Author Archives: pgfeldman

Phil 9.4.20

Back from a short, soggy vacation

Glasses!

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

  • This research examines the content, timing, and spread of COVID-19 misinformation and subsequent debunking efforts for two COVID-19 myths. COVID-19 misinformation tweets included more non-specific authority references (e.g., “Taiwanese experts”, “a doctor friend”), while debunking tweets included more specific and verifiable authority references (e.g., the CDC, the World Health Organization, Snopes). Findings illustrate a delayed debunking response to COVID-19 misinformation, as it took seven days for debunking tweets to match the quantity of misinformation tweets. The use of non-specific authority references in tweets was associated with decreased tweet engagement, suggesting the importance of citing specific sources when refuting health misinformation.

Book

  • Updated MikTex, which broke my epstopdf. Uninstalled and reinstalled everything. We’ll see how that works
    • basic-miktex-20.6.29-x64
    • texstudio-3.0.1-win-qt5
    • Hooray! Success!
  • Finished the first pass of “The spacecraft of Babel”. Not sure how much sense it makes?
  • Went over interviewing stuff with Michelle

GOES

  • Pinged Vadim – nothing
  • Security training

Phil 8.27.20

Ai Weiwei’s CORONATION is the first feature-length documentary about the coronavirus in Wuhan. As the first city hit in the global pandemic, the Chinese metropolis with a population of 11 million was placed under an unprecedented lockdown.

Working on potential areas of contribution. Added links and extended the text. Need to wait for the last paper to become active on ArXiv

IRAD

  • 11:30 Slides meeting

GOES

  • 10:00 Meeting with Vadim
  • 2:00 Status Meeting

GPT-2 Agents

https://twitter.com/joeddav/status/1266429443238699009

JuryRoom

  • 5:30 Meeting

Phil 8.26.20

Working on my CV so I can be considered for an adjunct position – done(?)

GOES

  • 10:00 Meeting with Vadim
  • Need to add an exit if a duplicate entry is made in DataDictionary
  • Yaw flip is working again!
  • 2:00 status

#COVID

  • Look at translations!
  • They look good! Going to translate the annotated tweets and use them as a base for finding similar ones in the DB
  • The only issue is that a few words are being consistently mistranslated or rendered phonetically. Need to put together a data dictionary for some post processing

JuryRoom

  • Good discussion with Tony about knowledge graphs and the analytics in general

Phil 8.25.20

GOES

  • All hands meeting at noon
  • Tag-team coding with Vadim – good progress. More tomorrow
  • Make slides for T in the GPT-2 Agents IRAD – done?

GPT-2 Agents

  • Finished updates to paper. The results are kind of cool. Here’s the error rate by model

errors

  • It’s pretty clear from looking at the charts is the ability of the model to learn legal moves is proportional to the size of the corpora. These are complicated rules, particularly for knights, bishops, and the queen. There needs to be a lot of examples in the text.
  • On the other hand, let’s look at overall percentage of moves by piece when compared to human patterns:

moves1

  • The overall patterns are substantially the same.  Here’s the 2-tailed correlation:

2-tail

  • There really is no substantial difference. To me that means that the low-frequency(?) information makes it into the model with this kind of thematic information with less text. Kinda cool!
  • JSON parser
    • Got the code working to grab the right files and read them:
      for filename in glob.glob('*.jsonl', recursive=True):
      trimmed = re.findall(r"\\\d+_to_\d+", filename)
      print("{}/{}".format(filename, trimmed))
      with open(filename) as f:
      jobj = json.load(f)
      print(json.dumps(jobj, indent=4, sort_keys=True))
    • Working on the schemas now
      • It looks like it may be possible to generate a full table representation using two packages. The first is genson, which you use to generate the schema. Then that schema is used by jsonschema2db, which should produce the tables (This only works for postgres, so I’ll have to install that). The last step is to insert the data, which is also handled by jsonschema2db
      • Schema generation worked like a charm
      • Postgres and drivers are installed. That’s enough good luck for the day

ML seminar

from transformers import GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained('gpt2')  # or any other checkpoint
word_embeddings = model.transformer.wte.weight  # Word Token Embeddings 
position_embeddings = model.transformer.wpe.weight  # Word Position Embeddings 

Book?

Phil 8.24.20

Getting new gutters today!

Clearing the Confusion: Scaling in PCA

  • Many resources on machine learning (ML) methodology recommend, or even state as crucial, that one scale (or standardize) one’s data, i.e. divide each variable by its standard deviation (after subtracting the mean), before applying Principal Component Analysis (PCA). Here we will show why that can be problematic, and provide alternatives.
https://twitter.com/scott_bot/status/1297862013373349888

GPT-2 Agents

  • Running analytics on legal moves and move frequency
    • gpt_view_400 – done
    • gpt_view_200- done
    • gpt_view_100- done
    • gpt_view_50- done
  • Need to start on Twitter parser

GOES

  • 2:00 Meeting with T- done
  • 2:30 Sim status- done

Keras Code examples

Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows.

All of our examples are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs in the cloud. Google Colab includes GPU and TPU runtimes.

Phil 8.21.20

Checking out Flourish for data visualization

GPT-2 Agents

  • Hitting malformed descriptions in the 200k model that caused the program to crash twice. It was at 146k moves, which is probably enough to get statistics on illegal moves. Fixed the error with a try/except and moved onto the next model. I’ll go back and rerun if the fix works. Now over 100k moves on the 100k model with no problems. Yet.
  • Pinged Dave Saranchak about the The Emerging Techniques Forum (ETF) as a venue

GOES

  • 1:00 Meeting with Vadim. Yaw flip today? YES!!!
  • Trying to work through what a malfunctioning RW would look like to the vehicle control. RW2 is critical for Roll, and RW5 is critical for Pitch and Roll. How is this system redundant?
  • I think I need to make some plots to understand this
https://viztales.files.wordpress.com/2020/08/secondary_correlations.png
  • So I think this is starting to make sense. There is always a point where any two rwheels are equal. You can see this on the RW1-RW4 graph. On the other, it’s just further to the left. This means that you can make them cancel and then manipulate the vehicle on the desired axis. This should be a matter of setting a scalar value such that the desired axis is nonzero. I’ll play around with that after lunch.

Book

Phil 8.20.20

COVID

  • I’m a little angry today. I was engaging with a Trump supporter and they wanted to know how things would have been better under Biden. So, based on how the Obama administration handled SARS and Ebola (very well), I thought I’d try mapping a democracy that is handling the pandemic very well, which is South Korea. Both the USA and S. Korea had their first case on the same day, so it is very easy to line up the data, and then scale the S. Korean results to match the population of the USA (About 6.4). The results are staggering:
https://viztales.files.wordpress.com/2020/08/usa_korea.png
  • I need to write up a longer post on this and annotate the charts, but the conclusion is stark: the mismanagement of the pandemic by the administration has led to the deaths of hundreds of thousands of people who might otherwise be alive today

The Emerging Techniques Forum (ETF) is driven to improve analysis and understanding throughout the defense community by seeking out novel and leading-edge approaches, methods, and techniques – wherever they are conceived. By sharing and incorporating the latest (and in-progress) developments across government, academia, private industry, and enthusiasts, the ETF aims to support and maintain relevant, timely, and early comprehension of lessons learned that may grow to have an outsized impact on the community at large.

  • December 7 – 10 2020

Book

  • Write some content for the dimension reduction and emergence chapters – made good progress

GPT-2

  • Created copies of the table_moves table for the 400, 200, 100, and 50 models. It’s easy!
CREATE TABLE foo LIKE bar;
  • Generating text for table_moves_400 – done
  • Generating text for table_moves_200

GOES

  • 10:00 meeting with Vadim
    • Progress! Trying for a Yaw flip tomorrow
  • 2:00 status meeting

JuryRoom

  • 5:30 Meeting

Leaving today with this

Phil 8.19.20

GPT-2 Agents

  • Based on feedback from yesterday, I’m going to train a model on 400,000, 200,000, 100,000, and 50,000
  • Training the 400k model – done
  • Training the 200k model – done
  • Training the 100k model – done
  • Training the 50k model – done

#COVID

  • Downloaded data from Dropbox
    • Need to write a parser
  • Killed the translator for now. At 292,719 translated tweets
    • Backing up the db and pushing. Nope, it won’t accept the db, and now I’m stuck
    • Looks like I have to add a large file extension? https://git-lfs.github.com/
    • Created a 100 line source/translation csv file and distributed
    • 3:00 Meeting – assigned translations

GOES

  • 10:00 Meeting with Vadim
    • We went over the axis results and I realized that the pairs of reaction wheels are spinning in opposite directions, which allows for easy rotation around the vehicle primary axis. Need to figure out what the exact configurations are, but here’s the math for a hypothetical set space at 120 degrees around the z axis, and at 45 degrees with respect to an x-axis rotated the same amount
  • 2:00 Status meeting. Nothing new. Still working on slow connectivity to the devlab

Phil 8.18.20

GPT-2

  • Downloading COVID files for storage and training
  • Writing parser and DB store

#COVID

  • Stop the translator and find the 60 quotes
    • Everyone gets 15 quotes to translate
    • Everyone reviews all other translations and grades them on a 1-10 scale
    • Compare human and machine results

GOES

  • 2:00 meeting with Vadim. Hopefully we’ll have a table of values
  • Working on the projections on the axis:
  • Still need to adjust so all angles are less than 90 by negating one of the axis (or the vector. It would have to be extended though)
  • The percentage of the contribution per axis is going to be something like
    • 1.0 – axis angle / (normalized sum of angles)
  • So if the angles are 50, 52, 62, the contributions would be
  • Nope, realized that I want to take the cosine with respect to the axis of interest. So it’s this:

Food shopping!

4:00 ML Seminar

  • Present slides, get feedback on paper and presentation – done!
  • Train models with different sizes of data and see how the models behave with respect to the legal moves and spectral qualities

Phil 8.17.20

A college kid’s fake, AI-generated blog fooled tens of thousands. This is how he made it.

  • The trick to generating content without the need for much editing was understanding GPT-3’s strengths and weaknesses. “It’s quite good at making pretty language, and it’s not very good at being logical and rational,” says Porr. So he picked a popular blog category that doesn’t require rigorous logic: productivity and self-help.

GPT-2 Agents

  • Slides – Done!?
  • Start writing code to store JSON in db

#COVID

  • Translation is chunking along a but faster now that I’m generating 20 translations per batch

GOES

  • 2:00 Meeting with Vadim
    • The rotations all look good
    • Explained the 3 vector reference frame rotation approach
    • V will characterize each RW, and then we’ll try to spin the vehicle in the XY plane

Phil 8.14.20

Looks like we lost the classic editor on WordPress. Sigh.

GPT-2 Agents

  • Updated ArXiv paper. Need to start thinking about slides for Tuesday. Started!

Book

  • Finished moving text to Overleaf project
  • 2:00 Meeting with Michelle – went through the whole structure of the book, added some chapters and moved other parts around. I’m going to start roughing in the new parts

Phil 8.13.20

Ride through the park today and ask about pavilion rental – done

Māori Pronunciation

Iñupiaq (Inupiatun)

GPT-2 Agents

  • Rewrite intro, including the finding that these texts seem to be matched in some way. Done
  • Uploaded the new version to ArXiv. Should be live by tomorrow
  • Read Language Models as Knowledge Bases, and added to the lit review.
  • Discovered Antoine Bosselut, who was lead author on the following papers.Need to add them to the future work section
    • Dynamic Knowledge Graph Construction for Zero-shot Commonsense Question Answering
      • Understanding narratives requires dynamically reasoning about the implicit causes, effects, and states of the situations described in text, which in turn requires understanding rich background knowledge about how the social and physical world works. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it.
        In this paper, we present initial studies toward zero-shot commonsense QA by formulating the task as probabilistic inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually relevant knowledge on demand using generative neural commonsense knowledge models.
        Empirical results on the SocialIQa and StoryCommonsense datasets in a zero-shot setting demonstrate that using commonsense knowledge models to dynamically construct and reason over knowledge graphs achieves performance boosts over pre-trained language models and using knowledge models to directly evaluate answers.
    • COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
      • We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge. We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and propose COMmonsEnse Transformers (COMET) that learn to generate rich and diverse commonsense descriptions in natural language. Despite the challenges of commonsense modeling, our investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs. Empirical results demonstrate that COMET is able to generate novel knowledge that humans rate as high quality, with up to 77.5% (ATOMIC) and 91.7% (ConceptNet) precision at top 1, which approaches human performance for these resources. Our findings suggest that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.

GOES

  • 10:00 sim status meeting – planning to fully evaluate off-axis rotation by Monday, then characterize Rwheel contribution, adjust the control system and start commanding vehicle rotations by the end of the week? Seems ambitions, but what the hell.
  • 2:00 status meeting
  • Anything about GVSETS? Yup: Meeting Wed 9/16/2020 9:00 AM – 10:00 AM

JuryRoom

  • 5:30 meeting. Discuss proposal and additional meetings

Book

  • Transfer more content

Phil 8.12.20

4:00 meeting with Marissa

#COVID

  • Nearly 150k tweets translated
  • Meeting today at 3:00
    • For next week, create a spreadsheet with 150 random tweets for translation tests (Pearson’s correlation?)

GPT2-Agents

  • Creating code that dumps probe results to files for better word clouds – done.
  • Boy, that produced some unexpected results. I changed the probe to “The man/woman walked into the room.”, and ran 100 probes for each, initializing with the same seed on the 117 million parameter GPT-2, which seems plenty large for these types of experiments.
  • The big surprise if how similar the start of the text is for each, then how it diverges. Here’s the first result for each probe (all text examples are ordered man, then woman):
    • This was about the time that an old friend of mine made his move and was going to be a huge help for me. I thought to myself and began crying. The thought of an old friend crying seemed almost too much, but I didn’t want to hurt him as much. My mind was racing, but all I could think about was how much the boy was hurting right now. He still wasn’t strong,
    • This was about the time that an accident really occurred. She had just turned up a couple hours earlier at the home they owned and hadn’t seen her since. She had a bad haircut and she couldn’t sit still and look after herself. When the accident happened, she didn’t even have her hair cut. She was so exhausted, she started crying. She was so exhausted and crying. After
  • And here’s the last:
    • He spoke, “I can’t believe there’s this situation here. This is very, very weird.” I told him I felt scared, and he replied, “You’re probably right about that, but I don’t know what it’s about.” He didn’t respond, and I was left thinking, “I’m sure I’m not alone in this crazy situation.” The next day, I went back to meet him. “He’s
    • She spoke, “I can’t believe there’s this situation here. This is very, very painful.” I told her I felt scared and I was upset. She said “I’ll tell you what I saw when I saw him.” I told her “He’s so skinny but the black hair looks gorgeous, he looks beautiful. “She started crying and began giving me some food and asking if I were alright. “
  • Here’s another good pairing:
    • It wasn’t that it was a bad situation. He just wasn’t feeling it. He felt that he wasn’t going to get laid, and if anything, he didn’t think it would help him get off. “We’ll go, then,” the woman said. There was still an argument at the back, but now it wasn’t too much worse. The woman had been arguing with the man, but the man was not
    • It wasn’t that it was a bad situation. That just wasn’t the case. She was just a little shy and reserved and didn’t really need anything to do with it. I had been on the phone with Amy for the last week. When I found out that she wanted to join me in our recent trip I was pretty bummed out. That’s when Amy started to feel bad about herself. For
  • The pairs don’t always begin with the same words. This pair still seems to have a legal/criminal context:
    • He handed out a number of hand-written handbags, which he claimed could be used to carry out a search that began when he first moved into the room. The door was then locked and the man went into the bathroom, which he said was the safest place. It was the first time the intruder had ever been caught, and the man said it was the second time they’d been spotted outside the residence. The man was
    • The two officers were holding their guns. As the woman made her way to her seat, she saw two men on a motorcycle walking towards her. She asked the man why he was not in the car with her. The man explained that he was afraid of the two men driving. The officers explained that she had to have sex and to stay with the men. The woman was terrified of the officers as the men drove away with their cameras and other equipment
  • It’s like the model start in similar places, but pointing in a slightly different direction. It seems to be important to run probes in identical sequences to have more insight into the way the model is perceiving the probes.

GOES

  • 1:30 Meeting with Vadim. He’s don an on-axis mass test and will do an off-axis test next. I showed him the quaternion frame tracker
  • 2:00 Status meeting

Book

  • Start moving chapters! Making progress!