

Generative AI for time series

See AI/Generative AI/GenAI for time series

Generative AI for tabular data

See GenAI for tabular data

Generative models for audio

See GenAI for audio

Generative AI for text and NLP

See LLMs

References

#PAPER On the Opportunities and Risks of Foundation Models (Bommasani 2021)
- A foundation model is any model that is trained on broad data at scale and can be adapted (e.g., fine-tuned) to a wide range of downstream tasks; current examples include BERT, GPT-3, and CLIP
- Foundation models are based on deep neural networks and self-supervised learning
- On a technical level, foundation models are enabled by transfer learning and scale
- The idea of transfer learning is to take the “knowledge” learned from one task (e.g., object recognition in images) and apply it to another task (e.g., activity recognition in videos).
- Within deep learning, pretraining is the dominant approach to transfer learning: a model is trained on a surrogate task (often just as a means to an end) and then adapted to the downstream task of interest via fine-tuning
- Transfer learning is what makes foundation models possible, but scale is what makes them powerful. Scale required three ingredients: improvements in computer hardware, the development of the Transformer model architecture that leverages the parallelism of the hardware to train much more expressive models than before and the availability of much more training data
#PAPER #REVIEW ChatGPT is not all you need. A State of the Art Review of large Generative AI models (Gozalo-Brizuela 2023)

Resources

Aplicationts per sector

Applications to science

Generative modelling

Foundation models

Benchmarks

Courses

Code

Books

Subtopics

Variational Autoencoders

GANs

Normalizing flows

Diffusion models

GFlow nets

Generative models for computer vision

Generative AI for time series

Generative AI for tabular data

Generative models for audio

Generative AI for text and NLP

References