"Give me a double cappuccino. Oh, make that two single cappuccinos. And make it all to go." Being indecisive with your coffee order has never been easier than with the Alibaba Machine Intelligence Technologies' "smart ordering" system. Launched Wednesday by the Alibaba DAMO Academy unit, the new technology lets customers at cafés and restaurants place their orders via voice with a smart-ordering machine and make as many changes or corrections as they want. The machine's AI is able to catch all the changes and update the order in real time.

Artificial Intelligence (AI) in Retail Market size is set to exceed USD 8 billion by 2024; according to a new research report by Global Market Insights, Inc. The AI in retail market is driven by the increasing investments in it across the globe. The growing investment in the technology is attributed to the wide applications of the AI technology along with advanced analytics, machine learning. AI is set to unleash the next phase of the digital disruption and the market participants are preparing themselves for it. The investment in the technology is growing rapidly, dominated by the tech giants such as Google, Microsoft, IBM, AWS, and Baidu.

During my first project in McKinsey in 2011, I served the CEO of a bank regarding his small business strategy. I wanted to run a linear regression on the bank's data but my boss told me: "Don't do it. Artificial Intelligence is the most general-purpose technology of our time. New products and processes are being developed thanks to better vision systems, speech recognition technologies or recommendation engines based on Machine Learning. In fact, most recent advances in Artificial Intelligence have been achieved in the area of Machine Learning. Long before McKinsey, in 2004, I started my career as a mobile software developer. At that time I had to write precise instructions for every step of my code. Developing the voice recognition system of today's phones would have been tedious and error-prone back then. It would have required literally hundreds of thousands of detailed instructions to codify every single step, including identifying phonemes from sound waves, grouping them into ...

Alibaba Holding Group Ltd. has teamed up with SenseTime Group Ltd., the world's most valuable artificial intelligence startup, to set up a new artificial intelligence lab in Hong Kong in a bid to promote AI innovation between mainland China and the special administrative region. The Hong Kong Artificial Intelligence Laboratory is the first major move following the Chinese government's announcement of new policies to develop Hong Kong into a global innovation base, online news journal Leiphone reported. Government-backed tech incubator Hong Kong Science and Technology Parks Corp. is also a partner in the new project. The HKAI Lab will support local research talent and share more of the mainland's smart computing technologies and application scenarios with them to provide a platform for interdisciplinary exchange. It kicked off operations by launching an accelerator program designed to cultivate more AI startups in Hong Kong.

Policy evaluation, commonly referred to as value function approximation, is an important and central part in many reinforcement learning (RL) algorithms [27], whose task is to estimate value functions for a fixed policy in a discounted Markov Decision Process (MDP) environment. The value function of each state specifies the accumulated reward an agent would receive in the future by following the fixed policy from that state. Value functions have been widely investigated in RL applications, and it can provide insightful and important information for the agent to obtain an optimal policy, such as important board configurations in Go [24], failure probabilities of large telecommunication networks [9], taxi-out times at large airports [2] and so on. Despite the value functions can be approximated by different ways, the simplest form, linear approximations, are still widely adopted and studied due to their good generalization abilities, relatively efficient computation and solid theoretical guarantees[27, 7, 13, 16]. Temporal Difference (TD) learning is a common approach to this policy evaluation with linear function approximation problem[27]. These typical TD algorithms can be divided into two categories: gradient based methods (e.g., GTD(λ) [28]) and least-square (LS) based methods (e.g., LSTD(λ)[4]). A good survey on these algorithms can be found in [17, 6, 12, 7, 13]. 1 As the development of information technologies, high-dimensional data is widely seen in RL applications [26, 30, 23], which brings serious challenges to design scalable and computationally efficient algorithms for the linear value function approximation problem. To address this practical issue, several approaches have been developed for efficient and effective value function approximation.

Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building. This helps to eliminate not so important factors from the model to minimize noise coming from the features, which does not contribute significantly enough to explain the behavior of the dependent feature, which we want to predict. Personalized Influence Estimation is a technique introduced in this paper, which can estimate key factor influence for individual observations, which contribute most for each observations behavior pattern based on the dependent class or estimate. Observations can come from multiple business problem i.e. customers related to satisfaction study, customer related to Fraud Detection, network devices for Fault detection etc. It is highly important to understand the cause of issue at each observation level to take appropriate Individualized action at customer level or device level etc. This technique is based on joint behavior of the feature dimension for the specific observation, and relative importance of the feature to estimate impact. The technique mentioned in this paper is aimed to help organizations to understand each respondents or observations individual key contributing factor of Influence. Result of the experiment is really encouraging and able to justify key reasons for churn for majority of the sample appropriately

Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although massive maps are available in the digital era, how to effectively and accurately access the required map remains a challenge today. Previous works partially related to map-type classification mainly focused on map comparison and map matching at the local scale. The features derived from local map areas might be insufficient to characterize map content. To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic map, terrain map, physical map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map, and land cover classification map. Experimental results show that the state-of-the-art deep convolutional neural networks can support automatic map-type classification. Additionally, the classification accuracy varies according to different map-types. We hope our work can contribute to the implementation of deep learning techniques in cartographical community and advance the progress of Geographical Artificial Intelligence (GeoAI).

Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper

Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5).

China's Baidu Inc. has partnered with a Guangdong-based furniture maker and an artificial intelligence start-up to launch an artificial intelligence-based home package retailer and the team kicked off operations with the opening of eight experience-based smart home stores. Yihua Lifestyle Technology Co., which makes flooring and furniture, and Beijing-based Xiaocong Smart Technology Co., which creates smart appliances, joined Baidu to open the Yihua retail centers in Beijing, Shanghai, and Zhengzhou in Henan province, reported tech news website DoNews. The parties plan to cooperate further to open 18 similar experience halls nationwide this year and in the future, create 300 outlets that combine real estate, elderly care, and community lifestyle stores to install smart home solutions in over 30,000 apartments. Under the deal, Baidu provides its DuerOS digital assistant as the control center for the smart home, while Xiaocong brings in connected home equipment such as lighting, sockets, curtains and door locks. Yihua provides the display venues.