“I am a careless person,” he said “AI will help me write better feedback.”. “Isn’t it better to try to be thoughful and think before writing?” I said. “But AI can be my teacher. AI is much smarter” he said with absolute certainty. “This could be his self fulfilling prophecy!” I thought and shrugged, ending the conversation. ¯\(ツ)/¯. The “All You Need is AI” Mindset! The previous lines were part of a conversation during a hackathon presentation I recently attended. [Read More]
AI Hype-Driven Development - Parallels in History
Recurring Patterns from Simulmatics to the Large Language Models Hype
In twenty-first-century Silicon Valley, the meaninglessness of the past and the uselessness of history became articles of faith, gleefully performed arrogance. “The only thing that matters is the future,” said the Google and Uber self-driving car designer Anthony Levandowski in 2018. “I don’t even know why we study history. It’s entertaining, I guess—the dinosaurs and the Neanderthals and the Industrial Revolution and stuff like that. But what already happened doesn’t really matter. [Read More]
NOT Just Tweaking Some Parameters
What data practitioners talk about when they talk about deep learning
In the world of data science and machine learning, people tend to talk about the shiny stories, publishable methods, state of the art experiments, the rainbows and butterflies. In reality, practitioners struggle with lots of challenges while learning and applying new methods, but their practical lived experiences are rarely shared for different reasons. I was recently following one of the few examples, in which someone shared their journey to the world of deep learning, without a lot of filtering. [Read More]
The Seeds of Bad Data Products!
The adoption of not-so-scientific facts and the path to harmful algorithms
How do harmful data products and algorithms get enforced on us and impact our lives? a question that is widely discussed nowadays, although I am not sure if “widely” is the right description of the current level of attention it takes. But there is another question that I find crucial which is, How do these products emerge in the first place and gain credibility?, How do crappy senseless products reach the market, proliferate our lives and put us in a reactive mode to analyze and prove their obvious worthlessness or harm? [Read More]
The Joy and Sorrow of the Data Science World
The untold stories of data practitioners!
Story # 1 Jasmin is a data scientist who works at a tech company in a product-oriented team. She likes to work on designing metrics and urges everyone in her team to reason about what they optimize for from a product perspective to link it to the models she builds. Everyone says that she asks good questions and she is tenacious. One day, a manager (non data science oriented) came to her to ask for some charts showing the “improvement” in the model/application performance and relevant metrics to use for a presentation in a company-wide event . [Read More]
Objective Function Engineering as an Interface Design Problem
Individual users control over algorithms?
How many times have you been on a platform with an objective in mind that is a bit different than what the product optimizes for?. I am saying a bit different because I mean the case when you want to use the original features of the platform but with some tweaks that improves your experience. This idea frequently comes up in a form of wishes by users saying “I wish Youtube allowed me to. [Read More]
The teaching/learning experience
Or my path to instructor certification
January 2017, I flew to Orlando to attend Rstudio::conf as my first R conference. I was lucky to have been granted a spot in the “Intermediate Shiny” workshop with Joe Cheng. Not only had I learned more about Shiny, but also about the structure of such intensive workshops and the way of teaching. I was observing these details closely, and I had a clear thought then; I would teach one of these workshops one day, whether in this conference or in other contexts. [Read More]
A Journey into a Team's Workflows
or how to navigate chaos and bring order to data projects!
“I thought my code was clear and organized, but I figured out it was not!", that’s how a data analyst told me after a couple of sessions I held to examine the workflows of different team members and discuss their practices. I like to help people reach this realization to see the value of improving their workflows to them as well as to others with whom they collaborate. And when I say workflows I mean how to organize a project? [Read More]
From H2O to POJO Models
Getting started with a minimal example
A while ago, I was experimenting with h2o and wanted to generate a Plain Old Java Object (POJO) model. I found the documentation useful but I decided to write a post with a simple example for future reference. In this post, we will see how to: build a simple h2o model in R. convert the model to POJO. create a main program in Java to use the POJO model. [Read More]
The Reporting/Dashboarding Dilemma!
Data scientists and dashboards: a complicated relationship
A couple of weeks ago I read a discussion on twitter initiated by a tweet from David Neuzerling who highlighted an observation about data science teams being pushed towards reporting/dashboarding inside organizations. Observation: any data science team will always face pressure from within an organisation to become a reporting/dashboarding team. — David Neuzerling (@mdneuzerling) June 19, 2019 I had some reflections from a previous experience and various discussions so I thought about gathering them in a blog post for future reference and further discussions. [Read More]