2021 continues to produce surprises: Several senior Republicans join impeachment push

I was looking at a McSweeny’s article (LEST WE FORGET THE HORRORS: A CATALOG OF TRUMP’S WORST CRUELTIES, COLLUSIONS, CORRUPTIONS, AND CRIMES). It references a pile of Trump Tweets that now look like this:

History has been deplatformed. Now what?

Good article on QAnon: QAnon reshaped Trump’s party and radicalized believers. The Capitol siege may just be the start.

Speaking of Twitter, this is a good thread on how to write and contest ML conference papers

ParametricUMAP allows users to train a neural network to optimize the embedding, resulting in a direct neural net based mapping from source data to embedding. This allows for extremely fast inference (embedding of new data points), orders of magnitude faster than standard UMAP. It also provides facilities for an inverse transform, mapping from the embedding space to the original data space that is both far faster and more robust that that provided by standard UMAP. Since network architectures can be user provided this also allows for CNN and RNN based UMAP embeddings for images or sequences.

Book

• Continue Making better Human-Computer Interfaces for Populations

GOES

• Add in mapping to script reader, verify by adding legends

MDS

• Status meeting maybe produce a spreadsheet to walk through that shows a time series of inputs and a calculation for each set? I think the inputs can be a column of six (for now?) variables as a set of rows, and the prediction calculations are shown below that. Make a DataFrame and see what that looks like.

From Charlottesville to the Capitol: how rightwing impunity fueled the pro-Trump mob

• The playbook for the Maga invasion of the nation’s Capitol building on Wednesday has been developing for years in plain sight, at far-right rallies in cities like Charlottesville, Berkeley and Portland, and then, in the past year, at state capitols across the country, where heavily armed white protesters have forced their way into legislative chambers to accuse politicians of tyranny and treason.

Here’s what seems to have happened with the Parler hack. The data may be available for research

• The person who did this: @donk_enby
• Gizmodo: Every Deleted Parler Post, Many With Users’ Location Data, Has Been Archived
• Cybernews: 70TB of Parler users’ messages, videos, and posts leaked by security researchers
• Repo on archive.org
• Another repo of videos (also plotted on a map based on geocodes) here: www.tommycarstensen.com/terrorism

Nice paper on training a model to generate synthetic data for better classification training: Reducing AI bias with Synthetic data. It uses the gretel’s gretel-synthetics library It’s free to use during the beta period, not sure about after, or what the pricing will be. They are hiring, with about seven openings at the moment, so they are burning through someone’s money.

GPT Agents

• Finish abstract submission – done
• Make an Overleaf project for qualitative paper?

GOES

• Finish up the ManeuverReader – done! Here’s the original, with some large number of points that is subsampled to 100 points and stored as a json file

• Here’s a reconstructed version that uses 1/3 (33) steps through the file. You can see a little roughness, but with more points it’s indistinguishable from the original pulled off influxDB:

• And here’s a snippet of the json file

{
    "title": "test",
    "speed_multiple": 1.0,
    "read_fmt": "%H:%M:%S",
    "mapping": {
        "GNC_AC_MOM_GEN_MOMBODY_X": "pitch",
        "GNC_AC_MOM_GEN_MOMBODY_Y": "roll",
        "GNC_AC_MOM_GEN_MOMBODY_Z": "yaw"
    },
    "duration": "00:29:56",
    "samples": [
        {
            "timestamp": "00:00:00",
            "values": [
                {
                    "name": "GNC_AC_MOM_GEN_MOMBODY_X",
                    "value": 63.68628693
                },
                {
                    "name": "GNC_AC_MOM_GEN_MOMBODY_Y",
                    "value": 1.657353401
                },
                {
                    "name": "GNC_AC_MOM_GEN_MOMBODY_Z",
                    "value": -3.304497004
                }
            ]
        },

• start to integrate into TopController

Book – Not much, just jotting down notes

GPT Agents

• Working on submitting

GOES

• Working on script generator

MDS

• Trying to find the right charge number
• Made slide deck for todays meeting and presented overview and next steps

This is one hell of a thread:

GPT Agents

• Moved the synthetic agents paper to the ijcai format and shared the Overleaf version with Antonio

Also:

GOES

• Work on script generator and reader

Book

• Working on Hierarchies, Networks, and Technology. New technologies may have the same arc as writing and printing, which is initial hierarchy that produces influence networks that counter (to a degree), the more aggressive aspects of a dominance hierarchy
• Meeting with Michelle

MDS

• Discussion with Aaron about phase2
• Wrote up thoughts and sent to Clay

And just so we remember that the pandemic is not going well here. For comparison, the battle that took the most American lives was Antietam, where there were 3,675 fatalities if you count both sides.

Need to look into replacing JetBrains

GOES

• Slide deck for 2:00 meeting
• 11:00 AI-ML meeting
• 2:00 Sim discussion. We have until the end of March to come up with a compelling demo
• More script generator. I need to write a method that searches through a Measurement list looking for the last value before a datetime
  • Need to map the names in the database to the desired name for the sim

MDS

• Write up notes from meeting and distribute
• Write up a couple of paragraphs for Clay

GPT Agents

• More coding
• ML group meeting

Book

• More Hierarchies, Networks, and Technology

Georgia is looking promising! Maryland is trying to be more flexible in its vaccinations!

And then later in the afternoon, this happened:

MDS

• 10:00 meeting with Aaron and Peter
  • Create pipeline for data (what does it look like?) and FOM evaluation (input/output)
  • Who creates the sim and generates the data?
  • Who feeds that into the FOM?
  • Can all this run locally?
  • Write up notes
• Meeting with Clay
  • Write up two paragraphs on phase 2 thoughts

GOES

• Slide deck for tomorrow’s meeting
• More work on script generator

GPT Agents

• Created a local version of the IJCAI paper project. Need to fit the format and then create the Overleaf project and share with Antonio
• Do some coding, dammit

JuryRoom

• 5:00 Huri Whakatau meeting

Book

• Continue Hierarchies, Networks, and Technology
• Found Charting the “Rise of the West”: Manuscripts and Printed Books in Europe, A Long-Term Perspective from the Sixth through Eighteenth Centuries, which is very cool.

Voting in Georgia today. I am pessimistic but hopeful about the outcome

GPT Agents

• I’m not sure if the meeting is today at 3:30 or Friday at 4:00?
  • It was today. Continuing on trying to figure out the best way to understand the behavior of the model. One of the interesting findings for today was that if the data isn’t in the dataset, then the model will start generating tokes at the meta wrapper.
• More coding

Book

• Working on what’s become Hierarchies, Networks, and Technology, and I think I’m now happy with where it’s going. It makes sense to use as the end of the chapter as well
• Made a cool figure:

GOES

• The Lambda box was cancelled. Sigh
• 11:00 Meeting with Vadim
• I’m going to start on a script-reading capability for TopController. I think a JSON or XML file that contains the following elements:
  • Absolute or relative move
  • axis name
  • Target (HPR or XYZ)
  • Timestamp
  • Required accuracy
• So a move could be a series of HPR coordinates that ‘play’. The first step is a MOVE command which includes the filename. The TopController opens the file (or fails and reports it), loads the move into memory and begins to step through it based on the timestamp. On reaching the end of the file and when the AngleController reports success/failure, the TopController reports DONE and is ready for the next MOVE
• Downloading the yaw flip maneuver from influx:

Have to get my fingers used to typing a new date

Book:

• Working on the section about displaying. I found Mike, the chimp that used the Kerosene cans. There’s apparently a paper as well, so I put in a request
• Loading data about democracies from here (ourworldindata.org/democracy) into my db for better queries and charts. I want to look at recent changes in authoritarian systems as social technologies have changed in the last couple of decades

GOES

• 11:00 Meeting with Vadim
• More sparring with Biruh?

MDA

• Need some kind of kickoff with the technical folks?

GPT Agents

• Continue coding
• Antonio found another venue, but it means pulling the paper down to SIX pages, Need to look into that (IJCAI-21 Call for Papers – IJCAI 2021 (ijcai-21.org))

Last work day of the year.

Still looking at COVID deaths. Here’s what’s going on in a sample of countries as of today

And here are the worst performing states over the duration of the epidemic. Georgia continues to be a mess. Those states at the bottom are coming up fast…

Book

• Working on importing and transcribing the debate. Since the original won’t upload, I pulled the video into Adobe Premiere and cut off the head and tail, then exported as an AVI. We’ll see how that works. Nope – it’s ENOURMOUS! Trying other formats and getting progressively more annoyed. Aaaaand never got it to work. At least not today.
• I did start editing the whole video down to just the displays

GPT Agents

• Need to start coding, Going to talk to Stacey about that before I start.
• Got some good advice and started.
• As I’m coding, it looks like I’m making a nice set of tags for a training set. I wonder how small a set could be used to train something like BERT. Here’s an article:
  • How to Fine Tune BERT for Text Classification using Transformers in Python (last updated Dec 2020)
    • In this tutorial, we will take you through an example of fine tuning BERT (as well as other transformer models) for text classification using Huggingface Transformers library on the dataset of your choice.
• interpreting GPT: the logit lens
  • Other work on interpreting transformer internals has focused mostly on what the attention is looking at. The logit lens focuses on what GPT “believes” after each step of processing, rather than how it updates that belief inside the step.

GOES

• Sent a note to Biruh asking how the servers will handle interactive video. He said that I could keep the server at home. So he just hates workstations? Anyway, lots of back and forth. Not sure where it’s going.

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

• Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime. Why can we use relatively vanilla gradient descent algorithms (e.g., without strong regularization) to tune a model with hundreds of millions of parameters on datasets with only hundreds or thousands of labeled examples? In this paper, we argue that analyzing fine-tuning through the lens of intrinsic dimension provides us with empirical and theoretical intuitions to explain this remarkable phenomenon. We empirically show that common pre-trained models have a very low intrinsic dimension; in other words, there exists a low dimension reparameterization that is as effective for fine-tuning as the full parameter space. For example, by optimizing only 200 trainable parameters randomly projected back into the full space, we can tune a RoBERTa model to achieve 90\% of the full parameter performance levels on MRPC. Furthermore, we empirically show that pre-training implicitly minimizes intrinsic dimension and, perhaps surprisingly, larger models tend to have lower intrinsic dimension after a fixed number of pre-training updates, at least in part explaining their extreme effectiveness. Lastly, we connect intrinsic dimensionality with low dimensional task representations and compression based generalization bounds to provide intrinsic-dimension-based generalization bounds that are independent of the full parameter count.

GPT Agents

• Working on getting the data out of the database in a useful way, so I learned how to create a view that combines multiple rows:

create or replace view combined as
    select distinct t_1.root_id, t_1.experiment_id, t_1.probe as 'probe', DATE_FORMAT(t_1.content, "%M, %Y") as 'date', t_2.content as 'text'
        from table_output as t_1
        inner join table_output as t_2
        on t_1.root_id = t_2.root_id and t_1.tag = 'date' and t_2.tag = 'trimmed';

• What’s nice about this is that I can now order results by date which gives a better way of looking through the data
• Imported the query output spreadsheet into NVivo and flailed with the importer a bit. I think I need to create a script that iterates over all the probes and creates a spreadsheet for each. It also needs to split off the probe from the content. Maybe remove the links as well? I’m conflicted about that because linking is an important thing. Maybe produce two files?

Book

• Working on coding the Biden-Trump debate in NVivo. Had to buy a transcription license. Can’t upload the video???

It’s the slowest week of the year

GPT Agents

• Working on getting extended “trimmed” data out of the model
• Had an extensive set of talks with Stacey about using the twitter dataset to support a qualitative study of the trained model of COVID data. The thing that finally clicked was my description of the model as analogous to someone who has read every one in the data set. Such a person could more-or-less repeat actual tweets in a way that would reflect the underlying frequency, but they could also synthesize knowledge. For example, we were using probes like “Dr. Fauci is “, which can also be found in the database. But the phrase “Dr. Fauci is like a ” does not appear anywhere. But the model has no problem with it. 2 of the responses in the first test of 15 results say Dr. Fauci is like a “president”, which makes a lot of sense, actually
• Working on getting the date info out. Everything works, but it doesn’t really make more text. The system has a sense of how long a tweet should be and how they end, it seems
• Getting up to speed on NVivo
  • Intro & Import: A general overview of what NVivo does, and how to import data.
  • Organize: A first look at how to code, note-keeping options, how to create cases (the units of analysis) and give them attributes (descriptive information).
  • Explore: Run a word frequency query and create a word cloud, then create a simple chart showing opinions on an issue.

Understanding journalist killings

• Why do state authorities murder journalists? We show that the majority of journalists are killed in democracies and present an argument that focuses on institutional differences between democratic states. In democracies, journalists will most likely be targeted by local state authorities that have limited options to generally restrict press freedom. Where local governments are elected, negative reporting could mean that local politicians lose power and influence, especially if they are involved in corrupt practices. Analyzing new global data on journalist killings that identify the perpetrator and visibility of the journalist, we show that local-level elections carry an inherent risk, particularly for less visible journalists. Killings perpetrated by criminal groups follow a similar pattern to those by state authorities, pointing to possible connections between these groups. Our study shows that without effective monitoring and accountability, national democratic institutions alone are unable to effectively protect journalists from any perpetrator.

A very nice set of examples on using mysqldump

GPT Agents

• Finished updating the db. Created update tables for Sim
• Adding date info to the current (experiment 7) dataset – done!

GOES

• Splitting out model construction code before tackling timing. Too much code in one file to work with

Next year, this date will be very symmetric. Is it going to be the last palandromic date for a while? like for about 1,000 years?

GPT-2 Agents:

• Need to go through the “full” data, parse the date and ad that row as content once the ingest is done
• Still adding phase 13 data
• Still need to fix the embedding code
• Nice chat with Mike D. about the whole concept. We’ll connect again after the holidays
• 3:30 meeting – nope

GOES

• Updating the ASRC box. The IDE and a bunch of plugins are all out of date – done!
• Verifying that the yaw flip works. Yep! That is a thing of beauty:

• Here’s the reaction wheel angular velocity. It’s a bit rough looking, which is curious, but I’m guessing that it has to do with clamping issues that I need to fix:

• Check stability on rotating cubes for quickstep and world.step
• Start cleaning up simulator physics and data. I think that a text file to describe the tasks (start angle, end angle, real world time, time multiple, output file) would be nice, since we’re going to have to generate a lot of data.
• Also, fix whatever is broken with the mouse controls
• Need to look into headless runs

Book

• Really extended the deprogramming section to highlight the importance of diversity