Phil 7.23.20

Amid a tense meeting with protesters, Portland Mayor Ted Wheeler tear-gassed by federal agents

GPT-2 Agents

• Good back-and-forth with Antonio about venues
• It struck me that statistical tests about fair dice might give me a way of comparing the two populations. Pieces are roughly equivalent to dice sides. Looking at this post on the RPG Stackexchange. That led me to Pearson’s Chi-square test (which rang a bell as the sort of test I might need).
• Success! Here’s the code:
```from scipy.stats import chisquare, chi2_contingency
from scipy.stats.stats import pearsonr
import pandas as pd
import numpy as np

gpt = [51394,
25962,
19242,
23334,
15928,
19953]

twic = [49386,
31507,
28263,
31493,
22818,
23608]

z, p = chisquare(f_obs=gpt,f_exp=twic)
print("z = {}, p = {}".format(z, p))

ar = np.array([gpt, twic])
print("\n",ar)

df = pd.DataFrame(ar, columns=['pawns', 'rooks', 'bishops', 'knights', 'queen', 'king'], index=['gpt-2', 'twic'])
print("\n", df)

z,p,dof,expected=chi2_contingency(df, correction=False)
print("\nNo correction: z = {}, p = {}, DOF = {}, expected = {}".format(z, p, dof, expected))

z,p,dof,expected=chi2_contingency(df, correction=True)
print("\nCorrected: z = {}, p = {}, DOF = {}, expected = {}".format(z, p, dof, expected))

cor = pearsonr(gpt, twic)
print("\nCorrelation = {}".format(cor))

```
• Here’s the results:
```"C:\Program Files\Python\python.exe" C:/Development/Sandboxes/GPT-2_agents/gpt2agents/analytics/pearsons.py
z = 8696.966788178523, p = 0.0

[[51394 25962 19242 23334 15928 19953]
[49386 31507 28263 31493 22818 23608]]

pawns  rooks  bishops  knights  queen   king
gpt-2  51394  25962    19242    23334  15928  19953
twic   49386  31507    28263    31493  22818  23608

No correction: z = 2202.2014776980245, p = 0.0, DOF = 5, expected = [[45795.81128532 26114.70012657 21586.92215826 24914.13916789 17606.71268169 19794.71458027]
[54984.18871468 31354.29987343 25918.07784174 29912.86083211 21139.28731831 23766.28541973]]

Corrected: z = 2202.2014776980245, p = 0.0, DOF = 5, expected = [[45795.81128532 26114.70012657 21586.92215826 24914.13916789 17606.71268169 19794.71458027]
[54984.18871468 31354.29987343 25918.07784174 29912.86083211 21139.28731831 23766.28541973]]

Correlation = (0.9779452546334226, 0.0007242538456558558)

Process finished with exit code 0
```

• It might be time to start writing this up!

GOES

• Found vehicle orientation mnemonics: GNC_AD_STA_FUSED_QRS#

• 11:00 Meeting with Erik and Vadim about schedules. Erik will send an update. The meeting went well. Vadim’s going to exercise the model through a set of GOTO ANGLE 90 / GOTO ANGLE 0 for each of the rwheels, and we’ll see how they map to the primary axis of the GOES

Phil 8.21.19

• This week, a conversation about privacy, ethics, and organizing in the world of technology.Who benefits from the lack of diversity in the tech industry? Does artificial intelligence reflect the biases of those who create it? How can we push for regulation and transparency?  These are some of the questions discussed by our guests, Meredith Whittaker, co-founder of AI Now at NYU and the founder of Google’s Open Research Institute; and Kade Crockford, Director of the ACLU Massachusetts’ Technology and Liberty Program. They appeared at the Sydney Goldstein Theater in San Francisco on June 7, 2019.

7:00 – 8:00 ASRC GOES

• Printed out some business cards for JuryRoom
• Antonio has submitted the manuscript – created a TAAS account and verified that its there
• Dissertation
• Finished 0.5 pass at chapter 1!
• Goddard today
• See if I can get a permanent card? Done!
• More control system work
• Meeting with Wayne
• Send the as-delivered TAAS paper and cover letter. Done
• Work on getting the ML/Weapons paper reformatted tomorrow
• Send chapter one of the dissertation
• I’ll then start sending the chapters as I “complete” them, and we’ll see how it’s going. If the dissertation seems to be coming together well, then we might switch strategies to a from a content-centric to a coherence-centric approach.

Phil 7/1/19

7:00 –  6:00 ASRC