In a time when health systems are struggling to gain meaningful insights from data – and simultaneously aware that safeguarding patient privacy is essential – synthetic data offers a lot of potential.

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

In today’s fast-changing data landscape, having a strong data system and advanced analytical tools is key to getting valuable insights and staying ahead of the competition. The data lakehouse ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...

In today’s dynamic global economy, financial institutions are increasingly confronted with uncertainties that defy historical precedent. Traditional stress testing long reliant on past market data ...

On November 7, CAAI hosted Dr. Ryan Kappedal, ’19, a Booth alumnus and Technical Lead Manager at Google, for an insightful discussion on the evolving landscape of AI and the critical role of data ...

In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Read more about AI-driven air quality system promises faster, more reliable urban health warnings on Devdiscourse ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...