Recent works on ride-sharing order dispatching have highlighted the importance of taking into account both the spatial and temporal dynamics in the dispatching process for improving the transportation system efficiency. At the same time, deep reinforcement learning has advanced to the point where it achieves superhuman performance in a number of fields. In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics. In particular, we model the ride dispatching problem as a Semi Markov Decision Process to account for the temporal aspect of the dispatching actions. To improve the stability of the value iteration with nonlinear function approximators like neural networks, we propose Cerebellar Value Networks (CVNet) with a novel distributed state representation layer. We further derive a regularized policy evaluation scheme for CVNet that penalizes large Lipschitz constant of the value network for additional robustness against adversarial perturbation and noises. Finally, we adapt various transfer learning methods to CVNet for increased learning adaptability and efficiency across multiple cities. We conduct extensive offline simulations based on real dispatching data as well as online AB tests through the DiDi's platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods. We finally show that the performance can be further improved through the efficient use of transfer learning.

Deep Learning (DL) is considered the state-of-the-art in computer vision, speech recognition and natural language processing. Until recently, it was also widely accepted that DL is irrelevant for learning tasks on tabular data, especially in the small sample regime where ensemble methods are acknowledged as the gold standard. We present a new end-to-end differentiable method to train a standard FFNN. Our method, \textbf{Muddling labels for Regularization} (\texttt{MLR}), penalizes memorization through the generation of uninformative labels and the application of a differentiable close-form regularization scheme on the last hidden layer during training. \texttt{MLR} outperforms classical NN and the gold standard (GBDT, RF) for regression and classification tasks on several datasets from the UCI database and Kaggle covering a large range of sample sizes and feature to sample ratios. Researchers and practitioners can use \texttt{MLR} on its own as an off-the-shelf \DL{} solution or integrate it into the most advanced ML pipelines.

Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhood precludes them from committing crimes, it is useful for a number of parties to understand under what type of intentional mode an algorithm might transgress. From the perspective of the creator or owner they would like ensure that their algorithms never intend to cause harm by doing things that would otherwise be labelled criminal if committed by a legal person. Prosecutors might have an interest in understanding whether the actions of an algorithm were internally intended according to a transparent definition of the concept. The presence or absence of intention in the algorithmic agent might inform the court as to the complicity of its owner. This article introduces definitions for direct, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor.

Current technology for autonomous cars primarily focuses on getting the passenger from point A to B. Nevertheless, it has been shown that passengers are afraid of taking a ride in self-driving cars. One way to alleviate this problem is by allowing the passenger to give natural language commands to the car. However, the car can misunderstand the issued command or the visual surroundings which could lead to uncertain situations. It is desirable that the self-driving car detects these situations and interacts with the passenger to solve them. This paper proposes a model that detects uncertain situations when a command is given and finds the visual objects causing it. Optionally, a question generated by the system describing the uncertain objects is included. We argue that if the car could explain the objects in a human-like way, passengers could gain more confidence in the car's abilities. Thus, we investigate how to (1) detect uncertain situations and their underlying causes, and (2) how to generate clarifying questions for the passenger. When evaluating on the Talk2Car dataset, we show that the proposed model, \acrfull{pipeline}, improves \gls{m:ambiguous-absolute-increase} in terms of $IoU_{.5}$ compared to not using \gls{pipeline}. Furthermore, we designed a referring expression generator (REG) \acrfull{reg_model} tailored to a self-driving car setting which yields a relative improvement of \gls{m:meteor-relative} METEOR and \gls{m:rouge-relative} ROUGE-l compared with state-of-the-art REG models, and is three times faster.

The Emotion Cause Extraction (ECE)} task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.

I made my first trip to China in late 2008. I was able to speak to developers and entrepreneurs and to get a sense of the then-nascent market for cloud computing. With over 900 million Internet users as of 2020 (according to a recent report from China Internet Network Information Center), China now has the largest user base in the world. A limited preview of the China (Beijing) Region was launched in 2013 and brought to general availability in 2016. A year later the AWS China (Ningxia) Region launched.

NSW Police has "infused" its insights platform with Microsoft Azure-based artificial intelligence and machine learning services to fast-track video and audio evidence analysis. Microsoft recently worked with Australia's largest policing agency to containerise cognitive processing for the core investigation platform in Azure and feed the results back. The work comes ahead of a future migration of insights to Azure, which is expected to take place "shortly". As revealed by iTnews in April, NSW Police is working to stand up a protected-level Azure data centre under what it calls the'Azura Cloud Project' to support its broader transformation program. NSW Police expects to retire, re-architect or replace more than 200 legacy systems with cloud-based systems as part of the program.

Australia's Charles Sturt University (CSU) has announced plans to create a "hands-free" smart farm where robots will do all the work -- no human laborers required. The challenge: The majority of the food we eat comes from farms, and as the population grows, so does the amount of food needed to feed it. However, there's a lot of work that needs to be done around a farm, and many farmers are having trouble finding people to do it. Labor shortages have been a chronic problem in farms throughout the developed world. The idea: Robots and AI could help close the labor gap, literally doing the jobs people used to do.

Apple has let use your iPhone and Apple Watch as digital car key. Come iOS 15, a new tool called Home Keys will let you do the same with a compatible smart lock to your home. It's one of several smart home-related features Apple showed off during WWDC 2021. Once you're inside your home, tighter integration between HomePod and Apple TV devices will allow you to control tvOS by issuing voice commands through one of Apple's smart speakers. For those who own both an Apple TV 4K and one or more HomePod mini speakers, the company will let you pair those devices together for a better audio experience.

Artificial intelligence capabilities are expanding exponentially, with AI now being utilized in industries from advertising to medical research. The use of AI in more sensitive areas such as facial recognition software, hiring algorithms, and healthcare provision, have precipitated debate about bias and fairness. Bias is a well-researched facet of human psychology. Research regularly exposes our unconscious preferences and prejudices, and now we see AI reflect some of these biases in their algorithms. So, how does artificial intelligence become biased? And why does this matter?