The report published on the global Artificial Intelligence Processor market is a comprehensive market study that focuses on the key players and key markets. The growth opportunities regarding this market as well as the future forecast and the status of the global Artificial Intelligence Processor market have been presented by this report. The market has been analyzed on the basis of the market value from the year 2020 to the year 2026. This study also includes an analysis of consumption, value, production and capacity. With the key manufacturers of the products in the market covered, the report presents its development plans for the future.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

An important aspect of urban planning is understanding crowd levels at various locations, which typically require the use of physical sensors. Such sensors are potentially costly and time consuming to implement on a large scale. To address this issue, we utilize publicly available social media datasets and use them as the basis for two urban sensing problems, namely event detection and crowd level prediction. One main contribution of this work is our collected dataset from Twitter and Flickr, alongside ground truth events. We demonstrate the usefulness of this dataset with two preliminary supervised learning approaches: firstly, a series of neural network models to determine if a social media post is related to an event and secondly a regression model using social media post counts to predict actual crowd levels. We discuss preliminary results from these tasks and highlight some challenges.

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression samples. Further, unlike existing studies that focus on learning depression signs from static data without considering temporal dynamics, we focus on longitudinal data because i) temporal changes in personal development and family environment can provide critical cues for psychiatric disorders and ii) it may enable us to predict depression before the illness actually occurs. Extensive experimental results on child depression data show that our model is able to i) achieve nearly perfect performance in depression detection and ii) accurately predict depression 2-4 years before the clinical diagnosis, substantially outperforming seven competing methods.

Robots such as Mitra are being used to reduce risk of infection for medical staff. Standing just 5ft tall, Mitra navigates around the hospital wards, guided by facial recognition technology and with a chest-mounted tablet that allows patients and their loved ones to see each other. Developed in recent years by the Bengaluru startup Invento Robotics, Mitra costs around $13,600 (£10,000) and – due to the reduced risk of infection to doctors – has become hugely popular in Indian hospitals during the pandemic. Since making headlines at its debut in 2017 at an international summit, where it greeted Ivanka Trump and interacted with India's prime minister Narendra Modi, Mitra has increasingly been put to use in hospitals treating Covid-19 patients. "Mitra was originally meant for care homes, but was adapted during the pandemic to assist doctors and nurses by taking vital readings, and to help in consultations," says Balaji Viswanathan, chief executive of Invento Robotics, which now exports the robot to five countries including the US and Australia.

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees' evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capacity on task-relevant dynamics, while simultaneously serving as an effective heuristic for planning with sparse rewards. We evaluate our method on challenging visual goal completion tasks and show a substantial increase in performance compared to prior model-free approaches. Deep reinforcement learning has proven to be a powerful and effective framework for solving a diversity of challenging decision-making problems (Silver et al., 2017a; Berner et al., 2019). However these algorithms are typically trained to maximize a single reward function, ignoring information that is not directly relevant to the associated task at hand. This way of learning is in stark contrast to how humans learn (Tenenbaum, 2018). Without being prompted by a specific task, humans can still explore their environment, practice achieving imaginary goals, and in so doing learn about the dynamics of the environment. When subsequently presented with a novel task, humans can utilize this learned knowledge to bootstrap learning -- a property we would like our artificial agents to have. In this work, we investigate one way to bridge this gap by learning world models (Ha & Schmidhuber, 2018) that enable the realization of previously unseen tasks. By modeling the task-agnostic dynamics of an environment, an agent can make predictions about how its own actions may affect the environment state without the need for additional samples from the environment. Prior work has shown that by using powerful function approximators to model environment dynamics, training an agent entirely within its own world models can result in large gains in sample efficiency (Ha & Schmidhuber, 2018).

HO CHI MINH CITY (Vietnam News/ANN): Vietnam should use artificial intelligence in more areas to boost productivity and to improve people's lives, experts have said. Deputy Minister of Science and Technology Bùi Thế Duy told a conference last Friday (Nov 27) that AI had been receiving more and more attention in recent years, and, along with other modern technologies (such as big data and cloud computing), was changing people's lives and business activities. Covid-19 was creating a push for faster digital transformation, and companies and researchers should consider how using AI solutions could help a recovery after the pandemic, he said. Nguyễn Việt Dũng, director of the HCM City Department of Science and Technology, said the city was among the first in Vietnam to issue a programme to facilitate digital transformation, focusing on promoting AI usage. Dr Stefan Hajkowicz of the Commonwealth Scientific and Industrial Research Organisation said AI could be used in many areas, pointing to how Australia did so in agriculture, mining and aviation.

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods.

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation.