Illuminating Diverse Neural Cellular Automata for Level Generation

• We present a method of generating a collection of neural cellular automata (NCA) to design video game levels. While NCAs have so far only been trained via supervised learning, we present a quality diversity (QD) approach to generating a collection of NCA level generators. By framing the problem as a QD problem, our approach can train diverse level generators, whose output levels vary based on aesthetic or functional criteria. To efficiently generate NCAs, we train generators via Covariance Matrix Adaptation MAP-Elites (CMA-ME), a quality diversity algorithm which specializes in continuous search spaces. We apply our new method to generate level generators for several 2D tile-based games: a maze game, Sokoban, and Zelda. Our results show that CMA-ME can generate small NCAs that are diverse yet capable, often satisfying complex solvability criteria for deterministic agents. We compare against a Compositional Pattern-Producing Network (CPPN) baseline trained to produce diverse collections of generators and show that the NCA representation yields a better exploration of level-space.
• This could be an interesting scenario generator

GPT Agents

• Started on importer

Book

• Send out emails to agents!

SBIRs

• Got all the stories done. Need to assign points, etc.
• 1:00 Sprint planning meeting
• Decided to try to put everything into a TKinter app. I already know the framework pretty well, I just need to brush up. This way I’ll be able to reuse a lot of the GraphNavigator code
• Maybe this?

• Today’s progress:

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

SBIRs

• 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:

node
  [
    id 0
    label "Bahamas"
    graphics
    [
      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"
  ]

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,

GPT-Agents

• 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!

SBIRs

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

Book

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

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

SBIRs

• 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

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

More reviews

SBIRs

• 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

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

SBIRs

• 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

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 Distill.pub! 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.

Book

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

SBIRs

• 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

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.

SBIRs

• 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

• That creates the following structure:

• Then to run the app, I use the terminal and use <ctrl> enter:

• 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?

SBIRs

• 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?

JuryRoom

• 7:00 Meeting

So we’re officially done in Afghanistan now? One of these years, I’m going to try to figure out what the response to 9/11 cost, what the expectations were, and what actually happened

SBIRs

• Working with Zach on the webapp. We may be able to do all this with websockets and no server
• Sprint planning – done
• Starting on websockets. Installed websockets. I installed asyncio, but it’s part of Python. That’s nice! Uninstalled and everything still works
• The hello world works!

• Took a detour down SSL and got stuck on cert format issues? Look at that later
• Sending data to the browser:

That works too!

GPT-Agents

• Still cranking on generating reviews with the untrained model
• 3:00 Meeting. Made a bet with Shimei that the 800k chess model has forgotten that the Queen could drink tea. We’ll see if we can prompt the model to talk about something other than chess next week

If you want to summarize your research in a sentence… have an AI do it. SciTLDR sums up papers given an abstract, intro & conclusion. And it works impressively well: https://scitldr.apps.allenai.org (Via Twitter)

The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers

• Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically. Here we demonstrate that by revisiting model configurations as basic as scaling of embeddings, early stopping, relative positional embedding, and Universal Transformer variants, we can drastically improve the performance of Transformers on systematic generalization. We report improvements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematics dataset. Our models improve accuracy from 50% to 85% on the PCFG productivity split, and from 35% to 81% on COGS. On SCAN, relative positional embedding largely mitigates the EOS decision problem (Newman et al., 2020), yielding 100% accuracy on the length split with a cutoff at 26. Importantly, performance differences between these models are typically invisible on the IID data split. This calls for proper generalization validation sets for developing neural networks that generalize systematically. We publicly release the code to reproduce our results.

SBIRs

• Got the client communicating with the server using Websockets and the server relaying those messages to RabbitMQ!

• Sprint Demos and story writing today
• Starting to look at Docker for this effort

GPT Agents

• Finish 1-5 star parser and start run on GPT-large, then GPT. Curious what we’ll get
  • Verified that everything seems to be working on a small run. Lots of parsing to get star values
  • Tring a full-sized run of 100 batches of 10 experiments with 10 return sequences
• OpenAI: The fine-tuning endpoint is now ready, and we’re excited to share it with you! Here’s how to get started: link

ETA Prediction with Graph Neural Networks in Google Maps

• Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modelling both the topological properties of the road network and anticipating events — such as rush hours — that may occur in the future). Hence, it is an ideal target for graph representation learning at scale. Here we present a graph neural network estimator for estimated time of arrival (ETA) which we have deployed in production at Google Maps. While our main architecture consists of standard GNN building blocks, we further detail the usage of training schedule methods such as MetaGradients in order to make our model robust and production-ready. We also provide prescriptive studies: ablating on various architectural decisions and training regimes, and qualitative analyses on real-world situations where our model provides a competitive edge. Our GNN proved powerful when deployed, significantly reducing negative ETA outcomes in several regions compared to the previous production baseline (40+% in cities like Sydney).
• I think that the GNNs should be usable to produce the maps themselves. Need to try this with simulation

Created a folder for Graph Neural Network research

Ride down to DC today for this and hopefully not get wet!

Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

• Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks. We host the results and videos at this https URL and isaac gym can be downloaded at this https URL.

SBIRs

• 1:00 meeting with Rukan
• Write some on the paper
• Do slides for demos
  • Add ‘assist Steve’ story
• Update repo and switch to dev. Verify that everything still works – it does! And receives messages as well. Oddly it seems to b e splitting the messages between the Python and TypeScript listeners:

GPT Agents

• Make some spreadsheets that compare the stars/sentiment properties of the relative models. Done. The models are remarkably stable, even down to 3k. They make more mistakes with the specific meta training but that seems to be about it?
• Trying to generate reviews from the untrained gpt2 models. The 117M model was (probably?) too small, so I’m trying the 774M model without finetuning. It requires two passes – the first creates the review (using a bigger prompt), and then I use the result and tack on “{}. I give it a star rating of“. Then I need to parse the ratings, which can be numbers or strings. I’ve kind of run out of energy so I’ll finish later.
• Start trying to figure out a posterior test?

SBIRs

• Continuing to make good progress with Zach on the webapp
• Here are a LOT of resources on self-play and reinforcement learning
  • How to build your own AlphaZero AI using Python and Keras
  • AlphaGo cheat sheet
  • AlphaGo Zero – How and Why it Works
  • How To Build Your Own AI To Play Any Board Game
  • MuZero: The Walkthrough (Part 1, Part 2, Part 3)

GPT Agents

• Brought all the model outputs from LIWC (manual here) and put them into a single spreadsheet. All the models are surprisingly stable, except for word count (WC):

• Here are some values beyond WC:
  • Analytical thinking — a high number reflects formal, logical, and hierarchical thinking; lower numbers reflect more informal, personal, here-and-now, and narrative thinking
  • Clout — a high number suggests that the author is speaking from the perspective of high expertise and is confident; low Clout numbers suggest a more tentative, humble, even anxious style.
  • Authentic — higher numbers are associated with a more honest, personal, and disclosing text; lower numbers suggest a more guarded, distanced form of discourse.
  • Emotional tone — a high number is associated with a more positive, upbeat style; a low number reveals greater anxiety, sadness, or hostility. A number around 50 suggests either a lack of emotionality or different levels of ambivalence

GPT Agents

• https://www.pnas.org/authors/submitting-your-manuscript – set up a paper repo in Overleaf and start to rough out
• Need to get the spreadsheets built for the 3k and 6k models. Done. This is pretty cool:

• Build a spreadsheet (and template?) for the LWIWC data

SBIRs

• 7:30 meeting with Zach. Good progress. We’re almost using RabbitMQ to talk between server-side TypeScript and server-side Python. And we made a cool diagram

JuryRoom

• 7:00 meeting