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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
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
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
Transforming Data into Actionable Intelligence in Regulated Industries In the first of a series of fireside chats between Bo Howell, CEO of Joot and owner of FinTech Law, and Cal Al-Dhubaib, CEO of Pandata, the two discuss managing data and
How To Cultivate a Responsible Machine Learning Culture in Your Organization Even though we’ve come a long way in improving the accuracy and integrity of AI, it’s still far from perfect. If no one is responsible for your AI, how
6 Key Players That Should Be Involved in Every AI Decision If you want to develop a Trusted AI solution that solves a business challenge, you need human input. Or, to be more precise, you need human input from a
Pandata’s Proactive Approach to Trusted AI At Pandata, we advocate for Trusted AI that is approachable and ethical. This means acknowledging that sometimes there is no right answer but because we have a duty to safeguard others from unintended consequences,
From Pigeon Navigation to Pandata: A Chat with Hannah Arnson, Director of Data Science When Hannah Arnson was just a little girl, someone gave her the perfect gift. Her Fisher-Price® Micro Explorer Set™ featured a 30-power microscope, a specimen bottle,
4 Ways to Reduce Bias in AI Ever since the advent of the first computer, programmers have known that garbage in equals garbage out. In other words, the quality of what you enter into your machine determines the quality
A Marketer’s Journey Through the Land of AI – Entry 5, ML + A/B = Awesome Entry 4. In the last entry we talked about bias in AI, and the ways that humans in the mix can both help and
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