Pandata Blog

Approachable, Ethical, Human-Centered AI

AI compute
What Is Compute in AI? 3 Key Challenges

What Is Compute in AI? 3 Key Challenges In its simplest form, compute describes the manipulation of information; it’s used to organize and process data. Machines that use compute to perform tasks have three primary components: logic, memory, and interconnect.

AI KPIs
Reliable KPIs for AI: How To Measure Success 

Reliable KPIs for AI: How To Measure Success  This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI in Regulated Industries

Bias in AI
How Do I Monitor for Bias in AI?

How Do I Monitor for Bias in AI? This post is part of a series that features answers to top AI questions, directly from our data scientists. Other posts in this series include:  Who’s Responsible for AI’s Mistakes? What Can

AI + Privacy
Is AI Really Surveillance? [Insight From Collision Conference]

Is AI Really Surveillance? [Insight From Collision Conference] When you hear the word “surveillance” what comes to mind? You might be thinking about hidden cameras, tracking website history, public health monitoring, or a military operation. It’s often construed as invasive

AI Strategy
How To Develop an AI Strategy That Yields Positive ROI

How To Develop an AI Strategy That Yields Positive ROI This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI in

machine learning
What Can I Expect From the Future of Machine Learning?

What Can I Expect From the Future of Machine Learning? Machine learning has come a long way since the 1952 introduction of the first computer learning program. In 1980, data scientists created Explanation Based Learning, where a computer could create

ethical AI
Who Is Responsible for AI’s Mistakes?

Who Is Responsible for AI’s Mistakes? This post is part of a series that features answers to top AI questions, straight from our data scientists. To receive similar content to your inbox, subscribe to our monthly email digest. Strategizing, designing,

Regulated industry AI
3 Tips for Setting and Managing Realistic Expectations for AI Projects  

3 Tips for Setting and Managing Realistic Expectations for AI Projects   This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI

healthcare AI
Why Trusted AI Has to Matter in the Healthcare Industry

Why Trusted AI Has to Matter in the Healthcare Industry  Over the last few years, healthcare providers have increasingly offered telehealth appointments, managed digital transfers of medical records, and encouraged patients to take a more active role in monitoring their

Exploring the Art of AI with Associate Data Science Consultant Niki Agrawal 

Exploring the Art of AI with Associate Data Science Consultant Niki Agrawal  Niki Agrawal’s passion for inspiring creativity with the scientific method led her to her career as an Associate Data Science Consultant with Pandata. Read on to learn more