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Machine Learning Failures
Machine Learning Failures
Garbage In, Garbage Out is Not Why Machine Learning Fails
At the RSA 2019 Conference I spoke with Davi Ottenheimer, product security at MongoDB, about where and why machine learning falls short. One might assume garbage in, garbage out, but who determines what's garbage and what's not can greatly change the accuracy and prejudice of a machine learning system. Make sure you also listen to the podcast episode of Defense in Depth (see below).
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This week's episode of Defense in Depth
Machine Learning Failures
On this episode of Defense in Depth:
Co-host Allan Alford, CISO of Mitel, and our guest Davi Ottenheimer, product security at MongoDB, discuss the following:
Don't fall victim to believing that success and failure of machine learning is isolated to just garbage in/garbage out. It's far more nuanced than that. Some human actually has to determine what is considered garbage and what is not.
It only takes a very small amount of data to completely corrupt and ruin machine learning data.
A small infection of data can spread and corrupt all of the data. Those with political and economic motivations can do just that.
We have failures in human intervention. Machine learning can just magnify that at rapid rates.
While there are many warning signs that machine learning can fail, and we have the examples to back it up, many argue that competitive environments don't allow us to ignore it. We're in a use it or lose it scenario. Even when you're aware of the pitfalls, you may have no choice but to utilize machine learning to accelerate development and/or innovation.
Special thanks to this week's Defense in Depth podcast sponsor, Remediant.
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