Artificial Intelligence and Deep Learning
We’re pleased to announce that our ORBOIMPACT VIRTUAL CONFERENCE, a two-day online event which covered Healthcare Payments on Thursday, October 29, and Banking & Payments on Friday, October 30, was a huge success! Industry speakers coupled with OrboGraph experts focused on AI innovation and payment strategy helped drive strong attendance, while innovative polling encouraged attendee…
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Artificial Intelligence has made waves in a stunning variety of businesses, and Healthcare Revenue Cycle Management is certainly no exception. Over the past year, we have covered many different angles in our Modernizing RCM with AI and our OrboNation Healthcare Blog, including:
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Even as we see our industry create and adopt newer and better, more precise tools for improving check processing and preventing fraud (we proclaimed 2019, remember, the Year of AI and Modernization), it’s important to be aware of how to most effectively integrate new technology the current ecosystem in a manner that maximizes return on investment.
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Predicting an economic recovery in today’s COVID-19 environment is far more complex than reading traditional individual economic indicators. As seen by the recent run in the stock market, there are certain market components somewhat disassociated from traditional indicators, i.e. unemployment rate, GDP growth, etc.
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In a little over a month, OrboGraph will host the ORBOIMPACT Virtual Conference: AI Innovation for Check Payments, Check Fraud, and Healthcare Remittance Automation.
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In a little over a month, OrboGraph will be hosting it’s first virtual conference since 2014: ORBOIMPACT Virtual Conference: AI Innovation for Check Payments, Check Fraud, and Healthcare Remittance Automation.
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By now, many of you have heard of the arguments between utilize GPU vs CPU. towardsdatascience.com provides a simple explanation on the reasoning behind the need for GPUs for machine learning:
GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously.
They have a large number of cores, which allows for better computation of multiple parallel processes. Additionally, computations in deep learning need to handle huge amounts of data — this makes a GPU’s memory bandwidth most suitable.
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Pymnts.com explores the various hurdles banks face when taking that big — and increasingly necessary — step. They note that migrating to the cloud is a vital tool traditional financial institutions (FIs’) will need in order to compete with digital-native FinTechs.
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To the layman, much of the language in the AI space can be mystifying, particularly in deep learning. Take for example one of the core elements; the node. A deep learning node is “a computational unit that has one or more weighted input connections, a transfer function that combines the inputs in some way, and an output connection. Nodes are then organized into layers to comprise a network.”
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Modern bank consumers are more and more comfortable interacting with their accounts online, often from a portable device. It’s never been easier to make deposits — using an app on an always-handy phone — and see the “funds available” right in an account, reassuring the user that the money is indeed deposited and… available. MyBankTracker…
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