Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Country: China Funding: $1.6B SenseTime develops face recognition technology that can be applied to payment and picture analysis, which could be used, for instance, on bank card verification and security systems. Country: China Funding: $607M Megvii develops Face Cognitive Services - a platform offering computer vision technologies that enable your applications to read and understand the world better. Face allows you to easily add leading, deep learning-based image analysis recognition technologies into your applications, with simple and powerful APIs and SDKs.
Edition: 6; Released: February 2022 Executive Pool: 133782 Companies: 202 - Players covered include ABB; Alphabet Inc. (Google Inc.); Amazon; Asustek Computer; Blue Frog Robotics; Bsh HausgerÃ¤te; Fanuc; Hanson Robotics; Harman International Industries; IBM Corporation; Intel Corporation; Jibo; Kuka; LG; Mayfield Robotics; Microsoft Corporation; Neurala; Nvidia; Promobot; Softbank; Xilinx and Others. Coverage: All major geographies and key segments Segments: Component (Software, Hardware); Robot Type (Service, Industrial); Application (Military & Defense, Law Enforcement, Personal Assistance & Caregiving, Public Relations, Education & Entertainment, Industrial, Stock Management, Other Applications) Geographies: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World. Complimentary Project Preview - This is an ongoing global program. Preview our research program before you make a purchase decision. We are offering a complimentary access to qualified executives driving strategy, business development, sales & marketing, and product management roles at featured companies.
This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.
Sen, Jaydip, Mehtab, Sidra, Sen, Rajdeep, Dutta, Abhishek, Kherwa, Pooja, Ahmed, Saheel, Berry, Pranay, Khurana, Sahil, Singh, Sonali, Cadotte, David W. W, Anderson, David W., Ost, Kalum J., Akinbo, Racheal S., Daramola, Oladunni A., Lainjo, Bongs
Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.
Recently, graph neural networks have become a hot topic in machine learning community. This paper presents a Scopus based bibliometric overview of the GNNs research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.
Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that ``overly-close'' user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.
Iakovidis, D. K., Ooi, M., Kuang, Y. C., Demidenko, S., Shestakov, A., Sinitsin, V., Henry, M., Sciacchitano, A., Discetti, A., Donati, S., Norgia, M., Menychtas, A., Maglogiannis, I., Wriessnegger, S. C., Chacon, L. A. Barradas, Dimas, G., Filos, D., Aletras, A. H., Töger, J., Dong, F., Ren, S., Uhl, A., Paziewski, J., Geng, J., Fioranelli, F., Narayanan, R. M., Fernandez, C., Stiller, C., Malamousi, K., Kamnis, S., Delibasis, K., Wang, D., Zhang, J., Gao, R. X.
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs.
Results released June 16, 2021 – Pew Research Center and Elon University's Imagining the Internet Center asked experts where they thought efforts aimed at ethical artificial intelligence design would stand in the year 2030. Some 602 technology innovators, developers, business and policy leaders, researchers and activists responded to this specific question. The Question – Regarding the application of AI Ethics by 2030: In recent years, there have been scores of convenings and even more papers generated proposing ethical frameworks for the application of artificial intelligence (AI). They cover a host of issues including transparency, justice and fairness, privacy, freedom and human autonomy, beneficence and non-maleficence, freedom, trust, sustainability and dignity. Our questions here seek your predictions about the possibilities for such efforts. By 2030, will most of the AI systems being used by organizations of all sorts employ ethical principles focused primarily on the public ...
"When you start coding, it makes you feel smart in itself, like you're in the Matrix [film]," says Janine Luk, a 26 year-old software engineer who works in London. Born in Hong Kong, she started her career in yacht marketing in the south of France but found it "a bit repetitive and superficial". So, she started teaching herself to code after work, followed by a 15-week coding boot camp. On the boot camp's last day, she applied for a job at cyber-security software company, Avast. And started there a week later.