Your Guide to Quality Data Science Products

We recently took part in Hakuna-ma Data, a competition organised by DrivenData in partnership with Microsoft’s AI for Earth, which asked participants to build an algorithm for wildlife detection that would generalise well across time and locations...

Many AI projects languish in pilot purgatory. Filip Stachura points out known risks in deploying and maintaining AI models. The question becomes, do you have the right people in place to respond to potential and likely issues?

Building Deep Learning vision models satellite imagery to assist in identifying and labeling property damage from natural disasters.

The algorithms we build affect lives. We have an ethical obligation not to teach our machines to be prejudiced. Here are some lessons and examples that will help you achieve this in your machine learning model assembly and use.

We need to build a culture where AI is built with a focus on the entire outcome, not just metrics or profit.

Climate change will exacerbate the consequences of natural disasters. Developing countries will be hit the hardest. We helped Dr. Junko Mochizuki build a decision support tool for policymakers in Madagascar to better mitigate this risk.

Explore ways NGOs can use AI for species conservation or find out how AI pros and data scientists can contribute to real-world projects.

How to help an organization turn piles of paper into a data lake - Now easier with Optical Character Recognition (OCR).

I had the honor to meet people who preserve biodiversity and genetic resources at the GenRes Bridge conference in Tuusula, Finland. I represented Appsilon Data Science's Data for Good initiative and shared pathways for incorporating DS and AI.

To achieve IA implementation success, plan for a partnership between humans and machines, not replacement. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Automate tasks, not jobs and start small.

What are the differences between AI and IA? When is Intelligence Augmentation superior to a pure Artificial Intelligence approach? Which solution makes the most sense for your problem set? Consider IA solutions when a limited amount of data exists

Intelligence Augmentation is concerned with leveraging machines to enhance human intelligence. It’s a partnership between person and machine in which both parties contribute their strengths. It's AI that partners with humans for decision support