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Tala's Director of Data Analytics Lauren Moores provides insight into how the company uses mobile data to give financial identi... Adra Graves, senior data analyst at OpenTable, highlights the problems that arise when analysts and data scientists are spread ... Annie Flippo, manager of analytics at Thinknear by Telenav, shares some insight into how her team uses GPS data and capabilitie... Hawthorne Direct's Head of Technology Sarah Arnett shares her rationale for bringing data science, engineering, and product und... Beachbody's Director of Data Aarthi Sridharan talks about the roadblocks she has encountered trying to align business stakehold... Women make up nearly half of the U.S. workforce -- but only 24% of STEM workers are women, according to the Census Bureau. How Do We Get More Women into Data Science? We asked panelists and attendees at our DataScience: Elevate - Spotlight: Women in Technology event to share with us the data s... Amgen Principal DevOps Lead and Data Scientist Pam McCaslin explains how the pharma company uses DevOps to shorten the time it ... Verizon Wireless Data Scientist Aurora LePort builds predictive models to understand customer churn and other behaviors. We asked experts at Verizon Wireless, Netflix, Oracle, and other companies where they see machine learning and artificial intel... What Does the Future Hold for Machine Learning? Shiny applications are ideal for non-technical decision makers who want a code-free way to interact with data.


Siemens' AI Work Delivers Competitive Advantage in IIoT

@machinelearnbot

You may think of German industrial powerhouse Siemens as being primarily a machine builder, but the company has a range of digital offerings that span throughout the entire value chain in manufacturing. From product development, production engineering, and production execution, the company offers a consistent data model across all levels of manufacturing, thanks to its product lifecycle management, digital twin software, and MindSphere IoT platform. Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. Michael May, Ph.D., the company's head of technology field business analytics and monitoring, told me at Hannover Messe that the corporation has been working on AI projects for decades. For instance, more than 20 years ago, Siemens implemented neural networks in more than 30 steel plants to monitor and improve quality, process, and efficiencies.


Siemens' AI Work Delivers Competitive Advantage in IIoT

#artificialintelligence

You may think of German industrial powerhouse Siemens as being primarily a machine builder, but the company has a range of digital offerings that span throughout the entire value chain in manufacturing. From product development, production engineering, and production execution, the company offers a consistent data model across all levels of manufacturing, thanks to its product lifecycle management, digital twin software, and MindSphere IoT platform. Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. Michael May, Ph.D., the company's head of technology field business analytics and monitoring, told me at Hannover Messe that the corporation has been working on AI projects for decades. For instance, more than 20 years ago, Siemens implemented neural networks in more than 30 steel plants to monitor and improve quality, process, and efficiencies.


Customer Risk Profiling using Machine Learning in Lending

#artificialintelligence

With technological advancements and Big Data, businesses are building more complex techniques and algorithms to identify risks. Using Machine Learning techniques, businesses are able to build Customer Risk Profiling models which enable the identification of likelihood and probability of customers being a risk. Machine learning, is beginning to create new avenues in the lending market. Machine Learning is an extension of artificial intelligence, that enables computers or robots with the ability to learn, analyze and predict, using algorithms that iteratively learn from data. It empowers the system to learn and adapt itself.


Lyft, Uber, Airbnb, and LinkedIn demonstrate the power of in-house AI solutions

#artificialintelligence

Companies understand the importance of artificial intelligence and machine learning, especially since it's become an increasingly important competitive differentiator, and are eager to jump in. But as always, the question stands: Once you've identified the potential of AI for your business, do you buy, or do you build? That's one of the big questions we'll be tackling at this year's Transform: Accelerating Your Business With AI. Spoiler alert: Lyft, Uber, Airbnb, and LinkedIn, featuring prominent speakers at this year's event, have come down firmly on the side of building their own AI solutions. And they've ended up with some dramatically successful -- and very cool -- results.