Monthly Archives: September 2021

Phil 9.13.2021

GPT Agents

  • Fixing CR/LF in db, and re-running analytics
  • Meeting with Andreea and her student, ___. We’re going to train up a model on their NZ twitter corpora


  • Updated last sprints stories and put together slides for demos
  • Work on stories for next sprint
  • Work on getting more content into GML files. Got it working:
node [
    id 1
    label "Canada"
    weight 150222.0
    long_text "A random number: 0.13436424411240122"
  • And after going through Gephi and getting positions, colors, and sizes:
    id 0
    label "Bahamas"
      x 78.24309
      y 161.46931
      z 0.0
      w 20.0
      h 20.0
      d 20.0
      fill "#edf8fb"
    weight "4179.0"
    long_text "A random number: 0.763774618976614"

Phil 9.10.2021

Finish reviews! DONE!

Papers with Code Newsletter #16

  • Welcome to the 16th issue of the Papers with Code newsletter. In this edition, we cover:
    • some of the latest developments in language modeling,
    • efficient Transformer models for long text modeling,
    • advancements in code understanding and generation,
    • top trending ML papers of August 2021,


  • Created a table of filtered results (%coronavirus%, %chinavirus%, and %sars-cov-2%) with 1,000 of each and ran sentiment to compare
  • Well crap, the carriage returns in the ground truth are messing everything up. Need to write come code to pull, fix and put back into the table. Not today!


  • Write new stories
  • Continue working on storing additional information in networkx nodes


  • 2:00 Meeting with Michelle. Finish cover letters! Done! Maybe? Tweaked a bit more

Phil 9.9.2021

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.

Getting started with 3D content for synthetic data (Unity)

More reviews


  • 9:15 Standup. Not sure what to talk about here given the new schedule crazyness
    • It also occurs to me that since I’ll be adapting my academic research code to produce the demo, there’s no IP for anyone being developed for this effort.
  • More poking at Svelte with Zach? Some progress. Still can’t get to switch pages
  • 11:00 Kickoff meeting – looks like we have a bit more time
  • 2:00 Adversarial reinforcement tagup

GPT Agents

  • Need to generate new tweets from the chinavirus, covid, and sars-cov-2 models using the prompt ‘[[[‘ as a baseline to compare with the ground truth – done!
  • Need to sample ground truth and put it in the gpt_experiments tables

Phil 9.8.2021

Need to tell the shop that it’s a 2016 Promaster

More reviews


  • Made some progress on Svelte, but still stuck on routing. Talking to Zach
  • Meeting about slides. Or schedule has shrunk from 3 months to six weeks. Massive shift in plans and proposal

GPT Agents

  • Go over untrained model results
  • See if we can make the chess models talk about having tea with the Queen. I win!
  • Need to generate new tweets from the chinavirus, covid, and sars-cov-2 models using the prompt ‘[[[‘ as a baseline to compare with the ground truth

Phil 9.7.2021

WikiGraphs: A Wikipedia Text – Knowledge Graph Paired Dataset

  • We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement.

Truck stuff – need to verify that they know it’s a 2016

Reviewing papers


  • Continuing to work on Svelte. Trying to get previous useful lessons to show up as pages, but they are svelte files, not HTML, so I’m not sure how to point to them
  • Pre-meeting
    • Scheduling. Orest wants to finish Oct 29, but we’re already a week into September, so I’m going to counter with Nov 5
    • Get slides done for Thurs meeting. Tried to get MARCOM to help with formatting, but the fuse is too short
    • Orest set up a meeting that conflicts with the GPT meeting. Trying to get him to move it, otherwise send a note that I will be about 15 min late

GPT Agents

  • Go over untrained model results
  • See if we can make the chess models talk about having tea with the Queen

Phil 9.3.2021

It’s September, and after weeks of humidity and 90+ highs, a storm passed through and left ups with clear blue skies, cool nights, and beautiful days.

New article on! A Gentle Introduction to Graph Neural Networks

  • Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network – and motivate the design choices behind them.


  • Working on tweaks for today’s meeting
  • 2:00 Meeting


  • Continue with Svelte
  • I seem to have been able to get typescript set up and running:
  • Which gives us this:
  • Work on finding a venue for the automating imagination paper
  • OED Definition of imagination:
    • The power or capacity to form internal images or ideas of objects and situations not actually present to the senses, including remembered objects and situations, and those constructed by mentally combining or projecting images of previously experienced qualities, objects, and situations. Also (esp. in modern philosophy): the power or capacity by which the mind integrates sensory data in the process of perception.
  • Also, using GNNs as ways of storing the relationships between the text generated by the GPT

Phil 9.2.2021

I Asked GPT-3 About Covid-19. Its Responses Shocked Me. Generative AI systems could guide future pandemic decision-makers

  • No public health authority should rely on an AI system to make recommendations, of course. But as they grow in power and reach, AI systems could become another tool in leaders’ belts, allowing them to quickly parse existing scientific knowledge for insights that could help to guide in-the-moment decision-making. As the systems become better at citing their sources and explaining their output, their value as tools for guiding decision-making will only grow, because the validity of their predictions can be checked and vetted.


  • 7:30 Meeting with Zach. I’m going to see if he agrees with the “front-end-first” approach I’d like to try. He agrees, so I’m working my way through the tutotial
  • To install a template project as per here, you have to use the git command line app
Installing the template project from the GIT command line
  • That creates the following structure:
Project structure in IntelliJ
  • Then to run the app, I use the terminal and use <ctrl> enter:
Getting things running
  • This handles hot deployment in the browser, so I think I’m doing it right?
  • This is pretty cool. Branching logic for HTML:
  • And looping!
  • 2:00 Meeting with Rukan & Aaron?

Phil 9.1.2021


  • Working with Zach to set up websocket-based project. Slow going today as we tried to figure out exactly how we want to set up the project
  • Working on the getting started guide from websockets
  • Developing with asyncio
  • Looking more deeply at Svelte and thinking about building a standalone frontend that doesn’t interact with websockets, but fakes the functionality so that when the Python connections are added in it works?


  • 7:00 Meeting