In our study, we demonstrate that an artificial intelligence (AI) model can learn the language of biology in order to generate proteins in a controllable fashion. Our AI system, ProGen, is a high capacity language model trained on the largest protein database available (~280 million samples).

Our graph-based trainable retriever-reader framework retrieves evidence paragraphs from Wikipedia to answer open-domain questions. We show state-of-the-art performance on HotpotQA, SQuAD Open, and Natural Questions Open without any architectural changes.

Many NLP applications today deploy state-of-the-art deep neural networks that are essentially black-boxes. One of the goals of Explainable AI (XAI) is to have AI models reveal why and how they make their predictions so that these predictions are interpretable by a human. But work in this direction has been

Large-scale language models show promising text generation capabilities, but users cannot control their generated content, style or train them for multiple supervised language generation tasks.

Commonsense reasoning that draws upon world knowledge derived from spatial and temporal relations, laws of physics, causes and effects, and social conventions is a feature of human intelligence.

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.

When humans generate novel neural architectures, they go through a surprisingly large amount of trial and error. This holds true almost regardless of how much experience in deep learning that person might have!

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.

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.

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.

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.

Over the past few years, neural networks have driven rapid improvements in accuracy and quality for natural language tasks like text classification and question answering.

A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce.

There are times when word vectors are initialized to lists of random numbers before a model is trained for a specific task, but it is also quite common to initialize the word vectors of a model with those obtained by running methods like word2vec, GloVe, or FastText.

The last few decades have witnessed a fundamental change in the challenge of taking in new information. The bottleneck is no longer access to information; now it’s our ability to keep up. We all have to read more and more to keep up-to-date with our jobs, the news, and social media.

The rise of Machine Learning, Deep Learning, and Artificial Intelligence more generally has been undeniable, and it has already had a massive impact on the field of computer science.

Automatically generating captions for images has emerged as a prominent interdisciplinary research problem in both academia and industry. It can aid visually impaired users, and make it easy for users to organize and navigate through large amounts of typically unstructured visual data.

LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular.

Humans learn natural languages, such as English, starting from basic grammar to complex semantics in a single brain. How do we build such a single model to handle a variety of natural language processing (NLP) tasks in computers?

We introduce the Dynamic Coattention Network, a state of the art neural network designed to automatically answer questions about documents.

In deep learning, there are two very different ways to process input (like an image or a document). Typically, images are processed all at once, with the same kind of computation happening for every part of the image simultaneously.

Imagine you were a young child and wanted to ask about something. Being so young (and assuming you are not exceedingly precocious), how would you describe a new object, the name of which you have yet to learn? The intuitive answer: point to it!

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.

Neural Machine Translation (NMT) systems, introduced only in 2013, have achieved state of the art results in many MT tasks. MetaMind’s submissions to WMT ’16 seek to push the state of the art in one such task, English→German newsdomain translation.

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks.

Today, we published new state of the art results on a variety of natural language processing (NLP) tasks. Our model, which we call the Dynamic Memory Network (DMN), combines two lines of recent work on memory and attention mechanisms in deep learning.