When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When COVID-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson-Janssen COVID-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For COVID-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user's preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user's demand on the news diversity. We have three main steps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user's interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.

Google has announced a series of updates soon coming to Google Maps, as part of the company's larger goal of delivering over 100 A.I.-powered improvements to the platform by year-end. Among the new improvements, detailed during Google I/O's developer conference this week, are new routing updates, Live View enhancements, an expansion of detailed street maps, a new "area busyness" feature, and a more personalized Maps experience. The new routing updates will involve the use of machine learning and navigation information to help reduce "hard-braking moments" -- meaning, those times when traffic suddenly slows, and you have to slam on your brakes. Today, when you get your directions in Maps, Google calculates multiple route options based on a variety of factors, like how many lanes a road has or how direct the route is. With the update, it will add one more: which routes are least likely to cause a "hard-braking moment."

The planet is faced with overwhelming environmental problems. Rising environmental pollution is wreaking havoc on nature and endangering the lives of millions of humans. Evolving digital technologies offer a bottom-up solution to tackling climate change. These digital technologies have a revolutionary way to involve citizens in addressing local and global issues. Young people are generally the most worried regarding the consequences of climate change. Early findings of ongoing projects suggest a high potential for leveraging digital technology in joint measures to preserve the world for ourselves and future generations.

The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is "The Abuse and Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics; Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The piece (and accompanying infographic), is a deep-dive into the historical and systematic silencing, erasure, and revision of Black women's contributions to knowledge and scholarship in the United Stations, and globally. Exposing and countering this Playbook has become increasingly important following the firing of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google. This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

Recently, plenty of work has tried to introduce transformers into computer vision tasks, with good results. Unlike classic convolution networks, which extract features within a local receptive field, transformers can adaptively aggregate similar features from a global view using self-attention mechanism. For object detection, Feature Pyramid Network (FPN) proposes feature interaction across layers and proves its extremely importance. However, its interaction is still in a local manner, which leaves a lot of room for improvement. Since transformer was originally designed for NLP tasks, adapting processing subject directly from text to image will cause unaffordable computation and space overhead. In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture, named Content-Augmented Feature Pyramid Network (CA-FPN), which proposes a global content extraction module and deeply combines with FPN through light linear transformers. What's more, light transformers can further make the application of multi-head attention mechanism easier. Most importantly, our CA-FPN can be readily plugged into existing FPN-based models. Extensive experiments on the challenging COCO object detection dataset demonstrated that our CA-FPN significantly outperforms competitive baselines without bells and whistles. Code will be made publicly available.

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over a possibly infinite random sequence of Markov Decision Processes (MDPs), each of which drawn from some unknown distribution. We call each such MDP a context. Most related works make strong assumptions such as knowledge about the distribution over contexts, the existence of pre-training phases, or a priori knowledge about the number, sequence, or boundaries between contexts. We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics that reflect whether novel, specialized policies need to be created and deployed to tackle novel contexts, or whether previously-optimized ones might be reused. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses; and (ii) it bounds the rate of false alarm, which is important in order to minimize regret. Our method constructs a mixture model composed of a (possibly infinite) ensemble of probabilistic dynamics predictors that model the different modes of the distribution over underlying latent MDPs. We evaluate our algorithm on high-dimensional continuous reinforcement learning problems and show that it outperforms state-of-the-art (model-free and model-based) RL algorithms, as well as state-of-the-art meta-learning methods specially designed to deal with non-stationarity.

The 4th edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since January 2021. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, with a particular focus on four key themes: Ethical AI, Fairness & Justice, Humans & Tech, and Privacy. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Opening the report is a long-form piece by Edward Higgs (Professor of History, University of Essex) titled "AI and the Face: A Historian's View." In it, Higgs examines the unscientific history of facial analysis and how AI might be repeating some of those mistakes at scale. The report also features chapter introductions by Alexa Hagerty (Anthropologist, University of Cambridge), Marianna Ganapini (Faculty Director, Montreal AI Ethics Institute), Deborah G. Johnson (Emeritus Professor, Engineering and Society, University of Virginia), and Soraj Hongladarom (Professor of Philosophy and Director, Center for Science, Technology and Society, Chulalongkorn University in Bangkok). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

In its most traditional setting, the main concern of optimization theory is the search for optimal solutions for instances of a given computational problem. A recent trend of research in artificial intelligence, called solution diversity, has focused on the development of notions of optimality that may be more appropriate in settings where subjectivity is essential. The idea is that instead of aiming at the development of algorithms that output a single optimal solution, the goal is to investigate algorithms that output a small set of sufficiently good solutions that are sufficiently diverse from one another. In this way, the user has the opportunity to choose the solution that is most appropriate to the context at hand. It also displays the richness of the solution space. When combined with techniques from parameterized complexity theory, the paradigm of diversity of solutions offers a powerful algorithmic framework to address problems of practical relevance. In this work, we investigate the impact of this combination in the field of Kemeny Rank Aggregation, a well-studied class of problems lying in the intersection of order theory and social choice theory and also in the field of order theory itself. In particular, we show that the Kemeny Rank Aggregation problem is fixed-parameter tractable with respect to natural parameters providing natural formalizations of the notions of diversity and of the notion of a sufficiently good solution. Our main results work both when considering the traditional setting of aggregation over linearly ordered votes, and in the more general setting where votes are partially ordered.