We use smaller language models as generative classifiers to guide generation from larger language models. We show that this method can make generations friendlier, reduce bias and toxicity, and achieve zero-shot controllable generation of unseen topics.

TL; DR: We propose a new webly-supervised learning method which achieves state-of-the-art representation learning performance by training on large amounts of freely available noisy web images.Deep neural networks are known to be hungry for labeled data. Current state-of-the-art CNNs are trained with supervised learning on datasets such as ImageNet

We introduce Photon, a live demo of natural language interface to databases based on our latest research in neural semantic parsing. 🔗 https://naturalsql.com/

For all the good that AI can bring, responsible tech companies understand they must recognize, prepare for, and mitigate the potential unintended, harmful effects. That’s why Salesforce sees ethics as foundational to AI — and why we’re sharing a closer look at how we infuse an ethical process into

August 3 - 7, 2020. Fully virtual sessions. At Salesforce Research Asia, we aim to play a more active role in raising awareness to the exciting possibilities surrounding the use of AI in real-world application domains, data innovation challenges and issues, as well as to highlight our work in the

Our Salesforce Research team is inviting submissions from university faculty, non-profit organizations, and NGOs to apply for our Salesforce AI Research Grant.

We are launching an open source collaborative project to build an AI Economist that can be used to guide policy making in the real world. We invite you to join us in our mission to help improve the world with AI and economics.

We propose a simple causal (unidirectional) language model for Task-oriented Dialogue. SimpleTOD enables modeling of the inherent dependencies between the sub-tasks of task-oriented dialogue, by optimizing for all tasks in an end-to-end manner.

Morpheus exposes the potential allocative harms of popular pretrained NLP models by simulating inflectional variation. We propose adversarial fine-tuning for mitigating the effects of training only on error-free Standard English data.

Sneak a peek into our Industrial PhD Program with Samson.

The 58th Association for Computational Linguistics (ACL) Conference kicked off this week and runs from Sunday, Jul 5 to Friday, Jul 10 in a fully virtual format. ACL is the premier conference of the field of computational linguistics, covering a broad spectrum of diverse research areas that are concerned with

Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance.

In our study, we show how a language model, trained simply to predict a masked (hidden) amino acid in a protein sequence, recovers high-level structural and functional properties of proteins.

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.