There should be a circle around your Venn Diagram called 'Statistics/Mathematics.' Data Science, AI, Deep Learning, and Machine Learning are all branches of very advanced applied statistics, which most people learn in their second or third year of a Statistics, Data Science, or Mathematics undergraduate degree.
Ethan, you have beat me to it! My next article gets into the Stats & Math underpinning for ML & AI. Make sure you give it a read and let me know if I'm hitting the mark!
Dylan, looking forward to the series. It feels like we used to talk about ML using terminology like "advanced analytics". We have been doing predictive analytics for years thanks to the rise of the data science role. Now we talk about ML in the context of AI thanks to GenAI! I would love for you to drill in a little on the data layer topic because we have been building mature data platforms for years, but mainly in the structured data world and our predictive models have been leveraging that data to predict a future event or make a recommendations. However, it's my understanding that the GenAI approaches do not handle or even "want" structured data, those approaches want content (unstructured data). I say content because how does one judge the quality of content like we do quality of structured data, interesting? And one last point I would love to get your thoughts on, when we think of the difference between data and content, data professionals typically manage the data stack and not necessarily the content stack, at least in my experience. Will data professionals need to broaden their technical responsibilities into things like document management systems or just try and extract documents like we do structured data?
There should be a circle around your Venn Diagram called 'Statistics/Mathematics.' Data Science, AI, Deep Learning, and Machine Learning are all branches of very advanced applied statistics, which most people learn in their second or third year of a Statistics, Data Science, or Mathematics undergraduate degree.
Ethan, you have beat me to it! My next article gets into the Stats & Math underpinning for ML & AI. Make sure you give it a read and let me know if I'm hitting the mark!
As ever. Really beautiful article. Thank you for sharing your pearls of wisdom with such clarity.
Thanks Paul! I try my best to simplify the complex, so glad I was able to do that for one of my five ML/ AI articles at least!
Excellent Article
Thanks so much Ghulam!
Dylan, looking forward to the series. It feels like we used to talk about ML using terminology like "advanced analytics". We have been doing predictive analytics for years thanks to the rise of the data science role. Now we talk about ML in the context of AI thanks to GenAI! I would love for you to drill in a little on the data layer topic because we have been building mature data platforms for years, but mainly in the structured data world and our predictive models have been leveraging that data to predict a future event or make a recommendations. However, it's my understanding that the GenAI approaches do not handle or even "want" structured data, those approaches want content (unstructured data). I say content because how does one judge the quality of content like we do quality of structured data, interesting? And one last point I would love to get your thoughts on, when we think of the difference between data and content, data professionals typically manage the data stack and not necessarily the content stack, at least in my experience. Will data professionals need to broaden their technical responsibilities into things like document management systems or just try and extract documents like we do structured data?