How do people learn the large, complex web of social relations around them? We test how people use information about social features (such as being part of the same club or sharing hobbies) to fill in gaps in their knowledge of friendships and to make inferences about unobserved friendships in the social network. We find the ability to infer friendships depends on a simple but inflexible heuristic that infers friendship when two people share the same features, and a more complex but flexible cognitive map that encodes relationships between features rather than between people. Our results reveal that cognitive maps play a powerful role in shaping how people represent and reason about relationships in a social network.
Hmm. Can’t download the PDF or read the full article
The spread of misinformation is a global phenomenon, with implications for elections, state-sanctioned violence, and health outcomes. Yet, even though scholars have investigated the capacity of fact-checking to reduce belief in misinformation, little evidence exists on the global effectiveness of this approach. We describe fact-checking experiments conducted simultaneously in Argentina, Nigeria, South Africa, and the United Kingdom, in which we studied whether fact-checking can durably reduce belief in misinformation. In total, we evaluated 22 fact-checks, including two that were tested in all four countries. Fact-checking reduced belief in misinformation, with most effects still apparent more than 2 weeks later. A meta-analytic procedure indicates that fact-checks reduced belief in misinformation by at least 0.59 points on a 5-point scale. Exposure to misinformation, however, only increased false beliefs by less than 0.07 points on the same scale. Across continents, fact-checks reduce belief in misinformation, often durably so.
Finished reading in Andreea’s data. I’m going to add a column called ‘test’, that has some text in it to judge the quality of training. I’m going to start out with ‘ten’, ‘twenty’, ‘thirty’, and ‘forty’, which will show up in those percentages. We’ll be able to compare the percentages in the generated and the original. Done with the original
Create corpora and start training model.
Done! Need to verify the test percentages
[[[month:August location:Auckland text:@rnz_news @NZStuff @minhealthnz @NewshubNZ @jacindaardern @simonjbridges @nzlabour I have a few theories but they are completely illogical. My theory is that many in government and opposition are too trusting, while many in the media are too partisan. #covid19nz #covid19_nz #nzpol, test:twenty]]][[[month:August location:New Zealand text:Dr Liz Gordon: NZ’s Covid-19 response a failure
[[[month:April location:New Zealand text:“As they travel around the world, as we go back to the U.S., it is critical that they be able to meet with health officials and other trusted advisers to update their status”. https://t.co/7kZk6HtRcV #covid19nz #Healthandsafety, test:forty]]][[[month:April location:Wellington City, New Zealand text:It's getting harder and harder to resist the temptation to throw shade at the PM's leadership. She's deliberately and deliberately slipping up
[[[month:April location:Wellington, New Zealand text:New Zealand will now have a COVID-19 emergency alert system. A system based on scientific certainty, based on best informed research. The sooner we use science the sooner we’ll all get back to normal life. This is a global challenge. https://t.co/YFpI6zWgv7 #coronavirus #COVID19nz, test:forty]]][[[month:April location:Wellington text:New Zealand is now in #COVID19nz mode. The system works
[[[month:April location:New Zealand text:A month of #Covid19nz has taught me to trust #SocialDistancing and not to accept #selfishness. So much of #NZtourism comes from poor, vulnerable, and elderly people. If you or someone you know has #Covid19NZ symptoms, please report them to contact tracing at 0800 451 9453 https://t.co/Jw2i0U8Jqn, test:forty]]][[[month:April location:New Zealand text:@MatthewHootonNZ @TheAMShowNZ
[[[month:May location:Auckland, NZ text:#coronavirusnz #COVID19nz One of the new covid-19 cases reported in Queenstown this week is a case in the community. https://t.co/XmqEZd2aDw, test:forty]]][[[month:May location:Muriwai, Aotearoa text:Māori Health Minister Māori Party @RikkiRakaka @nzlabour https://t.co/gwqEZqNrA7 #COVID19 #CO
[[[month:May location:Wellington, New Zealand text:This is a welcome relief to many. Here's an idea: don't just sell as much as you can. Instead, take out the cash and start collecting. #COVID19nz, test:forty]]][[[month:May location:0 text:Can't say my children are very good at math - and in math classes I find they get lots of confused - but when I read someone ask them "how many years of age do they still live with?", they instantly burst into laughter. #nzpol #covid19nz
[[[month:April location:Auckland, New Zealand text:Great article by @Kiwi_Country to explain the importance of #COVID19nz and how to use your personal details to protect your community. Great info in the article https://t.co/v9L0GtX8z3 https://t.co/s3Nm3z3qCZ, test:ten]]][[[month:April location:Auckland, New Zealand text:My thoughts: #covid19NZ #NewZealandLockdown https://t.co
[[[month:June location:Aotearoa, New Zealand text:?♂️ #Covid_19 #COVID19nz https://t.co/Nb8Cq8xFZJ, test:forty]]][[[month:June location:Te Upoko o Te Ika a Maui text:Māori #COVID19nz #lockdownnz https://t.co/tS9QjkFZhM, test:forty]]][[[month:June location:Christchurch City, New Zealand text:What
[[[month:June location:Auckland, New Zealand text:It's important to be clear about the amount of work we can do to safeguard the community and the health and wellbeing of NZers. Read this: https://t.co/vh0MxwKlG7 #COVID19nz, test:forty]]][[[month:June location:New Zealand text:“All the good work that the Govt's emergency plans have done” @SiouxsieW #covid19nz https://t.co/fDZFdZJ0
[[[month:March location:Wellington City, New Zealand text:RT TheDailyBlogNZ "Life in Lock Down: Day 2 | Frank Macskasy - The Daily Blog https://t.co/4tP8dZZWmA #nzpol #covid19nz https://t.co/HmDw5BcE5j", test:forty]]][[[month:March location:0 text:Life in Lock Down: Day 2 | Frank Macskasy - The Daily Blog https://t.co/xWZFyLk3
[[[month:April location:New Zealand text:MEDIA WATCH: Jacinda destroys Duncan Garner | The Daily Blog https://t.co/Hk5VZ9oTkC #nzpol #covid19nz https://t.co/p7tMkLH9Rk", test:ten]]][[[month:April location:New Zealand text:GUEST BLOG: Geoff Simmons – The Price of Citizenship | The Daily Blog https://t.co/JKc0NqEqH #nzpol #covid19nz https://t.
4:15 UMBC Meeting. We’ll try French, Chinese, (and Mexican) to see if the ratings change
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
Started on importer
Send out emails to agents!
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
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
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
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
Go over untrained model results
See if we can make the chess models talk about having tea with the Queen
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
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
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
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?
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?
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)
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.
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
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