But as your data scientists and data engineers quickly realize, building a production AI system is a lot easier said than done, and there are many steps to master before you get that ML magic. At a high level, the anatomy of AI is fairly simple. You start with some data, train a machine learning model upon it, and then position the model to infer on real-world data. Unfortunately, as the old saying goes, the devil is in the details. And in the case of AI, there are a lot of small details you have to get right before you can claim victory.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
As machine learning is applied more and more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" -- brittle, ad hoc ML systems. To address these problems, we introduce the Machine Learning Bazaar, a new approach to developing machine learning and AutoML software systems. First, we introduce ML primitives, a unified API and specification for data processing and ML components from different software libraries. Next, we compose primitives into usable ML programs, abstracting away glue code, data flow, and data storage. We further pair these programs with a hierarchy of search strategies -- Bayesian optimization and bandit learning. Finally, we create and describe a general-purpose, multi-task, end-to-end AutoML system that provides solutions to a variety of ML problem types (classification, regression, anomaly detection, graph matching, etc.) and data modalities (image, text, graph, tabular, relational, etc.). We both evaluate our approach on a curated collection of 431 real-world ML tasks and search millions of pipelines, and also demonstrate real-world use cases and case studies.
The USA has been the evergreen and most popular study abroad destination for Indian and international students. According to the data from the United Nations Educational, Scientific and Cultural Organization, 64% Indian students (who go abroad for higher studies) go to the US for graduate studies (MS and Ph.D.); whereas, only 12% go to the US for undergraduate studies. The reasons are very straightforward – presence of world-class and top ranked universities, flexible study system, exciting specializations, research, and innovation oriented curriculum, and funding options. When it comes to the subjects or majors, the STEM fields are always the preferred ones. However, business & management studies and liberal arts are also not too far behind.
Distributed denial of service protection firm Radware Ltd. has acquired cloud-based security firm Seculert for an undisclosed sum. Founded in 2012, Seculert offers a software-as-a-service security platform that aims to fill gaps left by legacy perimeter defense and breach detection systems. It claims to protect enterprises from advanced threats by focusing on malicious outbound network traffic. The company's platform combines big data analytics, machine learning technology and behavioral analysis to provide visibility on the final two stages of the malware kill chain. Seculert's Javelin service is claimed to be the first inside-out attack simulation and remediation service that allows enterprises to determine how well their secure web gateway, next-generation firewalls or proxy would do at preventing real world malicious malware attacks from succeeding in communicating with their perpetrator's command and control servers.