Beyond the “basics”, regulation needs to protect populations at large from AI-related safety risks, of which there are many. Most will be human-related risks. Malicious actors can use Generative AI to spread misinformation or create deep fakes.
Key Take away. Generative AI, or generative artificial intelligence, is a form of machine learning that is able to produce text, video, images, and other types of content. Chat GPT, DALL-E, and Bard are examples of generative AI applications that produce text or images based on user-given prompts or dialogue.
Generative Adversarial Networks:
Generative Adversarial Networks are good at generating random images. As an example, a GAN which was trained on images of cats can generate random images of a cat having two eyes, two ears, whiskers. But the color pattern on the cat could be very random.
A generative adversarial network (GAN) has two parts: The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator. The discriminator learns to distinguish the generator’s fake data from real data.
Autoregressive Models:
Autoregressive models predict future values based on past values. They are widely used in technical analysis to forecast future security prices. Autoregressive models implicitly assume that the future will resemble the past.
Autoregressive is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems.
Recurrent Neural Networks:
The Recurrent Neural Network will standardize the different activation functions and weights and biases so that each hidden layer has the same parameters. Then, instead of creating multiple hidden layers, it will create one and loop over it as many times as required.
Recurrent neural networks are a type of neural network that processes sequential data, such as natural language sentences or time-series data. They can be used for generative tasks by predicting the next element in the sequence given the previous elements. However, RNNs are limited in generating long sequences due to the vanishing gradient problem. More advanced variants of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have been developed to address this limitation.
Reinforcement Learning for Generative Tasks:
Reinforcement learning, serving as a competitive option to inject new training signals by creating new objectives that exploit novel signals, has demonstrated its power and flexibility to incorporate human inductive bias from multiple angles, such as adversarial learning, hand-designed rules and learned reward model to…
These are just some of the types of generative AI models, and there is ongoing research and development in this field, leading to the emergence of new and more advanced generative models over time.
Transformer-based Models:
transformer model is a neural network architecture that can automatically transform one type of input into another type of output. The term was coined in a 2017 Google paper that found a way to train a neural network for translating English to French with more accuracy and a quarter of the training time of other neural networks.
The technique proved more generalized than the authors realized, and transformers have found use in generating text, images and robot instructions. It can also model relationships between different modes of data, called multimodal AI, for transforming natural language instructions into images or robot instructions.
Variational Autoencoders:
A variational autoencoder (VAE) is a deep learning model that can generate new data samples. It comprises two parts: an encoder network and a decoder network.
Variational autoencoders are generative models that learn to encode data into a latent space and then decode it back to reconstruct the original data. They learn probabilistic representations of the input data, allowing them to generate new samples from the learned distribution. VAEs are commonly used in image generation tasks and have also been applied to text and audio generation.