Information Technology: Overviews


Cutting edge: A peek into recent developments in sci-tech

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Dr Peter B Scott-Morgan has just turned from Peter 1.0 to Peter 2.0, to use his own term, and has become the world's first full Cyborg. He's real and you can see his posts on Twitter. Dr Morgan is a scientists who has a muscle wasting disease that has now taken its toll on his body. In other words, he is terminally ill with a motor neurone disease. As the muscles in his body lose their power completely, only his brain will be alive.


The paradigm shift implied by AI The Future Of

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Artificial Intelligence (AI) is probably one of the most misinterpreted technologies within the Industry 4.0 umbrella. On the one hand, as a primary subject of science-fiction, many surreal, dramatic, and romanticized forms of AI emerged through the years, blurring our understanding of what computer science is capable of nowadays. On the other hand, doomsday reports of economic and job losses caused by AI fill chronicles, forecasts, and editorial works of traditional and digital media. Unfortunately, the scope of this post prevents the author from adequately address those misunderstandings by providing a clear and thorough landscape for AI. And because of this polarizing focus, only a few are realizing that AI is inducing a paradigm shift in computing.


A 6 Step Field Guide for Building Machine Learning Projects

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A 6 Step Field Guide for Building Machine Learning Projects Have data and want to know how you can use machine learning with it? Sep 21 · 19 min read I listened to Korn's new album on repeat for 6-hours the other day and wrote out a list of things I think about when it comes to the modelling phase of machine learning projects. Thank you Sam Bourke for the photo. The media makes it sound like magic. Reading this article will change that. It will give you an overview of the most common types of problems machine learning can be used for. And at the same time give you a framework to approach your future machine learning proof of concept projects. How is machine learning, artificial intelligence and data science different? These three topics can be hard to understand because there are no formal definitions. Even after being a machine learning engineer for over a year, I don't have a good answer to this question. I'd be suspicious of anyone who claims they do. To avoid confusion, we'll keep it simple. For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event. The following steps have a bias towards building something and seeing how it works. You may start a project by collecting data, model it, realise the data you collected was poor, go back to collecting data, model it again, find a good model, deploy it, find it doesn't work, make another model, deploy it, find it doesn't work again, go back to data collection.


A guide to artificial intelligence in enterprise: Is it right for your business? - Software Contract Solutions

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While true artificial intelligence is some way off, businesses are taking advantage of intelligent automation, like machine learning, to improve business operations, drive innovation and improve the customer experience. AI and automation is changing the business environment across industries, delivering new opportunities through intelligent, automated products. Some companies are ahead of the curve, and others are stagnating in their adoption of the tech. Board members and decision-makers are increasingly aware of the benefits of AI and automation, but the question should always remain: 'Is it right for my business? How does it solve a problem?'.


What is Suptech? An Overview of this Rapidly Growing Space Fintech Schweiz Digital Finance News - FintechNewsCH

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In the same way technology is changing the financial services industry, technology is also changing how the industry and supervisory authorities implement and enforce regulations. Today, an increasing number of supervisory authorities are turning to technology to support their work, leveraging so-called supervisory technology (suptech) applications to digitize report and regulatory processes. Suptech refers to the use of innovative technology such as artificial intelligence (AI) and machine learning (ML) by supervisory agencies to support supervision. As with other regtech solutions, suptech is about improving efficiency through the use of automation, streamlining administrative and operational procedures, and digitalizing data and working tools. The main goal here is to reduce the burden on firms and allow for more proactive monitoring, better reporting, oversight and overall compliance on the regulator's side.


What is Suptech? An Overview of this Rapidly Growing Space Fintech Schweiz Digital Finance News - FintechNewsCH

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In the same way technology is changing the financial services industry, technology is also changing how the industry and supervisory authorities implement and enforce regulations. Today, an increasing number of supervisory authorities are turning to technology to support their work, leveraging so-called supervisory technology (suptech) applications to digitize report and regulatory processes. Suptech refers to the use of innovative technology such as artificial intelligence (AI) and machine learning (ML) by supervisory agencies to support supervision. As with other regtech solutions, suptech is about improving efficiency through the use of automation, streamlining administrative and operational procedures, and digitalizing data and working tools. The main goal here is to reduce the burden on firms and allow for more proactive monitoring, better reporting, oversight and overall compliance on the regulator's side.


Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

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The sustained success random forests has led naturally to the desire to better understand the statistical and mathematical properties of the procedure. Lin and Jeon (2006) introduced the potential nearest neighbor framework and Biau and Devroye (2010) later established related consistency properties. In the last several years, a number of important statistical properties of random forests have also been established whenever base learners are constructed with subsamples rather than bootstrap samples. Scornet et al. (2015) provided the first consistency result for Breiman's original random forest algorithm whenever the true underlying regression function is assumed to be additive. Despite the impressive volume of research from the past two decades and the exciting recent progress in establishing their statistical properties, a satisfying explanation for the sustained empirical success of random forests has yet to be provided.


Python Geospatial Development, 3rd Edition - Programmer Books

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Geospatial development links your data to locations on the surface of the Earth. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you'll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums, and coordinate systems. This book provides an overview of the major geospatial concepts, data sources, and toolkits. It starts by showing you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database.


Design Thinking: A framework for realizing true AI potential Petuum

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When AI first became technologically feasible, the only people who were trying to harness the true potential of AI were programmers and engineers with advanced degrees in mathematics. According to an article in the Harvard Business Review, AI has moved from the theoretical (what can AI do?) to the practical (what can AI do for me?). This is the age of "deployed AI," which is destined to impact everyone. After all, the range of AI applications is staggering. The possibilities are endless, but realizing that potential requires mainstream application of AI technology.


Global Big Data Conference

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As the rise of e-commerce continues, companies around the globe have become increasingly sensitive to evolving consumer preferences. In a world where instant gratification has come to represent a generation, autonomous technologies are set to make a significant impact. When it comes to consumer shipping, McKinsey reports that 25 percent of all consumers would pay a premium for same-day or instant delivery made possible by autonomous tech. However, this figure is likely to grow, given that 30 percent of younger consumers are willing to pay more for the same shipping options. As industry use cases continue to expand, many have come to define the ecosystem as the autonomous "last-mile."