Big Data: Overviews


Artificial Intelligence In The Workplace: How AI Is Transforming Your Employee Experience 7wData

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Soon, even those of us who don't happen to work for technology companies (although as every company moves towards becoming a tech company, that will be increasingly few of us) will find AI-enabled machines increasingly present as we go about our day-to-day activities. From how we are recruited and on-boarded to how we go about on-the-job training, personal development and eventually passing on our skills and experience to those who follow in our footsteps, AI technology will play an increasingly prominent role. Here's an overview of some of the recent advances made in businesses that are currently on the cutting-edge of the AI revolution, and are likely to be increasingly adopted by others seeking to capitalize on the arrival of smart machines. Before we even set foot in a new workplace, it could soon be a fact that AI-enabled machines have played their part in ensuring we're the right person for the job. AI pre-screening of candidates before inviting the most suitable in for interviews is an increasingly common practice at large companies that make thousands of hires each year, and sometimes attract millions of applicants.


Global Big Data Conference

#artificialintelligence

Artificial intelligence (AI) is quickly changing just about every aspect of how we live our lives, and our working lives certainly aren't exempt from this. Soon, even those of us who don't happen to work for technology companies (although as every company moves towards becoming a tech company, that will be increasingly few of us) will find AI-enabled machines increasingly present as we go about our day-to-day activities. From how we are recruited and on-boarded to how we go about on-the-job training, personal development and eventually passing on our skills and experience to those who follow in our footsteps, AI technology will play an increasingly prominent role. Here's an overview of some of the recent advances made in businesses that are currently on the cutting-edge of the AI revolution, and are likely to be increasingly adopted by others seeking to capitalize on the arrival of smart machines. Before we even set foot in a new workplace, it could soon be a fact that AI-enabled machines have played their part in ensuring we're the right person for the job.


Adaptive Model Selection Framework: An Application to Airline Pricing

arXiv.org Machine Learning

Multiple machine learning and prediction models are often used for the same prediction or recommendation task. In our recent work, where we develop and deploy airline ancillary pricing models in an online setting, we found that among multiple pricing models developed, no one model clearly dominates other models for all incoming customer requests. Thus, as algorithm designers, we face an exploration - exploitation dilemma. In this work, we introduce an adaptive meta-decision framework that uses Thompson sampling, a popular multi-armed bandit solution method, to route customer requests to various pricing models based on their online performance. We show that this adaptive approach outperform a uniformly random selection policy by improving the expected revenue per offer by 43% and conversion score by 58% in an offline simulation.


How AI and Big Data are Improving Research Results Qualtrics

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Market research is a $44.5 B market and growing. Online research is among the fastest growing parts of the market thanks to the pervasiveness of the web and the ease with which we can now collect data. However, as the world conducts more and more survey research, the issues that we see elsewhere with big data are now affecting the survey research industry as well, specifically the issue of data quality. Thanks to the growth in online survey research, billions of survey responses are collected every year. But 1/4th of those responses are of poor quality[1].


Taking the pulse of machine learning adoption ZDNet

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A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Big data in GIS environment - Geospatial World

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GIS is virtual world, a world that is represented by points, polygon, line and graph. Processing of these datasets has always been a challenge since the day GIS got established as a field. Processing of huge data has always been a long standing problem not only in traditional Information and Technology(IT) sectors but also in the Geo-Spatial domain. However recent development in the both hardware and software infrastructure has enabled processing of huge data sets. This has given big push and new direction to those industries which were marred by slow data processing capabilities.


Contextual Bandits with Cross-learning

arXiv.org Machine Learning

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $a$ to perform, and receives some reward $r_{a,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{a,t}(c)$, the learner also learns the values of $r_{a,t}(c')$ for all other contexts $c'$; i.e., the rewards that would have been achieved by performing that action under different contexts. This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions (in this setting the context is the decision maker's private valuation for each auction). We call this problem the contextual bandits problem with cross-learning. The best algorithms for the classical contextual bandits problem achieve $\tilde{O}(\sqrt{CKT})$ regret against all stationary policies, where $C$ is the number of contexts, $K$ the number of actions, and $T$ the number of rounds. We demonstrate algorithms for the contextual bandits problem with cross-learning that remove the dependence on $C$ and achieve regret $O(\sqrt{KT})$ (when contexts are stochastic with known distribution), $\tilde{O}(K^{1/3}T^{2/3})$ (when contexts are stochastic with unknown distribution), and $\tilde{O}(\sqrt{KT})$ (when contexts are adversarial but rewards are stochastic).


Artificial Intelligence, Machine Learning and Big Data - A Comprehensive Report

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Artificial Intelligence and Machine Learning are the hottest jobs in the industry right now. For instance, did you know that more than 50,000 positions related to Data and Analytics are currently vacant in India? We are excited to release a comprehensive report together with Great Learning on how AI, ML and Big Data are changing and evolving the world around us. Additionally, this report aims to provide an overview of the kind of career opportunities available in these fields right now, and the different roles we might see in the future. The aim behind creating this report is to provide our Data Science community with the context of changes happening at a macro level, and how they can best prepare for these upcoming changes.


Taking the pulse of machine learning adoption ZDNet

#artificialintelligence

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.


Taking the pulse of machine learning adoption

ZDNet

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.