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Data Science Demystified: The Data Modeling Proposition - insideBIGDATA

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Data science is largely an enigma to the enterprise. Although there's an array of self-service options to automate its various processes, the actual work performed by data scientists (and how it's achieved) is still a mystery to your average business user or C-level executive. Data modeling is the foundation of this discipline that's responsible for the adaptive, predictive analytics that are so critical to the current data ecosystem. Before data scientists can refine cognitive computing models or build applications with them to solve specific business problems, they must rectify differences in data models to leverage different types of data for a single use case. Since statistical Artificial Intelligence deployments like machine learning intrinsically require huge data quantities from diverse sources for optimum results, simply getting such heterogeneous data to conform to a homogenous data model has been one of the most time-honored--and time consuming--tasks in data science.


How to Build an AI in a Day?

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AI systems are thought to be very complex. Some of the popular ones have been in the works for more than a decade. But, AI systems can actually be very simple as well. To elaborate on #2 vs. #3, let's say you are designing a system to predict whether an apple is ripe or not. You have designed a probe that takes the apple through some spectral analysis and gives a result.


Custom DU: A Web-Based Business User-Driven Automated Underwriting System

AI Magazine

Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules.


Amazon A2I is now generally available Amazon Web Services

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AWS is excited to announce the general availability of Amazon Augmented AI (Amazon A2I), a new service that makes it easy to implement human reviews of machine learning (ML) predictions at scale. Amazon A2I removes the undifferentiated heavy lifting associated with building and managing expensive and complex human review systems, so you can ensure your ML models produce accurate predictions. Amazon A2I enables humans and machines to do what they do best by easily inserting human judgment into the ML pipeline. Amazon A2I provides built-in human review workflows for common ML tasks such as content moderation and text extraction from documents, in combination with Amazon Rekognition and Amazon Textract. You can also create your own human review workflows for ML models built with Amazon SageMaker or with any on-premises or cloud tools via its API.


RPA Machine Learning Intelligent Automation - DZone AI

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Robotic process automation has generated a lot of buzz across many different industries. As businesses focus on digital innovation, automation of repetitive tasks to increase efficiency while decreasing human errors is an attractive proposition. Robots will not tire, will not get bored, and will perform tasks accurately to help their human counterparts improve productivity and free them up to focus on higher level tasks. Beyond simple RPA, intelligent automation can be achieved by integrating machine learning and artificial intelligence with robotic process automation to achieve automation of repetitive tasks with an additional layer of human-like perception and prediction. By design, RPA is not meant to replicate human-like intelligence.


Combating Insurance Fraud With Machine Learning Fintech Finance

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Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules. Machine learning has the potential to allow insurers to move from the current state of "detect and react" to "predict and prevent." It excels at automating the process of taking large volumes of data, analysing multiple fraud indicators in parallel – which taken individually may often be quite normal – and finding potential fraud. Generally, there are two ways to teach or train a machine learning algorithm, which depend on the available data: supervised and unsupervised learning.


Big Data Quotes of the Week - Dec. 18, 2019

#artificialintelligence

"Leaders don't understand what adoption of AI means. Many companies feel pressured to adopt AI by any means necessary -- without thinking through the why and how ... The metaphor that comes to mind is a fish lured to the next shiny bauble, only to realize too late that the hook will be its last meal." "People with disabilities constitute an untapped pool of critically skilled talent. AI, augmented reality (AR), virtual reality (VR) and other emerging technologies have made work more accessible for employees with disabilities." "The emergence of AI has prompted a fierce debate in English education over whether a knowledge-based curriculum is still appropriate for children who will potentially have to compete with robots and other AI technology in a future jobs market."


How to integrate robotic process automation in big data projects

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Information Services Group (ISG) reported in 2018 that 92% of companies were aiming to adopt robotic process automation (RPA) by 2020 because they wanted to increase operational efficiencies. This large number reflects how eager companies are to automate routine business processes. One of the easiest places to employ RPA is in very simple, highly repetitive business processes that rely on transactional data that comes in fixed record lengths, with data fields always in the same locations. This data is highly predictable, and automation tools like RPA that depend on recognizing repetitive data patterns are in strong positions to excel. However, even the most routine business process consists of unstructured and semi-structured big data, as well as the more traditional fixed record data.


Beyond microservices; Software architecture driven by machine learning

#artificialintelligence

It's not a question of if, it's a question of when and how AI and machine learning will change our programming and software development paradigms. Today's coding models are based on data storage, business logic, services, UX, and presentation. A full stack developer elects to build a three-tiered web architecture using an MVC framework. An IoT application calls for an event-driven architecture with services processing events and broadcasting state changes. These two architecture paradigms converge with microservice architectures where user interfaces are just one type of interaction node fulfilling high level functions by interfacing with many services.


Beyond microservices; Software architecture driven by machine learning

#artificialintelligence

It's not a question of if, it's a question of when and how AI and machine learning will change our programming and software development paradigms. Today's coding models are based on data storage, business logic, services, UX, and presentation. A full stack developer elects to build a three-tiered web architecture using an MVC framework. An IoT application calls for an event-driven architecture with services processing events and broadcasting state changes. These two architecture paradigms converge with microservice architectures where user interfaces are just one type of interaction node fulfilling high level functions by interfacing with many services.