Issue #488
March 30 2023

Hello and thank you for tuning in to Issue #488.

This is Hannah and Sebastian, curators of the Data Science Weekly newsletter.

We appreciate your support :)

Once a week we write this email to share the links we thought were worth sharing in the Data Science, ML, AI, Data Visualization, and ML/Data Engineering worlds.

If you find this useful, please consider becoming paid subscriber here:
https://datascienceweekly.substack.com/subscribe

Hope you enjoy it.

.

And now, let's dive into some interesting links from this week:

Editor's Picks

• My Objections to "We’re All Gonna Die with Eliezer Yudkowsky"
  I recently watched Eliezer Yudkowsky's appearance on the Bankless podcast, where he argued that AI was nigh-certain to end humanity…As an AI "alignment insider" whose current estimate of doom is around 5%, I wrote this post to explain some of my many objections to Yudkowsky's specific arguments. I've split this post into chronologically ordered segments of the podcast in which Yudkowsky makes one or more claims with which I particularly disagree…
  

• How to be socially impactful and financially successful in your data career — with Josh Wills [Video]
  Angel investor and data science consultant Josh Wills sits down with @JonKrohnLearns to discuss his former roles (Google, Slack, and Cloudera) and the essential skills for engineering scalable machine learning projects…
  

• What's new in the tidyverse - Isabella Velasquez [Video]
  In this video, Isabella will tell you about what’s new in the tidyverse…Recently, Tidyverse has undergone some changes and updates to make it even more user-friendly and powerful. The changes to Tidyverse include new packages, updates to existing ones, and improvements in performance and functionality. Some of the most notable updates include enhancements to package dependencies, performance improvements for specific functions such as group_by(), and the addition of new packages such as ggplot2, readr and dplyr…
  

A Message from this week's Sponsor:

Pinecone vector database

The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.

Use Pinecone to build semantic search, object recognition, recommendations, anomaly detection, and other vector-based functionality into your applications.

Want to sponsor the newsletter? Email us for details --> team@datascienceweekly.org

Data Science Articles & Videos

• Data Management in Large-Scale Education Research
  My hope is that this book can be a foundation to help researchers think through how to build a quality, standardized data management workflow that works for their team and their projects. As suggested in the title of this book, this content is designed to specifically help teams navigate the complicated workflows associated with large-scale research studies, such as randomized controlled trial studies, but ultimately these practices are applicable to any research project, no matter the scale…
  

• My brain has been melting over the past week or so--at first slowly and then faster by the day--over GPT-4 and all the possibilities it unlocks [Twitter Thread]
  I'll have a lot to say, but right now my primary thought is -- we all just became product designers. And specifically "data product designers" (whatever that was, which isn't even relevant now)…
  

• Low Light Computer Vision
  Today I learned that folks are trying to get computer vision to work in (extreme) low-light settings…It turns out it’s a tricky problem, but there’s a group of researchers that made an interesting development for pose estimation. Part of their effort revolved around gathering a new dataset (ExLPose). The trick in this dataset is that they gathered pairs of images, from the same camera with fancy hardware, that represent the low- and highlight setting…
  

• Stable Diffusion Newsletter - STABLE DIGEST #3
  Artist spotlight - Sagans join us straight from the studios of Linkin Park, Lorn and Die Antwoord for an AI animation deep dive…
  

• Leaving Google Brain
  After about 3.3 years of being at Google Brain and Research, I’ve decided to part ways and move on to my next adventure. In my own eyes, this indeed feels like graduation. After all, I have learned a lot from Google, from my wonderful colleagues, mentors, managers etc…
  

• The Real Python Podcast - Episode 150: Lessons Learned From Four Years Programming With Python
  Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk…We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year. We also dig into the four major themes of the talk. Along the way, we share a collection of resources to help you continue learning on your Python journey…
  

• Developer Tools 2.0
  Generative AI stands to change how work happens in one industry after another. But software engineering’s transformation isn’t done yet…Report by Sequoia Capital…
  

• The public imagination: OpenAI shouldn't be an app store. It should be a hardware store.
  OpenAI launched an app store…It’s a bold step—but it feels like either a mistake or misdirection. Because public AI providers like OpenAI aren’t destined to become the next iPhone, but the next—and maybe, much bigger—AWS…
  

• FOMO on the rapid pace of LLMs [Reddit /r/MachineLearning Discussion]
  Despite my background in "classical" ML, I'm feeling some anxiety about the rapid pace of LLM development and face a fear of missing out / being left behind…I thought I might not be the only one being humbled by the recent advances in ChatGPT, etc. and wanted to hear how other people feel / are getting involved…
  

• Lerrel Pinto : A Constructivist’s Guide to Robot Learning [Video]
  In this talk I will argue for the need for better constructivist approaches to robotics, i.e. techniques that take guidance from humans while allowing robots to continuously adapt in changing scenarios. The constructivist guide I propose will focus on three elements. First, creating physical interfaces to allow humans to provide robots with rich and dexterous data. Second, developing adaptive learning mechanisms to allow robots to continually fine-tune in their environments. Third, architecting models that allow robots to learn from un-curated play…
  

• Understanding what attention does [Twitter Thread]
  We held a reading group on Transformers (watched videos / read blog posts / studied papers by @giffmana @karpathy @ch402 @amaarora @JayAlammar @srush_nlp et al.), and now I _finally_ roughly understand what attention does. Here is my take on it. A summary thread. 1/n…
  

• How to learn the "Math language" [Reddit /r/DataScience Discussion]
  I'm studying Machine Learning, and I came across several mathematical formulas and I would like to understand them, not just learn how to use and apply the formulas…How can I begin to understand more about math so that I understand more, in the same way that I see an English sentence and understand its meaning?…
  

Jobs

Data Scientists and Engineers
Number 10 Downing Street, The UK Prime Minister's Office

The No10 data science team, 10DS, offers an unparalleled opportunity to develop your career personally through these demanding and intellectually stimulating roles. Formed in mid-2020, 10DS has a remit to radically improve the way in which key decisions are informed by data, analysis and evidence. We are looking for exceptional candidates with great mathematical reasoning. In return you will be provided an unparalleled opportunity to develop your technical skills and support advice to help improve your country…

Apply here
 

Want to post a job here? Email us for details --> team@datascienceweekly.org

Training & Resources

• Awesome PyLadies’ Blogs
  This repository provides a curated list of awesome blogs by PyLadies and also seeks to collect information to further promote blog posts by awesome PyLadies on Mastodon…
  

• Awesome R-Ladies’ Blogs
  This repository provides a curated list of awesome blogs by R-Ladies…
  

• Efficient Methods for Natural Language Processing: A Survey
  This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods… 
  

Last Week's Newsletter's 3 Most Clicked Links

• AI trends in 2023: Graph Neural Networks

• Comparing List Comprehensions vs. Built-In Functions in Python: Which Is Better?

• The Bayesian Killer App

* Based on unique clicks.
** Find last week's issue #487 here.
 

Cutting Room Floor

• Here’s the ChatGPT prompt that reduced my meal-planning time by 90%

• WiDS Workshop Leader Application: Apply to be a WiDS Workshop instructor

• When Do Startups Scale? Large-scale Evidence from Job Postings

• An aperiodic monotile - a shape that admits tilings of the plane, but never periodic tilings

• Introducing Gestalt principles to optimize spatial visualizations, specifically joint visualization of multi-dimensional data

• unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network

• A generation defining AI moment

• Hello Dolly: Democratizing the magic of ChatGPT with open models

• GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document [Video]

• What Power BI is still missing

Thanks for joining us this week :)

All our best,
Hannah & Sebastian

P.S.,
If you enjoyed reading this,
please consider becoming paid subscriber here:
https://datascienceweekly.substack.com/subscribe

:)

Copyright © 2013-2023 DataScienceWeekly.org, All rights reserved.