The development of the Indian software industry is an archetype of how economic liberalization combined with an entrepreneurial spirit can build an industry that today contributes as much as 8% to the GDP of a fast-growing country like India. On the back of thousands of IT services companies that were built over the last three decades, the industry has generated US$177 billion in revenue and more than US$135 billion in exports in FY 2018–2019 alone. The IT industry has also created over four million direct jobs and 12 million indirect jobs in India. A testament to this growth is the fact that the largest Indian IT services company is currently valued at over US$100 billion and generates over US$20 billion in revenue. Over the years, the Indian software industry has matured from providing cost-effective back office support to driving the digital transformation agenda ahead in global companies. Increasingly, leaders of more than a thousand global enterprises across the U.S., Europe, and other locations have realized India's potential and have set up their own IT or R&D centers to take advantage of the vibrant Indian software ecosystem. The current wave of Indian software entrepreneurs is focusing on building platforms and products for Indian and global markets. This has led to the creation of more than 7,000 tech startups in India.

Walk into any one of the many start-up events organized across India, and inevitably the image of an Indian bazaar comes to mind: people rushing around, shouting, bargaining, answering phones with great excitement, laughing loudly, boasting, blushing, and generally being optimistic, as if they are at the beginning of a rising trend of well-being. Such optimism might seem justified. According to data compiled by Fortune magazine,a from just eight'unicorns' in 2015, the number of start-ups in India valued at more than $1 billion has grown to 26. What is interesting is that in 2018 alone, India added eight unicorns to the club. These include diverse entities such as Ola, started in India as a competitor to Uber and has since expanded its footprint into the U.K. (and is eyeing Australia); an insurance aggregator called PolicyBazaar; the e-commerce site Paytm Mall; an eyewear retailer called Lenskart; food technology aggregators such as Swiggy and Zomato, and hotel-room aggregators like OYO and FabHotels. Thousands of entrepreneurs start up every year and aspire to become one of the new unicorns.

There has been a phenomenal increase in the use of online social media (OSM) services in India, including Facebook, Twitter, Instagram, LinkedIn, and YouTube. In addition to these services, one-to-one messaging services like WhatsApp have 200 million users, the highest in the world. India has 462 million users accessing the Internet, among these: Facebook has 250 million users, LinkedIn 42 million, and Twitter 23 million users, and the majority of users access these services through their mobile phones. These services have had a profound impact in India--overall digital literacy has increased, people are more connected, dissemination of local language content has increased, information exchanged during crises is substantial, and more. The deep penetration of social media services also has negative effects--the propagation of false information and hate, an increase in spammers and phishers, users are losing social skills, and more.

In June, Amazon announced it was close to being able to offer for package deliveries by drone for its Prime Air service. That same month, Uber said it plans to test food delivery by aerial drone in crowded cities. And drone delivery company Flytrex already touts the ability to deliver drinks via unmanned vehicle on the golf course. Despite such announcements, drones are not crowding the skies over major cities and population centers just yet. But that may be about to change.

According to a report by multinational professional services provider KPMG, defense agencies have strong motivations to adopt a group of technologies, known collectively as intelligent automation. More accurately described, artificial intelligence (AI) in the military sector is more appropriately called intelligent automation. We will not create yet another acronym for this here (you're welcome). Given increasing volumes of data that requires fast, safe, and accurate analysis, Ian McDonald, Director of Technology Enablement in Defense and National Security, KPMG in Australia says, "Intelligent automation is absolutely essential for the military, They cannot operate the capabilities they currently have to their full potential without it." "Ballistic missile defense is a vital part of many countries' military activity. As many as a dozen satellite and sensor systems may be used in detecting hostile missile launches, with a further dozen systems involved in destroying such missiles. A defense agency could have just 8-10 minutes to decide whether a launch represents a threat, share findings with allies and decide what to do. The use of countermeasures has to happen quickly, given that missiles could impact 16 minutes after launch."

The customer engagement centre (CEC) and contact centre (CC) have been integrating in silos for decades, with limited sharing of customer interaction channel functionality and data. This has resulted in a fragmented customer experience (CX), leaving customers to guess which channel will yield the best and fastest answer, reports Gartner. The analyst firm says its latest Gartner Hype Cycle for Customer Service and Support Technologies describes the most critical technologies for supporting customers as they seek answers, advice and resolutions to problems, either through a variety of interaction channels or by enabling customer-facing employees to deliver resolution and advice. "Combining the formerly separate yet closely related Hype Cycle for CRM customer service and customer engagement and Hype Cycle for contact centre infrastructure, this new Hype Cycle encourages customer service and support leaders to combine CEC and CC systems to create a broader technology ecosystem," says Drew Kraus, vice president in Gartner's Customer Service & Support practice. "In doing so, they can leverage consistent analytics and knowledge tools for gathering, analysing and sharing critical information and recommendations to both customers and employees."

Estimating machine learning performance 'in the wild' is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence of classification models. Our contributions are twofold: First, we establish a statistically rigorous definition of competence that generalizes the common notion of classifier confidence; second, we present the ALICE (Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise competence estimator for any classifier. By considering distributional, data, and model uncertainty, ALICE empirically shows accurate competence estimation in common failure situations such as class-imbalanced datasets, out-of-distribution datasets, and poorly trained models. Our contributions allow us to accurately predict the competence of any classification model given any input and error function. We compare our score with state-of-the-art confidence estimators such as model confidence and Trust Score, and show significant improvements in competence prediction over these methods on datasets such as DIGITS, CIFAR10, and CIFAR100.

W e consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to all its child nodes. The IK problem has various applications in vision and graphics, for example for tracking or reconstructing articulated objects, such as human hands or bodies. Most commonly, the IK problem is tackled using local optimisation methods. A major downside of these approaches is that, due to the non-convex nature of the problem, such methods are prone to converge to unwanted local optima and therefore require a good initialisation. In this paper we propose a convex optimisation approach for the IK problem based on semidef-inite programming, which admits a polynomial-time algorithm that globally solves (a relaxation of) the IK problem. Experimentally, we demonstrate that the proposed method significantly outperforms local optimisation methods using different real-world skeletons.

Human beings are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed as \emph{Catastrophic Forgetting}, is one of the major roadblocks that prevent deep neural networks from achieving human-level artificial intelligence. Several research efforts, e.g. \emph{Lifelong} or \emph{Continual} learning algorithms, have been proposed to tackle this problem. However, they either suffer from an accumulating drop in performance as the task sequence grows longer, or require to store an excessive amount of model parameters for historical memory, or cannot obtain competitive performance on the new tasks. In this paper, we focus on the incremental multi-task image classification scenario. Inspired by the learning process of human students, where they usually decompose complex tasks into easier goals, we propose an adversarial feature alignment method to avoid catastrophic forgetting. In our design, both the low-level visual features and high-level semantic features serve as soft targets and guide the training process in multiple stages, which provide sufficient supervised information of the old tasks and help to reduce forgetting. Due to the knowledge distillation and regularization phenomenons, the proposed method gains even better performance than finetuning on the new tasks, which makes it stand out from other methods. Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.

Although response generation (RG) diversification for single-turn dialogs has been well developed, it is less investigated for natural multi-turn conversations. Besides, past work focused on diversifying responses without considering topic coherence to the context, producing uninformative replies. In this paper, we propose the Topic-coherent Hierarchical Recurrent Encoder-Decoder model (THRED) to diversify the generated responses without deviating the contextual topics for multi-turn conversations. In overall, we build a sequence-to-sequence net (Seq2Seq) to model multi-turn conversations. And then we resort to the latent Variable Hierarchical Recurrent Encoder-Decoder model (VHRED) to learn global contextual distribution of dialogs. Besides, we construct a dense topic matrix which implies word-level correlations of the conversation corpora. The topic matrix is used to learn local topic distribution of the contextual utterances. By incorporating both the global contextual distribution and the local topic distribution, THRED produces both diversified and topic-coherent replies. In addition, we propose an explicit metric (\emph{TopicDiv}) to measure the topic divergence between the post and generated response, and we also propose an overall metric combining the diversification metric (\emph{Distinct}) and \emph{TopicDiv}. We evaluate our model comparing with three baselines (Seq2Seq, HRED and VHRED) on two real-world corpora, respectively, and demonstrate its outstanding performance in both diversification and topic coherence.