Object detection problems pose several unique obstacles beyond what is required for image classification. Five such challenges are reviewed in this post along with researchers' efforts to overcome these complications. The field of computer vision has experienced substantial progress recently owing largely to advances in deep learning, specifically convolutional neural nets (CNNs). Image classification, where a computer classifies or assigns labels to an image based on its content, can often see great results simply by leveraging pre-trained neural nets and fine-tuning the last few throughput layers. Classifying and finding an unknown number of individual objects within an image, however, was considered an extremely difficult problem only a few years ago.

As one of the leading logistics companies worldwide, FedEx is well-regarded in the supply chain industry. FedEx operates a portfolio of solutions; FedEx Express, FedEx Ground, FedEx Freight, FedEx Services, FedEx Logistics and FedEx Office. The company enables businesses to access over 99% of the world's GDP

One of the disruptive technologies that has gained increasingly more attention after the turn of the century is Machine Learning. Machine Leaning – closely related and usually considered as a subfield of Artificial Intelligence (AI) – is the process of automatic detection of usable patterns within data. The detection of these patterns is performed with the help of machine learning algorithms which are specifically tailored to deal with complex and large data sets. Such powerful algorithms have the potential of drastically revolutionizing the way of doing business and how businesses operate. With this article I will provide an overview of opportunities that machine learning algorithms and Artificial Intelligence (AI) pose to the business environment.

Prec ision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace o f the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological pers pective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses ho w other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide - s cale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.

This document explores the mathematics of deep convolutional neural networks. We begin at the level of an individual neuron, and from there examine parameter tuning, fully-connected networks, error minimization, back-propagation, convolutional networks, and finally deep networks. The report concludes with experiments on geometric invariance, and data augmentation. Relevant MATLAB code is provided throughout, and a downloadable package is available at the end of the document. Artificial neural networks (ANNs) [1] are at the core of state-of-the-art approaches to a variety of visual recognition tasks, including image classification [2] and object detection [3]. For a computer vision researcher interested in recognition, it is useful to understand how ANNs work, and why they have recently become so effective. An artificial neural network is a type of biologically-inspired pattern recognizer.

The excitement and activity surrounding the field of machine learning was clearly evident at the 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), which took place this summer in Valencia, Spain. Over 25 minisymposia--as well as several prize lectures and invited talks--touched on the theme of "learning," while other invited presentations addressed important mathematical research challenges necessary to advance the field. Panelists Hans De Sterck (University of Waterloo), Gitta Kutyniok (Technische Universität Berlin), James Nagy (Emory University), and Eitan Tadmor (University of Maryland, College Park) represented various core areas of computational and applied mathematics that develop and utilize machine learning techniques, including computational science and engineering, imaging science, linear algebra, and partial differential equations. Discussion broached a variety of issues surrounding machine learning, such as the obvious fact that machine learning will remain, as mathematician Ali Rahimi stated, "an area comparable to alchemy" without new mathematical understanding and developments. Deep learning is among the most transformative technologies of our time, and its many potential applications--from driverless cars to drug discovery--can have tremendous societal impact.

Below please find a short recap and an outlook for our next conference on September 6, 2018. The aim of this conference was to bring together European academics, young researchers, students and industrial practitioners to discuss the application of Artificial Intelligence to various practical fields. In a broader context, we wanted to promote «Mathematics for Industry» in Switzerland, as part of the European COST (Cooperation in Science and Technology) Action "Mathematics for Industry", where members of ZHAW are in the management committee for Switzerland. COST is the longest-running European framework supporting transnational cooperation among researchers, engineers and scholars across Europe. The 1st European COST Conference in Switzerland on this topic was held on September 15, 2016.

Webinar Overview & Objectives Artificial intelligence is the most transformative technology ever created by humankind and touches almost all aspects of our lives. But, as with all powerful tools, misapplication can cause real harm. When used correctly, AI offers tremendous benefits to candidates and organizations by creating a more fundamentally fair and personal hiring process that leads to greater job fit and satisfaction. To realize these benefits, AI must be deployed according to scientific principles and standards that ensure ethical application and practice. Attend this webinar to learn: - Hazards of using AI without rigorous oversight - Benefits AI offers talent acquisition leaders and their candidates - Standards for using AI in hiring to maximize benefit and eliminate risk AI is a revolutionary technology that has vast potential to benefit humankind, but the unintended consequences of its improper use can be lasting and destructive.

The current paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with the increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. Since on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc), covering such a large number of topics in a single survey is impractical. Instead, this survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.

The use of artificial intelligence (AI), cognitive technologies, and robotics to automate and augment work is on the rise, prompting the redesign of jobs in a growing number of domains. The jobs of today are more machine-powered and data-driven than in the past, and they also require more human skills in problem-solving, communication, interpretation, and design. As machines take over repeatable tasks and the work people do becomes less routine, many jobs will rapidly evolve into what we call "superjobs"--the newest job category that changes the landscape of how organizations think about work. During the last few years, many have been alarmed by studies predicting that AI and robotics will do away with jobs. In 2019, this topic remains very much a concern among our Global Human Capital Trends survey respondents.