The report on the AI (Artificial Intelligence) Chip industry provides an in-depth assessment of the AI (Artificial Intelligence) Chip market including technological advancements, market drivers, challenges, current and emerging trends, opportunities, threats, risks, strategic developments, product advancements, and other key features. The report covers market size estimation, share, growth rate, global position, and regional analysis of the market. The report also covers forecast estimations for investments in the AI (Artificial Intelligence) Chip industry from 2020 to 2027. The report is furnished with the latest market dynamics and economic scenario in regards to the COVID-19 pandemic. The pandemic has brought about drastic changes in the economy of the world and has affected several key segments and growth opportunities.
Market Study Report LLC has added a new report on Machine Learning as a Service Market Size that provides a comprehensive review of this industry with respect to the driving forces influencing the industry. Comprising the current and future trends defining the dynamics of this industry vertical, this report also incorporates the regional landscape of Machine Learning as a Service market in tandem with its competitive terrain. The Machine Learning as a Service market report provides a granular assessment of the business space, while elaborating on all the segments of this business space. The document offers key insights pertaining to the market players as well as their gross earnings. Moreover, details regarding the regional scope and the competitive scenario are entailed in the study.
The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.
Speech-to-text applications have never been so plentiful, popular or powerful, with researchers' pursuit of ever-better automatic speech recognition (ASR) system performance bearing fruit thanks to huge advances in machine learning technologies and the increasing availability of large speech datasets. Current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. However, a lack of transcribed audio data for the less widely spoken of the world's 7,000 languages and dialects makes it difficult to train robust speech recognition systems in this area. To help ASR development for such low-resource languages and dialects, Facebook AI researchers have open-sourced the new wav2vec 2.0 algorithm for self-supervised language learning. The paper Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations claims to "show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler." A Facebook AI tweet says the new algorithm can enable automatic speech recognition models with just 10 minutes of transcribed speech data.
This book comprehensively presents a novel approach to the systematic security hardening of software design models expressed in the standard UML language. It combines model-driven engineering and the aspect-oriented paradigm to integrate security practices into the early phases of the software development process. To this end, a UML profile has been developed for the specification of security hardening aspects on UML diagrams. In addition, a weaving framework, with the underlying theoretical foundations, has been designed for the systematic injection of security aspects into UML models. The work is organized as follows: chapter 1 presents an introduction to software security, model-driven engineering, UML and aspect-oriented technologies.
On the morning of November 9, 2016, the world woke up to the shocking outcome of the U.S. Presidential election: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots--automated social media accounts mimicking humans--played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory.16,19 In the aftermath of the 2016 U.S. elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects these malicious actors seem to have on our societies.27,29 This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times--during the run-up to the 2020 U.S. elections--the question appears as more crucial than ever. Particularly so, also in light of the recent reported tampering of the electoral debate by thousands of AI-powered accounts.a What struck social, political, and economic analysts after 2016--deception and automation--has been a matter of study for computer scientists since at least 2010. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other--more recent--forms of online deception, such as strategic information operations and political trolls.
Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different. Traditionally, reinforcement learning (RL) research takes place in grid-world-like settings or other 2D games, whereas children act in the real world which is rich and 3-dimensional.
This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.
As we see, Indian Government is totally focused to improve Indian technology and trying to make self reliance initiative more successful so there are a lot of announcement happening everyday. Recently, Tamil Nadu Chief Minister Edapaddi K Palaniswami has unveiled safe & Ethical Artificial Intelligence Policy 2020 during CII Connect 2020. The agenda is to provide a framework for inclusive, safe and ethical use of Artificial Intelligence (AI) in government domain. It also aims to build fairness, equity, transparency and trust in AI assisted decision making systems. The plan of Artificial Intelligence Policy 2020 is to build a mature and self-sustaining AI community to aid the growth of AI in the State and to train and skill people in AI, the policy document said.