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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Algorithms gone wild: Applying machine learning for insights into machine learning algorithms

Ira Cohen (Anodot)
16:00–16:40 Thursday, 11 October 2018
Secondary topics:  Temporal data and time-series

Who is this presentation for?

  • Chief data officers, chief analytics officers, heads of BI, and chief marketing officers

Prerequisite knowledge

  • Familiarity with machine learning

What you'll learn

  • Learn a practical way to apply anomaly detection for today‚Äôs businesses that not only is doable but will have rapid impact on the bottom line

Description

Recently, a machine learning algorithm went wild live in front of an audience of millions, as the cartoon South Park trolled Alexa in a really funny way. In the episode the iconic character Cartman kept asking Alexa to add items to his shopping cart. However, viewers’ Alexas near their TVs responded in the same way, adding the items that Cartman ordered to THEIR shopping carts.

The problem with Alexa is that it requires context. Machines lack the context of how the world actually behaves and are very limited in their scope. Yet by monitoring their own machine learning algorithms, Amazon could have easily tracked how Alexa units around the country were adding similar items at a massive scale.

The Alexa issue is one example of a far larger issue. Today, businesses are collecting unprecedented amounts of data. While this data is undeniably useful, the biggest issue is noticing when something strange is happening. Anomalies constantly occur, and detecting them fast is really critical. Ira Cohen explains how to catch problems and glitches early on by using machine learning algorithms to monitor these algorithms for anomalous behavior.

Photo of Ira Cohen

Ira Cohen

Anodot

Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.