Unearth the future of conversational AI and how Raffle places itself in AI history.

Read more on how open source increases the ease of using deep learning and how Raffle has built on this technology.

Find out what the future of conversational AI has to offer and how Raffle plans to be a part of technological innovation.

This is the first of a series of four posts about the technology behind Raffle products, the future of AI, and NLP.

Discover Raffle's deep learning framework and models to create our unique AI algorithms that are the core of our products.

The Attend, Copy, Parse architecture is a deep neural network model trained on end-to-end data, that bypasses the need for word-level labels

Learn more about recurrent relational networks to train AI to mimic human behaviors.

Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning.

Learn more about hash embeddings, an efficient method for representing words in a continuous vector form.

Dive into the conversation around unsupervised learning and its impact within Raffle to understand our unique structure.

We propose a Neural Machine Translation model with a hierarchical char2word encoder that takes individual characters as input and output.

Deep generative models have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning.

We present an autoencoder that leverages learned representations to better measure similarities in data space.