In our final Tech Tuesday for the year, I want to wish you all a healthy and happy holiday. This year has been surreal at times and I hope you’ve all kept your health and good spirits as much as that is possible.
At Bennett Data Science we’ve had a busy end to 2020 and have managed to get through this year with our health intact and have even managed to grow as more and more companies look to automate their workflows.
Finally, in the spirit of gratitude, I want to give some much-deserved recognition to Ronja, who week-after-week edits my writing, creates snippets of articles I’ve shared with her, posts to Medium and LinkedIn and Twitter and so much more. Without her drive and sagacity, these newsletters would be less fun to read, full of typos, and occasionally sent out on Wednesdays.
Thanks, Ronja, please take next week off! 🙂
See you all in 2021.
Best wishes to you and yours,
Looking back at this year, Zank has written an impressive number of Tech Tuesdays on topics ranging from ways to personalize product offerings, to the impact of Covid-19, to the A.I. behind online platforms such as Spotify and Tik Tok. As we’re nearing the end of the year, I thought it would be nice to share some of my personal favorites for you to look back at. It’s amazing how the content Zank writes remains valuable despite the passing of time, and I commend him for the enthusiasm and care with which he selects each week’s topic and writes about them. Please feel free to reply to this email and share what one of your favorite Tech Tuesdays was, he would greatly appreciate your feedback. Happy holidays, Ronja
The Controversy of Black Boxes
A “black-box system” refers to a predictive model that produces an output with no way for the operator or end-user to understand what was done “inside” the box. The reason data scientists employ these black-boxes is because, generally, ordinary machine learning models can’t approach the complexity required to accurately perform hard tasks such as text, image, or audio processing we rely on daily. Ordinary machine learning models are typically easy to explain, but come with the tradeoff of lower accuracy than neural networks or more complex A.I. So for the sake of accuracy, we sacrifice explainability. But what’s wrong with that? A lot, it turns out. Read about the controversy of black boxes here.
What Experience is Worth – Finding Good Data Scientists
Over the years, I’ve hired and worked with some extremely bright young data scientists who came from 12 week bootcamps or even transferred into data science from DevOps with very little or no experience working with real data. Real data always has warts and inconsistencies and myriad other problems. And it takes time to learn how to work with real data. Certainly longer than 12 weeks. It takes years. Here are a few things we can do to ensure hiring qualified data scientists.
Automation and the Perfect Shopping Experience
For this Tech Tuesday, I recorded a video to talk about something I had been hearing about a lot from our clients; the notion of automation. With the pandemic and how it’s affecting the world and the economy, a lot of companies were (and still are) looking to automate some of their processes. I started to think about that word automation. It is a word I don’t use very often – I usually use something like “personalization” or “optimization”. But so I started to think about what automation really means in terms of A.I. and it’s what I will talk about briefly today. Watch the video here.