Significant advances have been made in Natural Language Proc essing (NLP) modelling since the beginning of 2018. The new approaches allow for accurate results, even when there is little labelled data, because these NLP mo dels can benefit from training on both task-agnostic and task-specific unlabelle d data. However, these advantages come with significant size and computational cos ts. This workshop paper outlines how our proposed convolutiona l student architecture, having been trained by a distillation process from a la rge-scale model, can achieve 300 inference speedup and 39 reduction in parameter count. In some cases, the student model performance surpasses its teacher on the studied tasks.

In this paper, we consider the problem of online learning of Markov decision processes (MDPs) with very large state spaces. Under the assumptions of realizable function approximation and low Bellman ranks, we develop an online learning algorithm that learns the optimal value function while at the same time achieving very low cumulative regret during the learning process. Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE. One of our key technical contributions in AVE is to formulate the elimination steps in OLIVE as contextual bandit problems. This technique enables us to apply the active elimination and expert weighting methods from (Dudik et al., 2011), instead of the random action exploration scheme used in the original OLIVE algorithm, for more efficient exploration and better control of the regret incurred in each policy elimination step. To the best of our knowledge, this is the first $\sqrt{n}$-regret result for reinforcement learning in stochastic MDPs with general value function approximation.

Event Representation Learning Enhanced with External Commonsense Knowledge Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China {xding, kliao, tliu, zyli, jwduan }@ir.hit.edu.cn Abstract Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market 1 . 1 Introduction Events are a kind of important objective information of the world. Structuralizing and representing such information as machine-readable knowledge are crucial to artificial intelligence (Li et al., 2018b, 2019). The main idea is to learn distributed representations for structured events (i.e. Figure 1: Intent and sentiment enhanced event embed-dings can distinguish distinct events even with high lexical overlap, and find similar events even with low lexical overlap. The function maps the summed vectors into an event embedding space.

This paper considers the problem of self-driving algorithm based on deep learning. This is a hot topic because self-driving is the most important application field of artificial intelligence. Existing work focused on deep learning which has the ability to learn end-to-end self-driving control directly from raw sensory data, but this method is just a mapping between images and driving. We prefer deep reinforcement learning to train a self-driving car in a virtual simulation environment created by Unity and then migrate to reality. Deep reinforcement learning makes the machine own the driving descision-making ability like human. The virtual to realistic training method can efficiently handle the problem that reinforcement learning requires reward from the environment which probably cause cars damge. We have derived a theoretical model and analysis on how to use Deep Q-learning to control a car to drive. We have carried out simulations in the Unity virtual environment for evaluating the performance. Finally, we successfully migrate te model to the real world and realize self-driving.

In recent years, with the development of deep learning technology, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text generation technology, that is the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional text generation (c-TextGen) has thus become a research hotspot. As a promising research field, we find that many efforts have been paid to researches of c-TextGen. Therefore, we aim to give a comprehensive review of the new research trends of c-TextGen. We first give a brief literature review of text generation technology, based on which we formalize the concept model of c-TextGen. We further make an investigation of several different c-TextGen techniques, and illustrate the advantages and disadvantages of commonly used neural network models. Finally, we discuss the open issues and promising research directions of c-TextGen.

Exeter-based edtech startup Sparx is receiving an injection of €22 million from Oxygen House, an ethical and impact-centric group of companies. Founded in 2010, Sparx is an in-class and homework solution that uses machine learning, personalised content, and data analytics to help teachers be more effective and improve learning outcomes for students in maths. Its first product'Sparx Maths', which focuses on 11 to 16-year-old students, was launched in September 2018 and combines high-quality content, including 32,000 carefully designed maths questions and an adaptive AI technology platform. Its platform provides daily insights into class and student progress, allowing teachers to quickly identify trends. The initial investment from Oxygen House has helped Sparx to work closely with schools to research, test, and develop an approach to learning maths which is highly engaging.

Aristo has passed an American eighth grade science test. If you are told Aristo is an earnest kid who loves to read all he can about Faraday and plays the drums you will say so what, big deal. Aristo, though, is an artificial intelligence program and scientists would like the world to know this is a big deal, as "a benchmark in AI development," as Melissa Locker called it in Fast Company. We mean, just think about it. Cade Metz, in The New York Times, has thought about it.

Udacity, the global lifelong learning platform, together with Bertelsmann, a media, services and education company, announced that applications are now open for 15,000 scholarships in Data, AI, and Cloud-Computing. As Udacity and Bertelsmann shared earlier this year, the new scholarship program is part of a three-year commitment by Bertelsmann to fund 50,000 scholarships. Both companies have partnered to increase learning opportunities in emerging technologies for students across the globe. "There simply aren't enough people who are equipped with Cloud, Data, and Artificial Intelligence skills," said Gabriel Dalporto, CEO of Udacity. "That's why Bertelsmann and Udacity share a commitment to train new talent and diversify the talent pool in these three exciting fields. I'm confident that the Bertelsmann Scholarship Program will empower learners to master new skills and land some of the most exciting and in-demand jobs available today!"

At a 2017 O'Reilly AI conference, Andrew Ng ranked reinforcement learning dead last in terms of its utility for business applications. Compared to other machine learning methods like supervised learning, transfer learning, and even unsupervised learning, deep reinforcement learning (RL) is incredibly data hungry, often unstable, and rarely the best option in terms of performance. RL has historically been successfully applied only in arenas where mountains of simulated data can be generated on demand, such as games and robotics. Despite RL's limitations in solving business use cases, some AI experts believe this approach is the most viable strategy for achieving human or superhuman Artificial General Intelligence (AGI). The recent victory of DeepMind's AlphaStar over top-ranked professional StarCraft players suggests we might be on the cusp of applying deep RL to real world problems with real-time demands, extraordinary complexity, and incomplete information.

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