AI Research

Imagine having your own personal digital assistant. Not merely the command-oriented smart speakers of today, but rather a more sophisticated assistant that knows your behaviors and can anticipate your next inquiry through data-driven…

This summer, Salesforce Research announced our inaugural deep learning research grant for university researchers and faculty, non-profit organizations, and NGOs. Our goal is to identify and support diverse individuals with innovative ideas to join us in shaping the future of AI.

It has been empirically observed that different local optima, obtained from training deep neural networks don't generalize in the same way for the unseen data sets, even if they achieve the same training loss.

Deep learning has significantly improved state-of-the-art performance for natural language processing tasks like machine translation, summarization, question answering, and text classification.

In the same way that human decisions can be influenced by cognitive biases, decisions made by artificially intelligent systems can be vulnerable to algorithmic biases.

Deep reinforcement learning (deep RL) is a popular and successful family of methods for teaching computers tasks ranging from playing Go and Atari games to controlling industrial robots.

Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model.

Learning to answer open-ended questions about images, a task known as visual question answering (VQA), has received much attention over the last several years. VQA has been put forth as a benchmark for complete scene understanding and flexible reasoning, two fundamental goals of AI.

When humans generate novel neural architectures, they go through a surprisingly large amount of trial and error.

Most neural architectures for machine translation use an encoder-decoder model consisting of either convolutional or recurrent layers. The encoder layers map the input to a latent space and the decoder, in turn, uses this latent representation to map the inputs to the targets.