In this article, Lea Waniek (Data & Strategy Consultant) highlights the top 4 (non-technical) reasons for the failure of DS & AI initiatives. To counteract the pitfalls she identifies, she presents several possible solutions to each issue.
“Building trust through human-centric AI”: this is the slogan under which the European Commission presented its proposal for regulating Artificial Intelligence (AI regulation) in April 2021. In this commentary, Oliver Guggenbühl examines the EU’s proposal from various angles, highlighting both positive aspects and shortcomings.
Our CEO Sebastian Heinz is looking back at 2020, a year that pushed us out of our comfort zone and sparked many great initiatives and successes in our company. Make sure to read the article all through to the end – it may surprise you!
Due to recent achievements in deep learning, several different NLP (“Natural Language Processing”) tasks can now be solved with outstanding quality.
In this article, you will learn how NLP applications solve various business problems through five practical examples, which ensured an increase in efficiency and innovation in their field of application.
The foundations for image recognition and computer vision were already laid down in the 1970s. However, it is only in recent years that the field has found increasing application outside research. This article presents five selected and particularly promising use cases from different industries, which are either already in production or promise significant changes in their respective fields in the coming years.
The objective of this article is to show that fine-tuning Tesseract OCR on a small sample of data can already dramatically improve its OCR performance.
Artificial intelligence (AI) has become one of the essential drivers of digital change in business and society. Companies face the challenge of establishing AI as an integral part of their corporate strategy to remain competitive in the future. Based on our many years of AI project experience, we have identified 6 key elements that companies need to consider when developing an AI strategy.
How can you frame a data science question according to your client’s needs? In this blog post, our colleague Dominique explains how important it is to think about the business question in a different way – the data science way.
As data scientists, getting our hands on the data we need is often the most challenging part of a project. In practice, we tend to make life hard on ourselves because we don’t use the best tools for the job. Well no longer! Read on to learn how can you can harness Airflow to orchestrate your own ETL processes like a pro!
Have a look at what my team and I worked on during the Permafrost Hackathon in Zurich. The goal was to detect movements from multitemporal images. Since the images didn’t have any labels, we used unsupervised learning methods. Check it out, yo!
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