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Pros & Cons Of Artificial Intelligence App Development


When you want to incorporate AI into your own mobile development procedure, it is important to learn how AI functions for different kinds of applications. Artificial Intelligence is a major portion of the mainstream these days as its innovation is integrated into almost all contemporary devices. Right from predictive analytics to chatbots, developers & organizations are constantly examining ground-breaking approaches for utilizing AI for delivering enhanced client services & reconsidering business procedures. The classifications of Artificial Intelligence in mobile application development companies are varied. It can be categorized into weak & strong.

Using AI To Identify Your Best Customers In The Future


Using AI to boost sales is not a new idea. In fact, back in 2017, the Harvard Business Review did an extensive story about one sales office of motorcycle maker Harley Davidson that was able to increase sales from two bikes per week to 15 per weekend using AI-powered marketing support. If predictive analytics increased sales leads by 3,000 percent back in 2017, what's it doing for businesses now? AI and predictive analytics are still making an impact for businesses in terms of identifying customer trends, building customer profiles, and constructing tighter potential target audiences. However, it's also doing a lot more, both in terms of how it's able to gather data and how it's able to use it to offer customers the personalization they want and need.

How AI is Revolutionizing the Process of Fintech Firms?


Artificial Intelligence has revolutionized the finance industry. Not only does it improve the precision level in the industry, but it also enhances the customer engagement level and speed up the query resolution period. In this blog, we will be finding out answers about the importance of AI in financial sectors or FinTech firms. By the year 2030, traditional financial institutions can shave 22% in costs, as per the latest 84-page report of the Autonomous in an AI in the financial industry. Fintech companies and financial firms were the early adopters of relational databases, mainframe computers, and have eagerly awaited the next generation of computational and analysis power.

Monash University and The Alfred to develop AI-based superbug detection system


Monash University and Alfred Hospital are developing an artificial intelligence-based system to improve the way superbugs are diagnosed, treated, and prevented. According to Monash University professor of digital health Christopher Bain, infections from superbugs kill 700,000 people every year and by 2050, the world could see 10 million deaths annually from previously treatable diseases. Superbugs are created when microbes evolve to become immune from the effects of antimicrobials. The project, which will be mainly based at The Alfred, has received AU$3.4 million from the federal government's Medical Research Future fund. According to the project's lead researcher, Antony Peleg, the project will look to integrate genomics, electronic healthcare data, and AI technologies to address antimicrobial resistance in the healthcare system.

How to Increase Group Insurance Sales with AI - Global IQX


During peak business periods for group carriers, such as open enrollment in the United States, artificial intelligence can be leveraged to increase group insurance sales by streamlining quoting, optimizing resources, automating manual tasks and eliminating duplication of effort before and during enrollment. Peak enrollment period is here once again as group and voluntary benefits providers put their remote work arrangements to the test in what will be an unusually demanding season. This year has been the year of digital transformation in the insurance industry, and 2020's challenges will inspire new approaches and digitization within carrier ecosystems. Fortunately, insurers can use AI and predictive analytics to increase group insurance sales. AI can help carriers streamline quoting and enrollment, optimize resources, and automate manual tasks.

The Data Science Course 2020: Complete Data Science Bootcamp


Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

Review: Kinetica analyzes billions of rows in real time


In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.

Six barriers to digital transformation; CIO strategies to conquer them


That led to an aggressive pace of change over the past few years, said Merim Becirovic, Accenture's managing director of core infrastructure and business operations. Consider, for instance, this measure of success: Three years ago, Accenture had only 10% of its infrastructure and compute needs in the cloud, but now it has 90% in the cloud. Such gains didn't come without challenges, Becirovic said. Accenture leaders discovered a number of potential barriers to digital transformation, ranging from new skill requirements to security to just how fast the organization can keep changing. Accenture is far from alone in its quest for transformation.

Applications of Differential Privacy to European Privacy Law (GDPR) and Machine Learning


Differential privacy is a data anonymization technique that's used by major technology companies such as Apple and Google. The goal of differential privacy is simple: allow data analysts to build accurate models without sacrificing the privacy of the individual data points. But what does "sacrificing the privacy of the data points" mean? Well, let's think about an example. Suppose I have a dataset that contains information (age, gender, treatment, marriage status, other medical conditions, etc.) about every person who was treated for breast cancer at Hospital X.