Notes from Industry

Legal, data management & documentation plus disintermediation are good for a long-term AI journey

Image by Oli Götting from Pixabay

For centuries libraries have kept the worlds knowledge organized. In the age of big data, it would be good to honor the key library principles a little more.

While loading the tediously sourced secondary data, or when screening very expensive primary data collected a year ago, have you ever wondered what this one CX-98/001 code was used for, and are you allowed to use it for the new project?

The very definition of codes, labels and numbers in the context of the data flowing into models not only influences how models are designed, and features are extracted, but also how…

A quick rundown of multiple label issues and a discussion of potential solutions

by Chikilino via pixabay

Imagine you are in a forest and can only get home by looking at markers labeled “may be closer to home”. You probably would take a while to get out of the forest.

That is, in essence how an artificial intelligence project may start out. Often labels are inaccurate, incomplete, unreliable, unsteady or insufficient in number. Even if you are far from the only one walking in the woods!

In the initial example one could decide not to use supervised learning at all and only consider reinforcement or semi-supervised learning. Alternatively, incomplete records could be removed. But what if removed…

How to design and lead an effective ML/AI organization

Image by Tim Hill from Pixabay

It’s not a myth, you absolutely can go and sit down to build a great model all on your own. However, that may not be easy and may result in a not particularly interesting outcome. Whether you are an executive with AI on the agenda or an engineer wondering how to organize your individual initiative, an appropriate setup is critical to success. What does that really mean in practice?

I will unpack how to establish a successful AI initiative based on personal experience of more than 10 years of repeatedly building AI and data-driven teams from scratch. That said, this…

How to overcome cultural challenges in AI/ML organizations

Image by mskathrynne from Pixabay

Humans have used data for decision making for a long time. In Egypt, granaries were pioneered because data was used in novel ways. Newton, Fibonacci, and countless others have demonstrated the power that lies in numbers. So, how well do organizations actually leverage the AI opportunities ahead?

Perhaps you have been asked to “add this really important feature” to your model or told “years of experience show that this is what needs to be measured”. In this article, I will explore three developments that drive these requests and share a few observations on how to address them.

The intent of…

Jean Voigt

Creativity is Inspired by Activity — Shaping & transforming organizations to build amazing products leveraging AI

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