TL;DR:  Salesforce Research and Mila announce AI for Global Climate Cooperation, a working group collaboration and competition to design negotiation protocols and climate agreements. We plan to coauthor a peer-reviewed scientific paper with top-performing teams; insights will be distilled into a policy brief shared with leading policymakers, informing future

TL;DR: CodeRL is a new framework for program synthesis through holistic integration of pretrained language models and deep reinforcement learning. By utilizing unit test feedback as part of model training and inference, and integrating with an improved CodeT5 model, CodeRL achieves state-of-the-art results on competition-level programming tasks. The following

Conference Overview This weekend will kick off the thirty-ninth International Conference on Machine Learning (ICML). This conference specifically aims to bring together professionals who are dedicated to the advancement of Machine Learning (ML) in Artificial Intelligence. Participants at ICML come from many different backgrounds, including academic and industrial researchers, entrepreneurs

Conference Overview This weekend marks the start of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). NAACL provides a regional focus for members of the Association for Computational Linguistics (ACL) in North America. NAACL organizes annual conferences, promotes cooperation and information exchange among

Conference Overview The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the annual conference on Computer Vision. CVPR is composed of both the main conference, as well as workshops and other courses, to provide a unique learning experience and networking opportunities in the field of Computer Vision. CVPR

AUTHORS: Sharvin Shah, Jin Qu, Donald Rose TL;DR: TaiChi is an open source library for few-shot NLP, designed for data scientists and software engineers who want to get some quick results or build proof-of-concept products but don’t have much experience with few-shot learning (FSL). The library abstracts complex

TL;DR:OmniXAI (short for Omni eXplainable AI) is designed to address many of the pain points in explaining decisions made by AI models. This open-source library aims to provide data scientists, machine learning engineers, and researchers with a one-stop Explainable AI (XAI) solution to analyze, debug, and interpret their

AUTHORS: Huan Wang, Aadyot Bhatnagar, Doyen Sahoo, Wenzhuo Yang, Steven Hoi, Caiming Xiong, Donald Rose TL;DR: Time series data is a critical source of insights for many applications, including IT Operations, Quality Management, Financial Analytics, and Inventory & Sales Management. While a variety of dedicated packages and software exist,

TL;DR: We propose ALPRO, a new video-and-language representation learning framework which achieves state-of-the-art performance on video-text retrieval and video question answering by learning fine-grained alignment between video regions and textual entities via entity prompts. For more background (a review of key concepts used in this post), please see the

Lead Author: Xi Ye TL;DR: We propose RnG-KBQA, a Rank-and-Generate Approach for Question Answering over Knowledge Bases, which enables answering natural language questions over large-scale knowledge bases. Our approach is capable of answering questions about topics never seen in the training data, which makes it generalizable to a broad

TL;DR: WarpDrive is a flexible, lightweight, easy-to-use end-to-end reinforcement learning (RL) framework; enables orders-of-magnitude faster training on a single GPU. PyTorch Lightning enables you to modularize experimental code, and build production-ready workloads fast. Together, they can help significantly accelerate multi-agent RL R&D. Reinforcement Learning: Agents Learn by

Conference Overview This year marks the 60th annual meeting of the Association for Computational Linguistics Conference (ACL [https://www.2022.aclweb.org/]). ACL is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or

TL;DR: The AI Economist, a reinforcement learning (RL) system, learns dynamic tax policies that optimize equality along with productivity in simulated economies, outperforming alternative tax systems. We have now expanded this research, which is being published in the interdisciplinary scientific journal Science Advances. Humans or AI: Which Can Design

Conference Overview This year marks the Tenth International Conference on Learning Representations ( ICLR [https://iclr.cc/Conferences/2022]), one of the premier academic conferences dedicated to advancing research in representation learning - a type of machine learning also referred to as feature learning or deep learning. ICLR features the latest

AUTHORS: Tian Xie, Xinyi Yang, Angela Lin, Donald Rose Introduction and Background Creating a system capable of conducting a meaningful conversation with a human and helping them accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI), and has been since AI’s beginnings. Meanwhile, as real conversational

Links: Research Paper [https://arxiv.org/abs/2203.13474], Github [https://github.com/salesforce/CodeGen] -------------------------------------------------------------------------------- Can you imagine a machine writing an app for you, just by telling it what you want? As futuristic as this scenario sounds, it’s actually here today. Salesforce AI Research outlines conversational AI

TL;DR: BLIP is a new pre-training framework for unified vision-language understanding and generation, which achieves state-of-the-art results on a wide range of vision-language tasks. Background For a review of some terms and definitions used in this blog, see our Appendix. Vision and language, two of the most fundamental methods

Recommendation systems are common in the consumer world. For example, Netflix, YouTube, and other companies use these systems to recommend items you would probably like, based on data about you - such as what items you've consumed (e.g., watched) before. But recommendation systems are not just for

TL;DR: Today’s NLP models, for all their recent successes, have certain limitations. Case in point: they exhibit poor performance when processing multilingual code-mixed sentences (each containing multiple languages). Our new approach addresses this problem by constructing code-mixed inputs designed to degrade (or “attack”) the model, exposing the limitations

Salesforce is excited to announce its new AI Residency Program. We are inviting researchers and engineers to join our 12-month training program, designed to help launch their careers while serving as AI Residents at Salesforce AI Research.

Discover CTRLsum, a novel framework for controllable text summarization that enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.

December 21, 2021 The leading AI research conference, NeurIPS 2021, has recently wrapped up, spanning seven very full days, 2,344 accepted papers, eight invited talks, ten tutorials, and nearly 70 workshops. Though there was diverse and innovative thought leadership on display, I found myself drawn to the particular topics

TL; DR: Introducing CodeT5 --- the first code-aware, encoder-decoder-based pre-trained programming language model, which enables a wide range of code intelligence applications including code understanding and generation tasks. CodeT5 achieves state-of-the-art performance on 14 sub-tasks in the CodeXGLUE code intelligence benchmark. Given the goal of improving software development productivity with

tldr: WarpDrive is an open-source framework to do multi-agent RL end-to-end on a GPU. It achieves orders of magnitude faster multi-agent RL training with 2000 environments and 1000 agents in a simple Tag environment. WarpDrive provides lightweight tools and workflow objects to build your own fast RL workflows. Check out

This blog accompanies the interactive demo [https://einstein.ai/the-ai-economist/ai-policy-foundation-and-covid-case-study] and paper [https://arxiv.org/abs/2108.02904]! Dive deeper into the underlying simulation code [https://www.github.com/salesforce/ai-economist ] and simulation card [https://github.com/salesforce/ai-economist/blob/master/COVID-19_Simulation-Card.pdf] . In this blog post, we

August 5, 2021 In June 2021, four of us from the Salesforce AI Ethics and Conversational Design teams collaborated with the Montreal AI Ethics Institute [https://montrealethics.ai/] (MAIEI) to facilitate a workshop on the responsible creation and implementation of chatbots and conversational assistants. Connor Wright [https://www.linkedin.com/

AI can learn the language of biology to create artificial proteins across multiple protein types that are functional and unseen in nature.

> TL; DR: We propose a new vision-language representation learning framework which achieves state-of-the-art performance by first aligning the unimodal representations before fusing them. Vision and language are two of the most fundamental channels for humans to perceive the world. It has been a long-standing goal in AI to build

An AI model can generate sequences across natural language and proteins with attributes that go beyond the training distribution

With data rapidly being generated by millions of people, it's not feasible to label all of it. Learn about the recent advancements in ML for how to train vision models with unlabelled data using self-supervised learning.

How many emails and working-related conversations do you have every day? The average office worker receives about 121 emails [https://www.campaignmonitor.com/blog/email-marketing/2019/05/shocking-truth-about-how-many-emails-sent/] daily and uncountable messages on platforms such as Slack [https://slack.com/], Team [https://www.microsoft.com/en-us/microsoft-teams/group-chat-software], or iMessage

This year marks the 9th annual conference on International Conference on Learning Representations (ICLR) taking place in a fully virtual format from May 4th through May 8th, 2021. ICLR is a premier academic conference in the field of representation learning, generally referred to as deep learning or feature learning. ICLR

The empirical success of deep learning has posed significant challenges to machine learning theory: Why can we efficiently train neural networks with gradient descent despite its highly non-convex optimization landscape? Why do over-parametrized networks generalize well? The recently proposed Neural Tangent Kernel (NTK) theory offers a powerful framework for understanding

> TL;DR: We propose controllable counterfactuals (CoCo) to evaluate dialogue state tracking (DST) models on novel scenarios, which results in significant performance drop of up to 30.8% for state-of-the-art DST models. Using CoCo for data augmentation improves the accuracy of the SoTA DST model by 5.5% on

Transformer-inspired tips for enhancing the design of neural networks that process graph-structured data

February 2020 Summary from FAccT 2020 CRAFT Session > AI Ethics practitioners in industry look to researchers for insights on how to best identify and mitigate harmful bias in their organization’s AI solutions and create more fair or equitable outcomes. However, it can be a challenge to apply those

We are proud to announce the 2020 winners of our Salesforce AI Research Grant. Each of our winners will receive a $50K grant to advance their work and help us shape the future of AI.

AI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye

> TL; DR: We find that current self-supervised learning approaches suffer from poor visual grounding and receive improper supervisory signal when trained on complex scene images. We introduce CAST to improve visual grounding during pretraining and show that it yields significantly better transferable features. Self-supervised learning and its grounding problem

This year marks the 34th annual conference on Neural Information Processing Systems (NeurIPS [https://neurips.cc/]) reimagined for the first time ever in a fully virtual format. NeurIPS is a leading conference in the area of machine learning and neural information processing systems in their biological, technological, mathematical, and theoretical

> TL; DR: We propose a new semi-supervised learning method which achieves state-of-the-art performance by learning jointly-evolved class probabilities and image representations. What are the existing semi-supervised learning methods? Semi-supervised learning aims to leverage few labeled data and a large amount of unlabeled data. As a long-standing and widely-studied topic

This year marks the 24th annual Empirical Methods in Natural Language Processing (EMNLP) [https://2020.emnlp.org/] conference reimagined for the first time ever in a fully virtual format. EMNLP is a leading conference in the area of Natural Language Processing covering a broad spectrum of diverse research areas that

In recent years, the natural language processing (NLP) community has seen the development of increasingly powerful language models [1, 2], capable of generating textual output that is indistinguishable from human-written text. This includes our own model called CTRL [https://blog.salesforceairesearch.com/introducing-a-conditional-transformer-language-model-for-controllable-generation/] [3] (Conditional Transformer Language Model) for controllable

We recently released Foundation, an open-source framework [https://github.com/salesforce/ai-economist] to build economic simulations. Foundation has been designed with flexibility and AI research in mind, and can be modified by anyone. AI simulations offer researchers the power to generate data and evaluate outcomes of virtual economies that capture

At Salesforce, we take seriously our mission to create and deliver AI technology that is responsible, accountable, transparent, empowering, and inclusive. These principles ensure that our AI is safe, ethical, and engenders trust.

> TL;DR: We theoretically analyze the differential architecture search (DARTS) for understanding the role and impact of skip connections, which inspires a new method for Neural Architecture Search (NAS) using group-structured sparse gates and path-depth-wise regularization to overcome the limitation of existing NAS methods for AutoML. In our work

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

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