TL;DR: Applications are now open for the next cohort of Salesforce’s AI Residency Program. We invite 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. Candidates based in the U.S. can apply

TL;DR: Deep reinforcement learning (RL), a powerful learning framework to train AI agents, can be slow as it requires repeated interaction with a simulation of the environment. Our original WarpDrive accelerates multi-agent deep RL on NVIDIA GPUs, enabling 10-100x speedups compared to alternative CPU+GPU implementations of multi-agent simulations.

AUTHORS: Chenghao Liu, Quang Pham, Doyen Sahoo, Donald Rose TL;DR: Nonstationary data, which changes its statistical properties over time, can make time series forecasting difficult. Despite the recent success of deep learning techniques for time series forecasting tasks, these methods are not scalable for applications where data arrives sequentially

TL;DR: The performance of existing time-series forecasting methods can degrade due to non-stationarity, where the statistical distribution of time-series data changes over time. Our new DeepTime method overcomes non-stationarity issues by leveraging a “forecasting as meta-learning” framework on deep time-index models. DeepTime achieves competitive accuracy on the long-sequence time-series

AUTHORS: Zeyuan Chen, Ran Xu, Luyu Yang, Donald Rose TL;DR: Many methods designed to preserve online privacy propose complex security measures to protect sensitive data. We believe that not storing any sensitive data is the optimal way to preserve privacy, so we propose a “burn after reading” online framework:

As we say a fond farewell to summer (bummer!), let's look back and review some of the stellar work reported on by Salesforce AI researchers during the past few months. (For more details, we encourage you to click the link for each project to read the full blog

TL;DR: LAVIS (short for LAnguage-VISion) is an open-source deep learning library for language-vision research and applications, offering comprehensive support for a wide range of tasks, datasets, and state-of-the-art models. Featuring a unified interface and modular design, it’s easy to use off-the-shelf and to extend with new capabilities. With

TL;DR: We developed a new time-series forecasting model called ETSformer that leverages the power of two frameworks. By combining the classical intuition of seasonal-trend decomposition and exponential smoothing with modern transformers – as well as introducing novel exponential smoothing and frequency attention mechanisms – ETSformer achieves state-of-the-art performance. Background Before diving

AUTHORS: Chen Xing, Mingfei Gao, Donald Rose TL;DR: Most AI object detection methods work only on limited object categories, due to the human effort required for bounding-box annotations of training data. We developed a new method that automatically generates pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs,

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

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

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

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.