This code pattern teaches developers to quickly train a machine learning algorithm using PowerAI virtualization software through Nimbix. You can increase speeds over a non-Power architecture when running unsupervised learning iterations using NVIDIA GPUs and the CUDA parallel computing platform. This code pattern is designed for anyone who wants to increase their machine learning speed, showing you how to leverage IBM's new PowerAI for machine learning. The notebook focuses on evaluating the predictability of future financial market values in the renewable energy sector by examining related markets and sentiment detected in The New York Times news articles. When you've completed this pattern, you will understand how to: This pattern will assist application developers who need to efficiently build powerful deep learning applications and improve their machine learning speeds quickly.

A few months ago I wrote about how we built our wellness app experiment called "Kubecoin". It's a side project, a type of hobby app that we built to take to developer events, to challenge participants to walk more, and to offer an immersive taste of IBM Cloud technology. We updated the app, and renamed it CloudCoins (because it is built on more technology than just Kubernetes) and we're experimenting with it at the Cloud Foundry Summit Europe 2018 in Basel, Switzerland, this week. It is built for iPhone and Android. CloudCoins is a mobile app, backed by a blockchain system that anonymously converts participants' steps into a cryptocurrency, as they walk around the conference.

When it comes to data science nobody is asking the question of when AI will replace the data scientist. A common complaint is that there is a huge demand for data scientists and AI engineers, but have you ever thought that a data scientist's work is highly repetitive and that machine learning algorithms eventually will be able to learn from data scientists what to do with the data? So, is a data scientist's work repetitive? Especially, but not limited to deep learning, a lot of work involves a trial-and-error style tweaking of the abundance of different knobs you can tune. All of this can be done by a machine and sometimes more effectively.

During the training of machine learning models, data scientists have observed that the model can include some type of bias. Due to the black box nature of the training process, these biases are difficult to root out. The AI Fairness 360 toolkit offers a way to identify and quantify these biases, as well as a path for remediation. This code pattern explains how the AI Fairness 360 toolkit can help you identify and quantify bias in machine learning model training. A machine learning model makes predictions of an outcome for a particular instance.