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5 key areas for tech leaders to watch in 2020

O'Reilly on Data

Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. There’s plenty of security risks for business executives, sysadmins, DBAs, developers, etc., After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage.

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Deepfakes Security Risks

KDnuggets

Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

Risk 111
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What is Model Risk and Why Does it Matter?

DataRobot Blog

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks.

Risk 111
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How Insurance Companies Use Data To Measure Risk And Choose Rates

Smart Data Collective

Here is the type of data insurance companies use to measure a client’s potential risk and determine rates. Traditional data, like demographics, continues to be a factor in risk assessment. Teens and young adults are less experienced drivers and, therefore, at risk for more car accidents. Demographics. This includes: Age.

Insurance 113
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Streaming Market Data with Flink SQL Part II: Intraday Value-at-Risk

Cloudera

These interactions are captured and the resulting synthetic data sets can be analysed for a number of applications, such as training models to detect emergent fraudulent behavior, or exploring “what-if” scenarios for risk management. Value-at-Risk (VaR) is a widely used metric in risk management. Intraday VaR. Citations. [1]

Risk 99
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What are model governance and model operations?

O'Reilly on Data

Related to this is the need to monitor bias, locality effects, and related risks. An overview from a 2017 paper from Google lets us gauge how much tooling is still needed for model operations and testing. At the moment, few (if any) teams have checklists as extensive as the one detailed in the 2017 paper from Google.

Modeling 200
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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

They published the original Transformer paper (not quite coincidentally called “Attention is All You Need”) in 2017, and released BERT , an open source implementation, in late 2018, but they never went so far as to build and release anything like OpenAI’s GPT line of services. I think not.