Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harder tasks with more realistic application scenarios. In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation. In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework. With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art. 1 Introduction Coherence modeling involves building text analysis models that can distinguish a coherent text from incoherent ones. It has been a key problem in discourse analysis with applications in text generation, summarization, and coherence scoring. V arious linguistic theories have been proposed to formulate coherence, some of which have inspired development of many of the existing coherence models. These include the entity-based local models (Barzilay and Lapata, 2008; Elsner and Charniak, 2011b) that consider syntactic realization of entities in adjacent sentences, inspired by the Centering Theory (Grosz et al., 1995). Another line of research uses discourse relations between sentences to predict local coherence (Pitler and Nenkova, 2008; Lin et al., 2011). These methods are inspired by the discourse structure theories like Rhetorical Structure Theory (RST) (Mann and Thompson, 1988) that formalizes coherence in *Equal contribution terms of discourse relations.

We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models. The introduction of knowledge dissimilarity functions in BioNAS enables the joint optimization of predictive power and biological knowledge through searching architectures in a model space. By optimizing the consistency with existing knowledge, we demonstrate that BioNAS optimal models reveal novel knowledge in both simulated data and in real data of functional genomics. BioNAS provides a useful tool for domain experts to inject their prior belief into automated machine learning and therefore making deep learning easily accessible to practitioners. BioNAS is available at https://github.com/zj-zhang/BioNAS-pub.

A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementary requirement, we introduce the initial release of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is "self-dialog" in which crowdsourced workers write the entire dialog themselves. We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets. We offer several baseline models including state of the art neural seq2seq architectures with benchmark performance as well as qualitative human evaluations. Dialogs are labeled with API calls and arguments, a simple and cost effective approach which avoids the requirement of complex annotation schema. The layer of abstraction between the dialog model and the service provider API allows for a given model to interact with multiple services that provide similar functionally. Finally, the dataset will evoke interest in written vs. spoken language, discourse patterns, error handling and other linguistic phenomena related to dialog system research, development and design.

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing. 1 Introduction Knowledge tracing - where machine monitors students' knowledge states and their skill acquisition levels - is essential for personalized education and a fundamental part of intelligent tutoring systems [15,7,1,12]. However, tracing student knowledge states is inherently challenging because of the complexity of human learning process, which involves a variety of factors from diverse domains such as neural science [3,4], psychology [10], and education [8].

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and actions. These embeddings capture the structure of the environment's dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.

Artificial Intelligence is probably the biggest thing in tech right now. You can't walk two steps without finding either a new form of AI being developed, or a new …

Dedicated to pioneering the AI-empowered property insurance sector while offering perfect customer service, Ping An Property Insurance, as one of "Leading Biosphere Companies", is aiming to unleash the potential of artificial intelligence with its acute insight into the emerging technology, leveraging AI application and big data systems to transform the insurance industry. The FACEKYD extended the traditional driving risk ranking algorithm by leveraging state-of-the-art deep learning networks to extract facial driving risk factor. Different from the face recognition algorithm, the FACEKYD algorithm can not only differentiate the driving risk from different customers, but also keep the risk scores from the same customer stable by using the innovative rank algorithm "tetrad ranking". Another star product of the company currently under development is DRVR (Driving Risk Video Recognition). DRVR technology combines FACEKYD and DMS technology, through the identification and analysis of driving behavior, and combining the FACEKYD auxiliary driving risk prediction, to manage the risk in the whole process of driving and active warning, provide a variety of interim risk management solutions, rather than a single post-event compensation According to the Ping An Property Insurance technology center, the technology, with the bolster of database encompassing people, cars and roads, can identify hazardous driving behaviors including drowsy driving, smoking, looking at phones, dangerous lane-changing and speeding.

Everyone knows that Artificial Intelligence (AI) is a big thing and almost every single tech company in the world seems to be riding that hype wave. However, we feel like it's our purpose and responsibility to inform you that the claims of almost every tech company using AI are absolutely false, and here's why. Such hype wave comes with a whole new world of marketers and scammers using the buzz word with fake AI to trick their potential users into thinking they're using real autonomous AI. Our job and responsibility is to inform you on the business aspects of truly "Useful Autonomous AI vs Useless AI"; because there's no point in supposedly using AI if it's not going to outperform humans. Useful and autonomous AI implies using a combination of neural networks, machine, transfer, reinforcement and deep learning in production models that are actually taking action on the predictions of the simulations, not just making recommendations and providing insights of what to do (sorry "AI" analytics companies, but if some human needs to actually do what your platform recommends, then that's not a truly useful, independent and autonomous AI).

An engineer's mind can take them far, but for many tasks, an engineer is only as good as the tools that are currently available. This is where deep learning frameworks come into play as they provide engineers with the means to construct and tinker with many programs and applications. Like with all tools, some deep learning frameworks are better than others. Here is a handful of great ones that will see you through most machine learning tasks. Due to its Python foundation and its pre-loaded tutorials, TensorFlow is a great framework for amateur deep learning engineers.