Editor's Picks

• A CSV file with 100,000 rows?
  If someone gives you a CSV file with 100,000 rows in it, what tools do you use to start exploring and understanding that data? [Twitter thread and ressponses]...

• Collective Intelligence for Deep Learning: A Survey of Recent Developments
  We survey ideas from complex systems such as swarm intelligence, self-organization, and emergent behavior that are gaining traction in ML...In the last few years, I have been noticing many works in deep learning research pop up that have been using some of these ideas from collective intelligence, in particular, the area of emergent complex systems. Recently, Yujin Tang and I put together a survey paper called Collective intelligence for deep learning: A survey of recent developments about this topic, and in this post, I will summarize the key themes in our paper...

• Build a Career in Data Science Podcast
  Build a Career in Data Science teaches you what data science courses leave out: from how to land your first job to the lifecycle of a data science project and even how to become a manager. This is a true how-to on obtaining and then navigating a data science career--filled with real stories from data scientists. This podcast is an extension of the similarly named book: Build a Career in Data Science...

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Data Science Articles & Videos

• Getting tabular data from unstructured text with GPT-3: an ongoing experiment
  One of the most exciting applications of AI in journalism is the creation of structured data from unstructured text...Government reports, legal documents, emails, memos...these are rich with content like names, organizations, dates, and prices. But to get them into a format that can be analyzed and counted, like a spreadsheet, usually involves days or weeks of tedious manual data entry...Large language models like GPT-3 from OpenAI have the potential to greatly speed up this awful slog. Because these models have such a deep grasp of language (GPT-3 was trained on basically the entire internet — at least all of English Wikipedia), they can understand commands and pick out the right elements from text....

• Andrej Karpathy: Building makemore Part 3: Activations & Gradients, BatchNorm
  We dive into some of the internals of MLPs with multiple layers and scrutinize the statistics of the forward pass activations, backward pass gradients, and some of the pitfalls when they are improperly scaled. We also look at the typical diagnostic tools and visualizations you'd want to use to understand the health of your deep network. We learn why training deep neural nets can be fragile and introduce the first modern innovation that made doing so much easier: Batch Normalization...

• Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
  A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over the last years, making it challenging for human researchers to keep track of the progress. Here, we use AI techniques to predict the future research directions of AI itself...

• Discovering novel algorithms with AlphaTensor
  In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices...

• DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
  We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that image. The significance is that we achieve this zero-shot using DALL-E, without needing any further data collection or training...

• latexify_py
  A Python package that generates LaTeX math description from Python functions....

• Forrester changed the way they think about data catalogs, and here’s what you need to know
  This summer, Forrester scrapped its Wave report on “Machine Learning Data Catalogs” to make way for one on “Enterprise Data Catalogs for DataOps”. Here’s everything you need to know about where this change came from, why it happened, and what it means for modern metadata...

• End-to-end Neural Coreference Resolution in spaCy
  Coreference resolution is something all of us do instinctively many times every day even though most of us haven’t heard the term before. People use language to talk about entities, events and the relationships between them. When we mention the same thing multiple times throughout a discourse we tend to use different expressions...

• Large Language Models are Human-Level Prompt Engineers
  We propose an algorithm for automatic instruction generation and selection for large language models with human level performance...

• Erik Bernhardsson and Etienne Dilocker on Vector Search in Production
  This is a really special episode with Erik Bernhardsson! Erik is one of the early thought leaders on Approximate Nearest Neighbor (ANN) Search, creating the ANNOY library at Spotify. Erik shared incredible insights about vector search at Spotify such as the role of Offline and Online Machine Learning inference and the role of multi-stage re-ranking pipelines. Erik has also done massively impactful work on benchmarking ANN algorithms! We really hope you enjoy the podcast and would be thrilled to answer any questions you have about the conversation topics!...

• Apple's "Human Interface Guidelines for Charts"
  Human Interface Guidelines for Charts are here! We've been working on these pages for a bit—hope you find them useful 📊🥳 ... Including: Patterns page and Components pages...

• Blueprint for an AI Bill of Rights - Making Automated Systems Work For The American People
  The White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats—and uses technologies in ways that reinforce our highest values....

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Conference*

Jobs

• Data Scientist - Mount Sinai Data Commons - NYC
  
  A position is available for an individual with skills in data science, bioinformatics and software engineering to play the key role in running and managing the Mount Sinai Data Commons – known as the Data Ark. The Data Ark team brings together all the most important data sets used by Sinai researchers (e.g. 1000G, GTEx, UK Biobank) in a single location on our HPC server (minvera.org), performs QA/QC processing of the data, conducts initial demographics analyses to showcase the different data sets, and will be tasked with expanding the data commons to host a large range of different data sets of different types (genotype, WES, WGS, RNA-seq, EHR-linked, imaging etc.), which will come with their own computational and platform challenges...
   

Training & Resources

• The Illustrated Stable Diffusion
  The ability to create striking visuals from text descriptions has a magical quality to it and points clearly to a shift in how humans create art. The release of Stable Diffusion is a clear milestone in this development because it made a high-performance model available to the masses (performance in terms of image quality, as well as speed and relatively low resource/memory requirements)...After experimenting with AI image generation, you may start to wonder how it works...This is a gentle introduction to how Stable Diffusion works...

• How diffusion models work: the math from scratch
  In this blog post, we will dig our way up from the basic principles. There are already a bunch of different diffusion-based architectures. We will focus on the most prominent one, which is the Denoising Diffusion Probabilistic Models (DDPM) as initialized by Sohl-Dickstein et al and then proposed by Ho. et al 2020. Various other approaches will be discussed to a smaller extent such as stable diffusion and score-based models...

• Global Pooling in Convolutional Neural Networks
  In this article, we explore what global average and max pooling entail. We discuss why they have come to be used and how they measure up against one another. We also developed an intuition into why they work by performing a biopsy of our convnets and visualizing intermediate layers...

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Last Week's Newsletter's 3 Most Clicked Links

• Uncertainty in Deep Learning — Brief Introduction

• Reflecting on NLP gigs, thought I'd summarize a few things an NLP consultant can be expected to know about. 🧵

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Cutting Room Floor

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