Exploring the Future of AI with Jed Dougherty
HumAIn Podcast
Google's What if Tool Helps Explainability in AI
Google and Amazon are working on this really hard, but I think a lot of other people are. A lot of smaller players in the industry are also working on this. The Google What If allows you to basically slide along a single feature,. We'll hold everything else equal and see how that changes. And you know, making it more explainable.
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Jed Dougherty is the VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds and on-prem. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State.
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Jed Doughtery’s LinkedIn: https://www.linkedin.com/in/jediv/
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Jed Doughtery’s Website: https://www.dataiku.com/
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Outline:
Here’s the timestamps for the episode:
(00:00) – Introduction.
(02:03) – Making AI and Data Science relatively easy to use instead of limiting it to a few smart dudes encourages a more evenly distribution of the power that comes from it.
(03:27) – Google and Amazon want to keep control of the actual algorithms.
(04:37) – No big company in America, except Google, Amazon, Facebook and Netflix is able to hit the median income for their data scientists these giants have, which means they have a different pool of talent to pull from.
(07:22) – Universal Basic Income as a solution for a feasible future of jobs being replaced as a result of automation.
(12:27) – Empathy mapping to design AI systems to be diverse, inclusive and trained for multiple scenarios. AI has been about prediction and not an explanation of these predictions. Models should be more explainable than accurate.
(13:02) – Some of the new product lines for the explainability of an AI built by Google and Amazon .
(17:08) – Pushing the power back to the user immediately to empower them to have decisions driven by AI.
(11:53) – AI governance and ethical decision-making. If you don't have people connected to the things you're trying to predict, it's easy to miss a trend to assume that you have complete data when you do not.
(20:29) – Fair AI systems: labels are generated by humans, which means they have all the failures and foibles of our current society. Pushing those into a model makes that model exactly as good as our current society or worse.
(23:06) – We overestimated human enthusiasm for autonomous driving.
(28:51) – Computer centralized systems are weak. If Google, Amazon and Facebok were one single company, they would have this complete idea of your life and be able to predict every moment of it and say, how much of a worthy individual you were to society.
(32:35) – In NYC, people from all walks of life run into each other, touch each other accidentally. Nobody owns New York. You have the people who are gonna be affected by AI, the business knowledge and a growing tech base of folks who can implement technology. NYC could be the center of machine learning.
(36:22) – Linux command line is running 99% of the servers in the world right now
(38:32) – Knowledge to become tech-relevant: this is such a new industry that 10 years at school, you may have learned 5% of the industry.
(43:03) – Humans and the machines. Things from an ethical perspective or a human perspective combined with technical knowledge.
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