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Knowledge graphs have grown into a major trend in life sciences since the term was introduced by Google in 2012. However, despite this recent uptick in use, they significantly impact the space. Knowledge graphs enable several critical functions within life sciences, including drug development, literature review, and regulatory reporting.

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There's always some new site,
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I browse all day and night,
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It's like I'm surfing at the speed of light,
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Customer experience (CX) teams often face a complicated puzzle when it comes to budgetary decisions and resource allocation within their organizations.

MetLife is a leading global insurance company headquartered in New York City. It provides its customers various insurance and financial services, including life insurance, health insurance, retirement plans, and investment management.

AI is revolutionizing how companies conduct operations, serve customers, and manage risks – driving a USD 20 Billion investment in AI capabilities among BFSI firms last year. As AI continues to transform the industry, financial services organizations will seek to build effective AI teams to stay ahead of the competition. 

Event Title: 4th OECD Global Forum on Digital Security for Prosperity

Event Host: OECD

Location: Paris, France

Insurers have been long aware of the perfect storm of converging market trends pointing to the permanency of digital disruption and change. Yet, the industry – especially the big players – always seemed to move their collective house further away from the shoreline. 

Goldman Sachs was founded in 1869 and joined the New York Stock Exchange in 1896. Since 2016, Goldman Sachs has begun moving into consumer financial products. The company is headquartered in Lower Manhattan with regional offices in England, Poland, Hong Kong, Tokyo, Bangalore, and on US soil in Dallas and Salt Lake City.

Critical to the definition of robotic process automation (RPA) is the notion that the tasks a 'robotic' software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities. 

Proving the economic value of AI projects remains paramount to the success and continuation of any machine learning-related project. As more companies adopt AI technologies, measuring these projects' success is increasingly important – unfortunately, proving this value is proving anything but straightforward.