Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.

Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of different variables in customer reviews on a women clothing e-commerce, and to classify each review whether it recommends the reviewed product or not and whether it consists of positive, negative, or neutral sentiment. To achieve these goals, we employed univariate and multivariate analyses on dataset features except for review titles and review texts, and we implemented a bidirectional recurrent neural network (RNN) with long-short term memory unit (LSTM) for recommendation and sentiment classification. Results have shown that a recommendation is a strong indicator of a positive sentiment score, and vice-versa. On the other hand, ratings in product reviews are fuzzy indicators of sentiment scores. We also found out that the bidirectional LSTM was able to reach an F1-score of 0.88 for recommendation classification, and 0.93 for sentiment classification.

Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks. In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets of LibriSpeech. We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance. In some experiments, we also use an auxiliary CTC loss function to help the convergence. In addition, we train long short-term memory (LSTM) language models on subword units. By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model.

We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature. Our main results include a series of axiomatizations, allowing comparison between this framework and existing frameworks (normality-ordering models, causal structural equation models), and a complexity result establishing NP-completeness of the satisfiability problem. Perhaps surprisingly, some of the basic logical principles common to all existing approaches are invalidated in our causal simulation approach. We suggest that this additional flexibility is important in modeling some intuitive examples.

Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.

The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches. In terms of context-aware session modeling, however, the existing RNN-based models are limited in that they are not designed to explicitly model rich static user-side contexts (e.g., age, gender, location). Therefore, in this paper, we explore the utility of explicit user-side context modeling for RNN session models. Specifically, we propose an augmented RNN (ARNN) model that extracts high-order user-contextual preference using the product-based neural network (PNN) in order to augment any existing RNN session model. Evaluation results show that our proposed model outperforms the baseline RNN session model by a large margin when rich user-side contexts are available.

Transport and logistics businesses are investing in Internet of Things (IoT)-based smart technologies to help them take advantage of the wealth of opportunities that the Fourth Industrial Revolution offers. This is according to research data collected by Inmarsat, the mobile satellite communications provider, which reveals that the sector is prioritising IoT, machine learning and robotics to increase efficiencies across the supply chain. Inmarsat's The Future of IoT in Enterprise report, featuring responses from 100 large global transportation companies, found that respondents see IoT as the top priority in their approach to digital transformation, with 36% having already deployed IoT-based solutions, and a further 45% expecting to roll the technology out by 2019. The research further revealed that transport companies are rapidly exploring a wide range of other next generation technologies in the pursuit of digital transformation. The most popular are machine learning (37%), robotics (37%) and 3D printing (29%).

China was late to the last industrial revolution, but with the arrival of AI it is determined not to miss the next one. The Chinese government is introducing a textbook called "Fundamentals of Artificial Intelligence" to 40 high schools, according to the South China Morning Post. It describes the history of AI and how the technology can be applied in areas like facial recognition, autonomous driving, and public security. Last year, the central government asked the country's education policy makers to include AI courses in primary and secondary schools. The nine-chapter book was penned by an all-star lineup, including the chairman of one of the world's most valuable AI startups, SenseTime.

E-commerce sales for 2017 were $453.5 billion in the U.S. and $1.1 trillion in China, an increase of 16.0% and 32.6% respectively over 2016. This upward trend is projected to continue for the next many years. Consequently flexibility and an ability to handle an ever-increasing number of parcels is of concern to warehousing, fulfillment and distribution center (DC) managers around the world. Handling, distribution, transport and delivery – and the amortization of facility setup charges which often represent more cost than raw materials and manufacturing combined – are part of mounting challenges faced by today's fulfillment executives. Accordingly, warehousing and material handling are a big business for hundreds of different types of companies that provide conveyors, rollers, racks, vision systems, hoists, shelving, electric motors, slides, barcode readers, printers, ladders, gantries, tugs, forklifts, skids, totes, carts, and software systems of all types.