Learning a good representation is an essential component for deep reinforcement learning (RL). Representation learning is especially important in multitask and partially observable settings where building a representation of the unknown environment is crucial to solve the tasks. Here we introduce Prediction of Bootstrap Latents (PBL), a simple and flexible self-supervised representation learning algorithm for multitask deep RL. PBL builds on multistep predictive representations of future observations, and focuses on capturing structured information about environment dynamics. Specifically, PBL trains its representation by predicting latent embeddings of future observations. These latent embeddings are themselves trained to be predictive of the aforementioned representations. These predictions form a bootstrapping effect, allowing the agent to learn more about the key aspects of the environment dynamics. In addition, by defining prediction tasks completely in latent space, PBL provides the flexibility of using multimodal observations involving pixel images, language instructions, rewards and more. We show in our experiments that PBL delivers across-the-board improved performance over state of the art deep RL agents in the DMLab-30 and Atari-57 multitask setting.

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the decision-making process of DNNs has blossomed into an active research field. A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking. This complexity is further exacerbated by the general confusion that exists in defining what it means to be able to explain the actions of a deep learning system and to evaluate a system's "ability to explain". To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field.

Time-critical analysis of social media streams is important for humanitarian organizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis related data posted on social media. However, due to the dispersed nature of the datasets used in the literature, it is not possible to compare the results and measure the progress made towards better models for crisis informatics. In this work, we attempt to bridge this gap by standardizing various existing crisis-related datasets. We consolidate labels of eight annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks, respectively. The consolidation results in a larger dataset that affords the ability to train more sophisticated models. To that end, we provide baseline results using CNN and BERT models.

Zoom will let its paid users decide where their data is going after its latest privacy scandal. The changes come after criticism over the fact that users' data was being sent through servers in Chinese data centres, potentially allowing conversations and video chats to be intercepted by the Chinese government as they were sent. Zoom said its centres in the country have "always been" geofenced, meaning that data generated outside of China would not move through the country. But chief executive Eric Yuan admitted that in the rush to meet demand during the coronavirus lockdown some best practices were not implemented and some meeting data may have been routed through China. Mr Yuan said this issue had since been corrected.

Sonos has launched a new radio service, allowing people to stream music for free. It is the first time that Sonos has offered a music service of its own on its smart speakers, which until now have played music from other streaming platforms such as Spotify and Apple Music. The new radio service sits alongside those streaming platforms, which will continue to work in the same way as before. It is available now through a software update that can be installed using the Sonos app. It integrates traditional radio programming on news, music and sport from other radio stations, taken from partners such as TuneIn.

Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.

In recent years, a critical initialization scheme of orthogonal initialization on deep nonlinear networks has been proposed. The orthogonal weights are crucial to achieve {\it dynamical isometry} for random networks, where the entire spectrum of singular values of an input-output Jacobian are around one. The strong empirical evidence that orthogonal initialization in linear networks and the linear regime of nonlinear networks can speed up training than Gaussian initialization raise great interests. One recent work has proven the benefit of orthogonal initialization in linear networks. However, the dynamics behind it have not been revealed on nonlinear networks. In this work, we study the Neural Tangent Kernel (NTK), which can describe dynamics of gradient descent training of wide network, and focus on fully-connected and nonlinear networks with orthogonal initialization. We prove that NTK of Gaussian and orthogonal weights are equal when the network width is infinite, resulting in a conclusion that orthogonal initialization can speed up training is a finite-width effect in the small learning rate regime. Then we find that during training, the NTK of infinite-width network with orthogonal initialization stays constant theoretically and varies at a rate of the same order as Gaussian ones empirically, as the width tends to infinity. Finally, we conduct a thorough empirical investigation of training speed on CIFAR10 datasets and show the benefit of orthogonal initialization lies in the large learning rate and depth phase in a linear regime of nonlinear network.

These days consumers and businesses no longer think of artificial intelligence (AI) as something from a sci-fi movie or book – that's because AI is now part of everyday life. Just think of Netflix's suggestion algorithm, Uber's location algorithm and even Tinder's matching algorithm – all business models powered by AI to help drive consumer decisions. And the same AI can be used in the sales process to help sales teams make decisions. At World Tour Sydney Reimagined we saw Salesforce Einstein Voice provide deal coaching to sales reps using predictive technology. And in the Sales keynote, Modern Star explained how their sales reps were using data insights to further understand their customer and close deals.

Background: In the United States, 5.7 million patients are admitted annually to intensive care units (ICU), with costs exceeding $82 billion. Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Methods: Using the University of Florida Health (UFH) Integrated Data Repository as Honest Broker, we created a database with electronic health records data from a retrospective study cohort of 38,749 adult patients admitted to ICU at UF Health between 06/01/2014 and 08/22/2019. This repository includes demographic information, comorbidities, vital signs, laboratory values, medications with date and timestamps, and diagnoses and procedure codes for all index admission encounters as well as encounters within 12 months prior to index admission and 12 months follow-up. We developed algorithms to identify acuity status of the patient every four hours during each ICU stay. Results: We had 383,193 encounters (121,800 unique patients) admitted to the hospital, and 51,073 encounters (38,749 unique patients) with at least one ICU stay that lasted more than four hours. These patients requiring ICU admission had longer median hospital stay (7 days vs. 1 day) and higher in-hospital mortality (9.6% vs. 0.4%) compared with those not admitted to the ICU. Among patients who were admitted to the ICU and expired during hospital admission, more deaths occurred in the ICU than on general hospital wards (7.4% vs. 0.8%, respectively). Conclusions: We developed phenotyping algorithms that determined patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding resource use and escalation of care.