Prototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

In this work, we focus on the opportunity to use AI to promote social welfare through the design of optimal tax policies in dynamic economies.

SummaryWe investigate NVIDIA's Triton (TensorRT) Inference Server as a way of hosting Transformer Language Models. The blog is roughly divided into two parts: (i) instructions for setting up your own inference server, and (ii) benchmarking experiments. The instructions are intended to be detailed and standalone, but readers interested solely in

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.

We are proud and excited to announce this year’s winners of the Salesforce Research Deep Learning Grant.

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.

Learn about the ethical implications of voice for business and how to make them an operational and strategic priority now—before you’re too far down the path.

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.

The Salesforce Research team announces their second annual Deep Learning Grant to fund AI research projects from around the world.

Published: August 6, 2019 Earlier this year, Danielle Cass and I ran a workshop with 23 ethical AI practitioners from 15 organizations and shared out insights of what they are doing that has been successful and the open questions or challenges they are working through. A few months later, Matt

Published: July 10, 2019 As a discipline, those of us working on ethical or responsible AI, are learning together how to translate ethical principles into business practices that work for each of our organizations. The result is a shared community of practice. In this blog post, we share early lessons

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.

Published: May 30, 2019 With the advent of low-code developer tools, people of varying skill levels and backgrounds — not just those with PhDs — are not only able to leverage the benefits of artificial intelligence (AI), but also build intelligent systems. As access to AI widens and the depth of its

For some, the world we live in today can be represented as data in high-dimensional spatiotemporal space—which we humans typically use language to describe, interpret, and reason about. For Salesforce Research Intern, Kevin (Chih-Yao) Ma, this topic became a key focal point in his research “Self-Monitoring Navigation Agent via

Most research on question answering implicitly assumes that the answer and its evidence appear close together in a single document. How can we teach computers to answer questions that require reasoning over multiple pieces of evidence?

May 6th - May 9th @ Ernest N. Morial Convention Center, New Orleans ABOUT:Salesforce is excited to be a diamond sponsor of the Seventh International Conference on Learning Representations happening Monday, May 6th through Thursday, May 9th in New Orleans, Louisiana. We encourage you to stop by our Salesforce Booth

In the research paper, “A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation” Futureforce PhD Intern Akhilesh Gotmare, worked with Research Scientist Nitish Shirish Keskar, Director of Research Caiming Xiong, and Salesforce Chief Scientist Richard Socher, to leverage recent tools built specifically for analyzing deep networks,

There's a lot of conversation these days around deep learning, specifically around its remarkable successes in solving challenging reinforcement learning (RL) problems. That said, deep learning still suffers from the need to engineer a reward function that not only reflects the task, but is also carefully shaped. It's a challenge

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 intelligence. It's just the sort of thing Futureforce PhD Intern Chien-Sheng (Jason) Wu aims to solve in

This is my obsessively curated list of research papers and articles on ethics in AI that I have been collecting over the years. Ones in bold are those that I refer back to and found particularly useful. Let me know if I am missing your favorites.

If you are thinking about incorporating ethics into your company's culture or product development cycle, check out this list of dozens of ethical tools before you try recreating the wheel.

On December 19th, 2018, 2 dozen women working on AI ethics in tech companies, non-profits, and industry analysts came together for a day to share their experiences and insights, as well as to brainstorm solutions to big challenging we are facing.

You may be the first person in your company focusing on the need to create or implement ethical AI. If so, I’d like to share some lessons learned that could help you in your work.

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.

This post is for those responsible for implementing AI systems but do not have a background in data science, statistics, or probability. The intention is to create an approachable introduction to the key concepts to identify potential bias (error) in their training data.

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.

What kind of world do we want for ourselves, those we love, and future society? How do we are organizations, employees, customers, and members of society ensure that technology is in service of society and not the other way around?

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.

In this article, I will focus on mechanisms for removing exclusion from your data and algorithms. Of all the interventions or actions we can take, the advancements here have the highest rate of change.

This is part one of a two-part series about how to build ethics into AI. Part one focuses on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally.

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.