Last week saw the International Conference on Learning Representations (ICLR 2020) go virtual. Over 5600 people, from 89 different countries, registered to participate. Here we provide a round-up of tweets from event participants, speakers and organisers. We wanted to build something that was fun to browse, async first, and feels alive. All the papers (sorted by filters) are easily accessible (finding papers in poster sessions can be exhaustive).

After decades spent discussing how and what to teach in the classrooms, the focus is now turning more to implementation, experts said at the World Innovation Summit for Education (WISE) conference in Doha, hosted by the Qatar Foundation on 19-21 November. Ministers and education experts discussed in Doha how to reap the benefits of the digital revolution as new challenges arise from teaching students across the world in the era of artificial intelligence. OECD countries spend on average 4.5% of their GDP on education. At the same time, education itself is transforming to adapt to a changing planet. The constant retooling of labour skills will be a central element of a European Commission paper on the future of the EU social pillar, to be published on 26 April, EURACTIV.com In an increasingly uncertain and unstable world, citizens are expected to become life-long learners in order to remain relevant for a fast-changing labour market that will be disrupted by machines.

We propose our solution to a particular practical problem in the domain of vehicle routing and scheduling. The generic task is finding the best allocation of the minimum number of \emph{mobile resources} that can provide periodical services in remote locations. These \emph{mobile resources} are based at a single central location. Specifications have been defined initially for a real-life application that is the starting point of an ongoing project. Particularly, the goal is to mitigate health problems in rural areas around a city in Romania. Medically equipped vans are programmed to start daily routes from county capital, provide a given number of examinations in townships within the county and return to the capital city in the same day. From the health care perspective, each van is equipped with an ultrasound scanner, and they are scheduled to investigate pregnant woman each trimester aiming to diagnose potential problems. The project is motivated by reports currently ranking Romania as the country with the highest infant mortality rate in the European Union. We developed our solution in two phases: modeling of the most relevant parameters and data available for our goal and then design and implement an algorithm that provides an optimized solution. The most important metric of an output scheduling is the number of vans that are necessary to provide a given amount of examination time per township, followed by total travel time or fuel consumption, number of different routes, and others. Our solution implements two probabilistic algorithms out of which we chose the one that performs the best.

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

A recent comprehensive study titled Global Machine Learning Operationalization Software Market 2020 by Company, Regions, Type and Application, Forecast to 2026 starts with offering the analytical inspection of the global market based on various segments. The report comprises the summary and advances the size of the marketplace owing to the various outlook possibilities. The report includes details about important products, revenue, production, and the business of top industry players. The report helps the companies to understand the threats and challenges in front of the businesses. This is a well-established, and precisely formulated report acknowledges major Machine Learning Operationalization Software industry vendors, key regions, demand & supply, applications, technology, revenue cost, and challenges.

Humans may sometimes regard robots with apprehension or resentment over the increasing automation of labor, but the coronavirus pandemic is showing how the two can work together in new ways that might save lives during a crisis. Around the globe, robots and other technologies, like drones and telehealth devices, are being used in a variety of settings and capacities to assist in the COVID-19 response since there is a level of elevated risk for human workers. Automated devices have delivered meals to quarantined travelers in a Chinese hotel; enforced curfews in Tunisia; scanned visitors for fevers entering a South Korean hospital; monitored patients in a hard-hit Italian city; and tracked social distancing compliance from the skies in a number of cities around the world, including Elizabeth, New Jersey. Many of the technologies were available commercially prior to the coronavirus outbreak, said Texas A&M University professor Robin Murphy, who studies how robots can be deployed during disasters. But now, "they are being used 24/7 and adapted to fit the needs of those using them," Murphy added.

Classical Planning is concerned with finding plans, or sequences of actions, that when applied to some initial condition specified by a set of logical predicates, will bring the environment to a state that satisfies a set of goal predicates. This is usually performed by some heuristic search procedure, and the resulting plan is applicable only to the specific instance that was solved. However, a possibly stronger outcome would be to find some sort of higher level plan that can solve many instances that belong to the same domain, and thus share an underlying structure. The study of methods that can discover such higher level plans is called Generalized Planning. Generalized plans do not necessarily exist for all classical planning domains, but finding such solutions for domains in which it is possible could obviate the need to perform compute intensive search in cases where we only wish to find a goal satisfying solution. To give an example of such a generalized plan, let us consider a simplified Blocksworld domain. In this domain there are unique blocks that can be either stacked on each other or strewn about the floor, and the goal is to stack and unstack blocks such that we arrive at a goal configuration from an initial configuration. Finding a plan that does so in an optimal number of steps is generally NPhard [10], but finding a plan that satisfies the goal regardless of cost can be done in polynomial time in the following manner: 1. Unstack all the blocks so that they are scattered on the floor 2. stack the block according to the goal configuration, beginning with the lower blocks This strategy is not optimal since we might unstack blocks that are already in their proper place according to the goal specification, but it will yield a goal satisfying plan for every instance in this simplified Blocksworld domain.

Linear integer constraints are one of the most important constraints in combinatorial problems since they are commonly found in many practical applications. Typically, encodings to Boolean satisfiability (SAT) format of conjunctive normal form perform poorly in problems with these constraints in comparison with SAT modulo theories (SMT), lazy clause generation (LCG) or mixed integer programming (MIP) solvers. In this paper we explore and categorize SAT encodings for linear integer constraints. We define new SAT encodings based on multi-valued decision diagrams, and sorting networks. We compare different SAT encodings of linear constraints and demonstrate where one may be preferable to another. We also compare SAT encodings against other solving methods and show they can be better than linear integer (MIP) solvers and sometimes better than LCG or SMT solvers on appropriate problems. Combining the new encoding with lazy decomposition, which during runtime only encodes constraints that are important to the solving process that occurs, gives the best option for many highly combinatorial problems involving linear constraints.

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.