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Origami Risk, AI firm partner - Business Insurance

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Origami Risk LLC and Gradient A.I. Corp. have formed a partnership allowing Gradient's claims and policy modeling capabilities and predictive analytics resources to be used on Origami's digital platform, the companies said in a joint release Tuesday. Insurers, third-party administrators, risk pools, and self-insured organizations will be able to access Gradient's proprietary data sets of millions of claims and policies, which are integrated with the Origami platform's workflow, reporting and digital engagement tools, the statement said. The Gradient tools can be applied to policy underwriting and claims adjusting processes, such as enabling claim teams to focus greater attention on claims with a high probability of becoming significant cost-drivers, the statement said. "Our collaboration with Gradient AI offers insurers, risk pools and large self-administered plans using our platform ready access to" Gradient's tools, Robert Petrie, CEO of Origami Risk, said in the statement.


Who Does the Machine Learning and Data Science Work?

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A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., gathering data, analyzing data, communicating to stakeholders) to complete those projects. Today's post will focus on the broad work activities (or projects) that make up their roles at work, including "Build prototypes to explore applying machine learning to new areas" and "Analyze and understand data to influence product or business decisions".


Mobile Robots Propel Research with Automated Robotic Lab Assistants

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Tasks in the pharmaceutical, life sciences and biomedical industries have always been time-consuming and complex. With the advent of the Covid-19 pandemic, these undertakings will only grow in complexity. To ensure speed, accuracy and mitigate the infectivity stress among the humans, robots are called upon to meet the ever-increasing range of workflows in today's research and development laboratories. Laboratory automation, drug discovery and pharmaceutical manufacturing are emerging fields where the services of robots are leveraged for research and development. Robotic lab assistants help researchers and scientists focus on high-level tasks like the analysis of potential therapeutic compounds rather than mundanely mixing compounds to determine their curative characteristics.


The era of artificial intelligence (AI) and machine learning for Army tasks in the field has arrived

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AI techniques can help predict when vehicle parts need to be replaced before the vehicle breaks down. If this can be done well it will save money.


Artificial intelligence: Companies employing skilling initiatives and AI get most tech value, finds …

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New research by Microsoft India has revealed that organizations combining the skilling initiatives with artificial intelligence are the ones that have …


AI technology and services revenues hit £156 Billion

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A new report from IDC shows that global AI software, hardware, and services revenues are expected to exceed $156 billion in 2020, with 80% generated from software. The market is expected to grow to $300 billion in 2024. AI-powered CRM and ERM applications account for more than $120 Billion in 2020, with platforms making up the rest. Other areas of activity include content production, management, workflow and collaboration. The market for AI services will reach $18.4 billion in 2020, an increase of 13% year over year, with AI IT services accounting for nearly 80%.


Why You Should Care About AI (Even If You Don't Want To)

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AI is one of the hottest buzzwords right now. And, while almost every media outlet is talking about AI, most people do not even know what it is and what exactly it can do. AI is a mystery technology. Some of the messages in the media warn that it could take all of our jobs, replacing humans completely in the workforce. While both messages grab your attention, unsurprisingly, neither is entirely true.


You Should Be Aware Of These (Common )Deep Learning Terms And Terminologies

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Convolutional layer: A convolution is a mathematical term that describes a dot product multiplication between two sets of elements. Therefore a convolutional layer simply houses the convolution operation that occurs between the filters and the images passed through a convolutional neural network. Batch Normalization layer: Batch Normalization is a technique that mitigates the effect of unstable gradients within a neural network through the introduction of an additional layer that performs operations on the inputs from the previous layer. The operations standardize and normalize the input values, after that the input values are transformed through scaling and shifting operations. MaxPooling layer: Max pooling is a variant of sub-sampling where the maximum pixel value of pixels that fall within the receptive field of a unit within a sub-sampling layer is taken as the output.


The 8 Terms Every Machine Learning Beginner Should Know

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If you ever want to work and research in the Machine Learning arena, these are the 8 key terms you cannot ignore. Algorithms are a basic element in the world of Machine Learning. An algorithm is a logical sequence of instructions that describe step by step how to solve a problem. Most often, the algorithm works as a sequence of simple if then statements. Others are more complex and include mathematical equations or formulas.


Empowered by AI to Get Close to Customers

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AXA XL is the property and casualty (P&C) and specialty risk division of multinational insurance giant AXA. It is known for resolving even the most complex risks for its customers, which range from mid-sized firms to the world's largest multinationals. I run the digital transformation initiative at AXA XL. I'm going to tell you about our particular digital transformation journey, its highs and lows, and how we dealt with them. And the first points I want to make is that, first, this journey is all about the customer; and that secondly, that it follows approximately the same trajectory as the well-known five-stage cycle of grief: from denial, to anger, to bargaining, depression, and, finally, to acceptance.