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Captain Michael Kanaan

Enterprise Lead AI and ML, U.S. Air Force
Captain Michael Kanaan is an AI Hero, guiding the U.S. Air Force with AI insights and framing their mindset to be intelligence focused by harmonizing people with machine learning.
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Captain Kanaan’s Impact as an AI Hero

Michael is an inspirational thought leader on addressing bias in AI and getting to a state of trust in AI for businesses. He is the author of the upcoming book, T-Minus AI, which “explains AI from a human-oriented perspective we can all fully grasp and what each of us should know about modern computing, AI, machine learning, and its global implications.” (Pre-order at bit.ly/TminusAI).
650,000 Jobs Across the Air Force
human resources, finance, logistics, medical, food delivery, etc.
50,000 STEM Graduates for 500,000 Open Jobs in U.S.
Barriers to education have never been lower. In a few years, there will be one million open jobs in this space, and “we need to reimagine what education is.”
Avoid AI bias to gain trust in AI
“AI in its ethical, moral, and legal use is of concern to our nation and to the federal government, and it needs to be a concern to our enterprises, for it affects us all.”

Building AI You Can Trust and Respect

Machine learning applications are designed to analyze data and formulate predictions without any overall guidance from us. That doesn’t mean, however, that machine learning is necessarily safe from the effects or influence of our human biases. Far from it.

Captain Michael Kanaan has learned many pragmatic lessons in the enterprise deployment and acceptance of AI-based solutions. Kanaan’s philosophy brings the pressing issues squarely to the table as we march toward a future far different than we ever imagined. He encourages the U.S. Air Force and businesses everywhere to value people as their greatest asset and build solution-oriented, rather than task-focused, workforces.

Kanaan stresses the value of humans in machine learning, democratizing AI to enable anyone to independently ask and receive AI-based wisdom. He stresses that we must gain trust and ethics with AI, avoid bias in AI, and harness the ultimate power of AI for business and beyond — indeed, for all humankind.

One significant AI challenge has been machine learning bias and how to make predictions without guidance from humans. Biases are often reflected in our data, which means that our predictions and analysis can be biased as well. If organizations then take action on these predictions that have underlying biases, then they can perpetuate or sustain inequities. Steps to prevent this are possible with oversight and development and training of algorithms.

“What I think is important now is to talk about AI and provide explanations that we all can understand of an incredible evolution in technology and have a resulting capability that will forever change our information opportunities, our interactions, how well we (the broadest ‘we’) understand the rudiments and real potential of AI, anticipate its implications, and coordinate a course ahead. They’re all imperative matters…Get started — dive in! There are open use cases by DataRobot, it’s a welcoming community and everyone has a voice.”

  • DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely – it’s almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.

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