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Investing in AI: Unlocking Profitable Machine Learning with Experiment Management

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We live in an age of rapid AI innovation and progress. Yet even as academics and researchers make astonishing advancements, demonstrating real business value and positive return on investment is challenging. Developing cutting edge AI applications based on machine learning models integrated with existing business software is a common challenge. This article discusses a few of the core pain points and strategies to address them. The first challenge most organizations encounter is the increased complexity of preparing data and dataset management.


6 tips for creating an AI Center of Excellence

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A Center of Excellence is "a team, a shared facility or an entity that provides leadership, best practices, research, support, and training for a focus area," and they are commonly used in healthcare to focus on specific problems or disciplines. I advocate that they can be used in organizations for artificial intelligence (AI) as well. What makes AI a strong candidate for a dedicated Center of Excellence is its rapidly expanding role as mission-critical technology in enterprises. Companies are finding that people in many different business units--not just data science or IT--want to be or are already involved with AI. In some cases, people are bringing in their own AI tools and solutions, but there is a need to orchestrate this buying to avoid waste.


Industry 4.0 and AI Best Practices - Connected World

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Here's an attention-grabbing idea: Deploying cellular-enabled Industry 4.0 solutions can generate a 10-20x operational cost-savings ROI (return on investment) over the course of five years. This is according to a joint research study from ABI Research and Ericsson. The research also suggests Industry 4.0 solutions can generate up to 8.5% in operational cost savings, which, for a factory or industrial site, can equate to an operational cost savings of up to $600 per square meter per year. Industry 4.0, also known as the fourth industrial revolution, is the idea that connectivity, automation technologies, and digitization are creating the fourth major revolution in the business of manufacturing. Thanks to trends like leveraging the IoT (Internet of Things), including wireless networking and sensors to collect machine data and enable predictive maintenance, as well as 3D printing, robots and cobots on the factory floor, machine learning and AI (artificial intelligence), 5G, and digital twins, among other trends, the Industry 4.0 market is projected by MarketsandMarkets to reach almost $157 billion by 2024. A big part of Industry 4.0 is the use of AI technologies to enable smarter machines that can take on tasks like self-monitoring and diagnosis autonomously.


140 Machine Learning Formulas

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Data Science Leadership Exchange: Best Practices for Driving Outcomes Despite an increasing awareness of the role data science plays in successful business outcomes, data science leaders still struggle to organize, implement and communicate effective data science initiatives. Some of the industry's best and brightest from Bayer, S&P Global and Transamerica will be presenting their insights and experiences. Data Science Leadership Exchange: Best Practices for Driving Outcomes Despite an increasing awareness of the role data science plays in successful business outcomes, data science leaders still struggle to organize, implement and communicate effective data science initiatives. Some of the industry's best and brightest from Bayer, S&P Global and Transamerica will be presenting their insights and experiences.


Full Stack Software Engineer - Swansea SA1 - Indeed.com

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Vortex IoT Ltd was established in 2018 and has gone from strength to strength. As a winner of several awards and recognised for the company's ability to cast a shadow over their competitors, Vortex IoT Ltd leads in its space due to its team of specialists with expertise in Software Development, Research & Development, Firmware, Product Design and Artificial Intelligence. In a nutshell, Vortex IoT Ltd build sensors and network devices for harsh environments, where conditions are hostile, power supply is limited, AI is needed, and data security is critical. We are looking for Software Engineer with the capability to work across the tech stack including infrastructure, back-end microservices and the front-end UI. The main purpose of the Software Engineer is to build secure software applications to integrate with remotely deployed mesh networks and provide an easy-to-use UI to enable quick and inciteful analytical processes on the data collected.


Intelligence Agencies Release AI Ethics Principles

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Then-Rep John Ratcliffe visited the DHS' National Cybersecurity and Communications Integration Center (NCCIC) in 2016, as part of a roll-out of automated cyber tools. ALBUQUERQUE -- Today, the Office of the Director of National Intelligence released what the first take on an evolving set of principles for the ethical use of artificial intelligence. The six principles, ranging from privacy to transparency to cybersecurity, are described as Version 1.0, approved by DNI John Ratcliffe last month. The six principles are pitched as a guide for the nation's many intelligence especially, especially to help them work with the private companies that will build AI for the government. As such, they provide an explicit complement to the Pentagon's AI principles put forth by Defense Secretary Mark Esper back in February.


Principles of Artificial Intelligence Ethics for the Intelligence Community

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The Principles of Artificial Intelligence Ethics for the Intelligence Community are intended to guide personnel on whether and how to develop and use AI, to include machine learning, in furtherance of the IC's mission. To assist with the implementation of these Principles, the IC has also created an AI Ethics Framework to guide personnel who are determining whether and how to procure, design, build, use, protect, consume, and manage AI and other advanced analytics. We will employ AI in a manner that respects human dignity, rights, and freedoms. Our use of AI will fully comply with applicable legal authorities and with policies and procedures that protect privacy, civil rights, and civil liberties. We will provide appropriate transparency to the public and our customers regarding our AI methods, applications, and uses within the bounds of security, technology, and releasability by law and policy, and consistent with the Principles of Intelligence Transparency for the IC.


What is Web Scraping: Introduction, Applications and Best Practices

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However, manually copy data from multiple sources for retrieval in a central place can be very tedious and time-consuming. "Web scraping," also called crawling or spidering, is the automated gathering of data from an online source usually from a website. While scraping is a great way to get massive amounts of data in relatively short timeframes, it does add stress to the server where the source hosted. However, as long as it does not disrupt the primary function of the online source, it is relatively acceptable. Despite its legal challenges, web scraping remains popular even in 2019.


6 Latest Job Openings For Data Scientists And Analysts

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Despite the recession, organisations have been hiring job roles for data science and analysts. In one of our previous articles, we discussed some of the ways that a data science enthusiast needs to do in order to get hired during the pandemic. In this article, we list down the 6 latest job openings for data scientists and analysts one must apply now. About: Being a Data Scientist at UnitedHealth Group, you will be working in teams addressing statistical, machine learning and data understanding problems. You will be contributing to the development as well as the deployment of machine learning models, operational research, semantic analysis, statistical methods, among others for finding structure in large data sets.


Scientific AI in materials science: a path to a sustainable and scalable paradigm - IOPscience

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Recent reports, reviews, symposia, and workshops have heralded machine learning (ML) and artificial intelligence (AI) methods as the next scientific paradigm in materials discovery and optimization [1–5]. Applications to materials science have exploded, spanning data analysis, knowledge extraction, and experiment selection [1, 6–9]. The numerous reasons for this trend are related to the omnipresence of ML systems in our everyday lives, the free availability software, and the demonstrated successes in materials discovery and on-the-fly data acquisition inspired by the Materials Genome Initiative (MGI) [1, 10–12]. However, despite their recent prominence, these techniques have been applied in a variety of materials science fields since the early 1960's [13–17]. Some recent examples of the successful implementation of ML to materials science were demonstrated by the high-throughput experimental (HTE, also known as'combinatorial') community. Parallel material synthesis and rapid characterization introduces a critical bottleneck in the analysis of hundreds to thousands of high-quality measurements correlated in composition, processing and microstructure [18–21]. There have been several international efforts to standardize data formats and create data analysis and interpretation tools for large scale data sets [22–24].