In brief:Reality Team runs ads on Instagram designed to limit the influence of disinformation. We developed a method to run randomized control trials to test the impact on knowledge and opinions about climate and covid vaccines. We saw very significant increases in knowledge 24–72 hours post exposure to a single viewing of a 10 second video ad, and shifts in opinions 7–18 days later within a specific audience of Passive Information Consumers.
Book
Writing a bit more on Age Dominance
While reading Hierarchy in the Forest, I realized that Egalitarianism in bands is probably a form of Nash Equilibrium. Which is wild, since my coevolution of weapons and agression turned out to be the iterated prisoner’s dilemma.
SBIRs
More DARPA abstract. Check to see if the LAIC Phase II proposal has any good text
How is the BigScience 176B model trained: a visual overview of the hardware and parallelism setup https://t.co/jX68m4UGVp— BigScience Large Model Training (@BigScienceLLM) March 23, 2022
Book
Finished the first pass at age bias. I still want to add examples of age dominance like
Continue with code generator. I think I need to set up the hmodule class explicitly, rather than having them store a count. This will allow multiple nodes to have multiple children and generate the correct connections
Good progress. Starting to create modules and connect them in bdmon
1:00 Dev meeting
Look at resumes and send Orest an example of what we’re looking for
Time series analysis has proven to be a powerful method to characterize several phenomena in biological, neural and socio-economic systems, and to understand their underlying dynamical features. Despite a plethora of methods having been proposed for the analysis of multivariate time series, most of them do not investigate whether signals result from independent, pairwise, or group interactions. Here, we propose a novel framework to characterize the temporal evolution of higher-order dependencies within multivariate time series. Using network analysis and topology, we show that, unlike traditional tools, our framework robustly differentiates various spatiotemporal regimes of coupled chaotic maps, including chaotic dynamical phases and various types of synchronization. By analysing fMRI signals, we find that, during rest, the human brain mainly oscillates between chaotic and few partially intermittent states, with higher-order structures reflecting sensorimotor areas. Similarly, in financial and epidemic time series, instead, higher-order information efficiently discriminates between radically different coordination and spreading regimes. Overall, our approach sheds new light on the higher-order organization of multivariate time series, allowing for a better characterization of dynamical group dependencies inherent to real-world systems.
SBIRs
8:30 Meeting
9:15 standup + went over generator concept
2:00 meeting with Ron
Need to set up overleaf project and add meeting notes section – in progress
Continue on code generator
Here’s my fancy piece of code for the dat that sets attributes from a dict:
class HierarchyModule: quantity: int name: str parent: str commands:List
Worked with Rukan on the RCSNN test implementation. You CANNOT have two enum classes with some of the same elements and get an equality between the two
Chat with Loren about the stunt fom and how the various pieces work together. We’re goring to need some kind of table that describes the behavior of each of the agents
TriMapis a dimensionality reduction method that forms a low-dimensional embedding of data by minimizing a contrastive loss over a set of triplets. The triplets are sampled from the original high-dimensional data representation and are weighted based on the distances between the (closer and farther) pairs of points. Although t-SNE and UMAP are excellent methods for forming low-dimensional embeddings, TriMap provides an alternative view of the data which is more representative “globally”. Specifically, TriMap is able to:
reflect the relative placement of the clusters in high-dimension,
reveal possible outliers and anomalies in the data,
generate embeddings that are more robust to certain transformations (see here for more details).
Working on ages of presidents and kings. Below is a chart of the Kings of England from 1066 – 1830, split into groups based on the age they became king. The blue bar is the number of rulers and the red is the number who were successful in that their reign lasted longer than 10 years:
A different way of looking at this is what is the percentage of successful rulers given the age they became king?
SBIRs
9:15 standup
Kickoff with Lauren
Meeting with Lambda, they’ll get back by next Tuesday
Set up project main and three controllers with Rukan
11:00 SSDS meeting
1:00 Server meeting (also, connect back with Lambda!) Set up meeting for tomorrw
Book
More Age dominance. Went down a small rabbit hole about age and competence. Did you know that there is almost no relationship between age and historical ratings of US presidents? If anything, younger seems to be a bit better.
By going as close as we dared, we have still crossed a grey moral boundary, demonstrating that it is possible to design virtual potential toxic molecules without much in the way of effort, time or computational resources. We can easily erase the thousands of molecules we created, but we cannot delete the knowledge of how to recreate them.
Drop the computer off for upgrade today? Maybe Thursday would be better. Rain
We present a new dataset built on prior work consisting of 1,671,370 randomly sampled pages of English-language prose roughly divided between modes of fictional and non-fictional writing and published between the years 1800 and 2000. In addition to focusing on the “page’’ as the basic bibliographic unit, our work employs a single predictive model for the historical period under consideration in contrast to prior work. Besides publication metadata, we also provide an enriched feature set of 107 features including part-of-speech tags, sentiment scores, word supersenses and more. Our data is designed to give researchers in the digital humanities large yet portable random samples of historical writing across two foundational modes of English prose writing. We present initial insights into transformations of linguistic patterns across this historical period using our enriched features as possible pointers to future work. The data can be accessed at https://doi.org/10.7910/DVN/HAKKUA.
Rhinocéros! presents a small town overrun with radical ideas, clashing ideology and not so subtle transformations. When Beringer, a local drunk, finds himself surrounded by neighbors who are slowly turning into giant beasts, he’s forced to navigate a new world where the rights of citizens are changing as rapidly as the body of the mob around him.
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