#machine learning

Interview with Jay Jay Billings, Team Leader for Scientific Software Development at Oak Ridge National Laboratory

“Good code generators will be the most helpful and useful tools for coding by 2040”

A team of researchers at Oak Ridge National Laboratory wrote a paper in which they argue that machines will write most of their own code by 2040. Does this mean that humans won’t be writing code at all? How will coding in 2040 look like? We talked with Jay Jay Billings, one of the authors about their ideas and the future of machine learning.

Interview with Sumanas Sarma and Rob Hinds

“Machine learning tends to have a Python flavor because it’s more user friendly than Java”

Artificial Intelligence and Machine Learning are all the rage right now. JAXenter editor Gabriela Motroc caught up with Sumanas Sarma and Rob Hinds at JAX London 2017 to talk about engineering best practices that can be applied to ML, their favorite programming languages and libraries for machine learning, and when it’s wise to jump on the ML bandwagon.

Interview with Jesper Richter-Reichhelm

Machine Learning as a microservice in a Docker container on a Kubernetes cluster — say what?

It is always fascinating to see the versatile ways in which machine learning can be used. At Outfittery, algorithms help the experts select the most suitable outfits for customers — quite literally. In an interview at W-JAX 2017 in Munich, Jesper Richter-Reichhelm, CTO at Outfittery GmbH, explains how the company uses machine learning and which frameworks they use. He also tells us who makes better suggestions — human beings or machines.