If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Smart Home Telematics company positions itself to pursue the growing interest among forward-thinking insurance carriers for data-driven insights and customer engagement. VANCOUVER, BC AND NEW YORK CITY, NY – 04-25-2018 (Press Release Jet) -- Plasmatic Technologies (Plasmatic) is pleased to announce the addition of Mike Moen to its advisory team, where he will assist the company as it caters to the tremendous need for innovation in Property and Casualty Insurance. David Sussman, Chief Executive Officer of Plasmatic stated: "We are very excited to enlist Mr. Moen's industry knowledge, deep relationships, and track record in analytics-based businesses. Mike's strong collaboration with our leadership team has already helped validate the appeal of our solutions with decision makers at top insurance carriers and Insurtech scouts in some of the more prominent industry hubs". With over 20 years of experience as an entrepreneur and business executive, the enlistment of Plasmatic's latest advisor is a sign of definitive momentum in the sector, where device-driven insights and mobile experiences are poised to help insurance providers deliver more personalized services and augment their policyholders relationships.
The National Healthcare Anti-Fraud Association estimates that health care fraud costs tens of billions of dollars every year. Health insurance special investigations units have long worked to stem the tide of losses, but it can be a challenge to stay ahead of the curve. To learn more about the tactics investigators employ and new tools at their disposal, SmartBrief spoke with Patrick Stamm, principal adviser to FraudScope, an AI-assisted platform for detecting health care fraud, waste and abuse. Please describe the existing health care FWA investigation paradigm and some ways in which it fails health plans? Historically, health care fraud investigations were almost exclusively initiated after the fact -- often as a result of a tip submitted to the special investigations unit from a member or employee who noticed something amiss while processing a claim.
When you are renting an apartment via Airbnb, hailing an Uber or paying for a cup of coffee in your favorite shop, you are sharing your personal data with a business. For you, as a customer, the process is simple, secure and trustworthy. What you may not think about it how these exchanges are handled by the business. Peer-to-peer marketplace platforms, financial institutions, insurance companies and healthcare providers constantly engaged in the'balancing act' of great customer experience vs. security. All of them want to satisfy the consumer demand for fast and seamless payment experience.
Science fiction authors and futurologists often imagine a world where ubiquitous use of artificial intelligence (AI) in medicine means the end of physicians. In such a world, omniscient AI bots effortlessly diagnose and treat diseases, while various robots are busy performing life-saving surgeries. Many physicians, on the other hand, hold the view that medicine is as much an art as it is science, which makes it unique among other industries. They believe that efforts to incorporate artificial intelligence into healthcare are a fool's errand. The truth probably lies somewhere in the middle, at least in the present and in the near future.
I expect that we will see an increased focus on improving health outcomes utilizing artificial intelligence. Patients are producing significant amounts of health data with mobile devices and connected wearables. Providers are using electronic health records generating enormous amounts of information. Applying artificial intelligence will utilize information from patients and providers to actively identify health conditions that may not have been detected until later. By definition, AI improves the more it's used.
As we live in the new world of quality, value-based care, we must be able to draw more insights and conclusions from ever-increasing amounts of information. We have the data, now we must put it to work. When we combine all of this data with machine learning, we are equipped to make smarter decisions. We have the power to transform healthcare – from the way we use electronic health records to the way we predict and deliver care. Most EHRs are built on technology that is 20 or 30 years old.
Differences in imaging equipment, procedures and protocols can dramatically affect the performance of deep machine learning when analyzing brain tumors, according to a new study in Medical Physics. Automatic brain tumor segmentation from MRI data using deep learning methodologies has gained steam in recent years. Convolutional neural networks (CNNs), a type of deep learning algorithm, are commonly used for segmentation of brain tumors, and provider organizations have recently begun sharing images to increase the data to work with. However, providers often use different imaging equipment, image acquisition parameters and contrast injection protocols, which could cause institutional bias; a CNN model trained on MRI data from one organization may stumble when tested on MRI data from another. The researchers, from the Radiology Department at Duke University School of Medicine, used MRI data of 22 glioblastoma patients from MD Anderson Cancer Center and 22 glioblastoma patients from Henry Ford Hospital to assess how CNN models worked with their own and each other's MRI data.
Localization (also referred to as "l10n") is the process of adapting a product or content to a specific geographic locale or market with the aim of giving it the look and feel of having been created specifically for a target market, no matter their language, culture, or location. Language translation and cultural adaptation are obviously a big part of localization, and globally visible companies heavily rely on sophisticated technology and localization engineering to get the job done. Localization is a complex process--some of it is automated by tools, but much of it is still a human-driven, manual undertaking. So it's no wonder that recent AI advances in Machine Translation (MT), as well as the allure of automated one-click translation platforms have caused a stir in the translation and localization industry and some fear that this development might spell doom for language professionals and perhaps even be the end of language service providers (LSPs) altogether. So, is complete push-button localization imminent or hyped?
Experienced lawyers in the US have been left behind by AI when it came to reviewing legal documents according to a new report – with the lawyers exhibiting 85% average accuracy compared to 94% average accuracy rate achieved by AI software. This revelation is based on a study carried out by professors at Duke Law, University of Southern California, and Stanford Law School. Metaphorically, the study was a race between LawGeex, an AI contract review platform provider, and a team of 20 top corporate lawyers with notable experience particularly in reviewing Non-Disclosure Agreements (NDAs). For the study, the lawyers and the LawGeex AI had to analyse five previously unseen contracts with 153 paragraphs of technical legal language, under controlled conditions precisely prepared the way lawyers review and approve everyday contracts. The highest performing lawyer stood in line with LawGeex AI by achieving 94% accuracy but the average accuracy achieved by the least performing lawyer stood at just 67%.
Those who attended Cloud Expo Europe earlier this week took their opportunity to assess the next level of cloud services, ranging from blockchain, to artificial intelligence (AI) and machine learning. The cloud underpins these technologies and enables them to flourish, while as this publication has previously reported, the M&A cycle has been lit up by it. But what are some of the practical applications for cloud-enabled AI? Lifesize, a cloud-based conferencing hardware and software provider, is exploring how AI can improve the meeting room – as well as outside of it. A recent video the company put out showed the concepts and potential available. The key is around machine vision; using analytics to assess each participant on a call, whether it is a workplace meeting, virtual classroom or anywhere else, to calculate engagement.