Josh Lewis

Josh Lewis is the VP, Product at Alpine Data. Josh has ten years of experience across academia and industry in machine learning, data analysis, cognitive science and user experience. Prior to joining Alpine, Josh lead the frontend engineering team at Ayasdi where he built apps and APIs for the healthcare, pharmaceutical and finance verticals, as well as Ayasdi’s domain-general data analysis and visualization software.

Before joining Ayasdi, Josh was a PhD student and postdoc at the UC San Diego Cognitive Science Department where he investigated the role of human perception and insight in the data analysis process. He also developed novel software for applying unsupervised machine learning algorithms called Divvy, a project that was supported by a multi-year NSF grant.

Josh graduated from Pomona College with majors in Cognitive Science and Philosophy.

The opinions expressed in this blog are those of Josh Lewis and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies.

The siren song of Hadoop

With deep learning, the data-rich get richer

With deep learning, the data-rich get richer

Why AIaaS will dominate business applications for deep learning.

From presentation to conversation: How A.I. will transform enterprise apps

From presentation to conversation: How A.I. will transform enterprise apps

Apps will soon shift from dumb repositories to deft assistants.

Job inequality and artificial intelligence in the enterprise

Job inequality and artificial intelligence in the enterprise

The future has fewer jobs, but those that remain will be better.

Machine learning: The deplorable state of deployment

Machine learning: The deplorable state of deployment

A failed standard, complex alternatives -- and a way forward.

Machine learning: Clustering and classification on the campaign trail

Discovering and targeting micropopulations for politics and profit

Putting deep learning to work

Hype aside, deep learning can open up new sources of data for business analysis.

Machine learning: Demystifying linear regression and feature selection

The time-tested technique for predicting numbers, and the role of domain knowledge in machine learning.

The cost of an error: Balancing the role of humans and machines

When the cost of an error isn’t trivial, we need better approaches to error mitigation to operationalize and scale machine learning.

Your business should demand more from machine learning

Your business should demand more from machine learning

As a businessperson, the ability to make specific, actionable requests of your data scientists, hold them to a high standard and connect their work to relevant action is basic contemporary corporate literacy.

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