A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defined as the probability that it beats a randomly chosen other item. Finding an exact ranking typically requires a prohibitively large number of comparisons, but in practice, approximate rankings are often adequate. Accordingly, we study the problem of finding approximate rankings from pairwise comparisons. We analyze an active ranking algorithm that counts the number of comparisons won, and decides whether to stop or which pair of items to compare next, based on confidence intervals computed from the data collected in previous steps. We show that this algorithm succeeds in recovering approximate rankings using a number of comparisons that is close to optimal up to logarithmic factors. We also present numerical results, showing that in practice, approximation can drastically reduce the number of comparisons required to estimate a ranking.

Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconscious biases about membership in particular groups can have an effect on their decision-making, even when they have no deliberate intention to discriminate against members of these groups. A growing body of experimental work has pointed to the effect that implicit bias can have in producing adverse outcomes. Here we propose a theoretical model for studying the effects of implicit bias on selection decisions, and a way of analyzing possible procedural remedies for implicit bias within this model. A canonical situation represented by our model is a hiring setting: a recruiting committee is trying to choose a set of finalists to interview among the applicants for a job, evaluating these applicants based on their future potential, but their estimates of potential are skewed by implicit bias against members of one group. In this model, we show that measures such as the Rooney Rule, a requirement that at least one of the finalists be chosen from the affected group, can not only improve the representation of this affected group, but also lead to higher payoffs in absolute terms for the organization performing the recruiting. However, identifying the conditions under which such measures can lead to improved payoffs involves subtle trade-offs between the extent of the bias and the underlying distribution of applicant characteristics, leading to novel theoretical questions about order statistics in the presence of probabilistic side information.

We call this a human/computer learning network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both and thereby continually improves the effectiveness of the network as a whole. In this article we explore how human/computer learning networks can leverage the contributions of human observers and process their contributed data with artificial intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts. For example, projects such as Galaxy Zoo, eBird, and FoldIt demonstrate the power of engaging the public in the investigation of a variety of large-scale scientific problems. These and similar projects leverage emerging techniques that integrate the speed and scalability of mechanical computation, using advances in artificial intelligence (AI), with the real intelligence of human computation to solve computational problems that are beyond the scope of existing algorithms (Law and von Ahn 2011). Human computational systems use the innate abilities of humans to solve certain problems that computers cannot solve (Man-Ching, Ling-Jyh, and King 2009).

This system has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint-programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011, and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources.

One of the consequences of the growth in AI research in South Africa in recent years is the establishment of a number of research hubs involved in AI activities ranging from mobile robotics and computational intelligence to knowledge representation and reasoning and human language technologies. In this survey we take the reader through a quick tour of the research being conducted at these hubs and touch on an initiative to maintain and extend the current level of interest in AI research in the country. Despite a peaceful transition to a democratic dispensation in 1994, South Africa is still struggling to achieve the goal of providing decent basic education to the majority of its citizens. The lack of quality education on the primary and secondary levels also serves as a barrier to obtaining tertiary-level education. According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country.

In this post we are going to develop a Cat&Dog Recognizer Java Application using deeplearning4j.If you would like to experiment on your own cat or dog feel free to check out the source code or download the application(fairly short instructions at the end). Although with the great progress of deep learning, computer vision problems tend to be hard to solve. One of the reason is because Neural Networks(NN) are trying to learn a highly complex function like Image Recognition or Image Object Detection. We have a bunch of pixels values and from there we would like to figure out what is inside, so this really is a complex problem on his own. Another reason why even today Computer Vision struggle is the amount of date we have.

We hope the new year is off to a great start. If you're like us, chances are you made the resolution to learn something new this year. To help you do so, we've put together a collection of 5 learning resources that we value. For those of you that haven't committed to resolutions for 2018, hopefully, the resources below will inspire you to better yourself. From learning data science, to managing your personal finance, here are 5 cross-discipline learning tools.

Azure Machine Learning services (preview) is an integrated, end-to-end data science and advanced analytics solution. It helps professional data scientists to prepare data, develop experiments, and deploy models at cloud scale. This Quickstart shows you how to create experimentation and model management accounts in Azure Machine Learning Preview. It also shows you how to install the Azure Machine Learning Workbench desktop application and CLI tools. Next, you take a quick tour of Azure Machine Learning Preview features by using the Iris flower dataset to build a model that predicts the type of iris based on some of its physical characteristics.

In the past several years, we've witnessed how deep learning, specifically convolutional neural networks, has been successfully applied to computer vision, natural language processing, speech recognition, logistics, online advertising, and many other problem domains. There are a few things that are unique about the application of deep learning to computer vision and understanding these characteristics will help in understanding the state of computer vision. In this article, I'd like to share a nice summary of the state of computer vision from Course 4 "Convolutional Neural Networks" from the new Deep Learning Specialization series on Coursera. Dr. Andrew Ng provides some compelling observations about deep learning and computer vision with the goal of mapping out the future of this increasingly popular technology. Consider that many machine learning problems fall somewhere on the spectrum between where you're working with "small data" to where you have "big data." For example, there is a decent amount of data available for speech recognition.

The future of higher education is intrinsically linked with developments on new technologies and computing capacities of the new intelligent machines. In this field, advances in artificial intelligence open to new possibilities and challenges for teaching and learning in higher education, with the potential to fundamentally change governance and the internal architecture of institutions of higher education. With answers to the question of'what is artificial intelligence' shaped by philosophical positions taken since Aristotle, there is little agreement on an ultimate definition. In 1950s, Alan Turing proposed a solution to the question of when a system designed by a human is'intelligent.' Turing proposed the imitation game, a test that involves the capacity of a human listener to make the distinction of a conversation with a machine or another human; if this distinction is not detected, we can admit that we have an intelligent system, or artificial intelligence (AI).