3:00 Physical and COVID booster

SBIRs

• Still trying to get bids from NVIDIA and Dell
• Pinged Orest about the ITM next steps

GPT-Agents

• keyword-explorer is now on PyPI! Need to put together documentation

Book

• Putting together some simulation code to show when egalitarianism/diversity makes sense

Tasks

• Roofwork
• BGE Home

SBIRs

• Sprint meeting
• Discuss triage with Orest?

GPT Agents

• Work some more on deploying Keyword Explorer – done! Here’s how you deploy using JetBains PyCharm
• First, make sure you have up-to-date versions of setuptools and twine. Also make sure your directory looks something like this:

• Create your setup.cfg file

[metadata]
description-file = README.md
name = keyword_explorer

• Then, your setup.py file. It is important to explicitly list the subdirectories in the packages array. Here’s my example (Note that “long_description) is a variable. It is what PyPI uses to create its description, and needs to be updated before creating the wheel. (see here for an example):

from  distutils.core import  setup

setup(
    name='keyword_explorer',
    version= "0.0.3.dev",
    packages=['keyword_explorer',
              'keyword_explorer.utils',
              'keyword_explorer.TwitterV2',
              'keyword_explorer.tkUtils',
              'keyword_explorer.OpenAI',
              'keyword_explorer.Apps'],
    url='https://github.com/pgfeldman/KeywordExplorer',
    license='MIT',
    author='Philip Feldman',
    author_email='phil@philfeldman.com',
    description='A tool for producing and exploring keywords',
    long_description= long_s,
    install_requires=[
        'pandas~=1.3.5',
        'matplotlib~=3.2.2',
        'numpy~=1.19.5',
        'sklearn~=0.0',
        'scikit-learn~=0.24.2',
        'requests~=2.27.1',
        'wikipedia~=1.4.0',
        'openai~=0.11.5',
        'networkx~=2.6.2',
        'tkinterweb~=3.12.2'],

    classifiers=[  # Optional
        # How mature is this project? Common values are
        #   3 - Alpha
        #   4 - Beta
        #   5 - Production/Stable
        'Development Status :: 3 - Alpha',
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
)

• Jetbrains has a description of how to set up the resource file (.pypirc), but it’s a bit out of date. Here’s what it should look like:

[distutils]
index-servers=
    testpypi
    pypi

[testpypi]
repository = https://test.pypi.org/legacy/
username = your_login
password = your_password

[pypi]
repository = https://upload.pypi.org/legacy/
username = your_login
password = your_password

• At this point, you can run setup from within the IDE:

• That will bring up a dialog. Type “sdist” as the task name

• That will bring up another dialog. Type bdist_wheel and click OK

• That will build the files and place them in the dist directory
• Then, using the terminal or console window, deploy using twine. Notice that -r option uses the .pypirc file to get the rest of the arguments:

D:\Development\External\KeywordExplorer> twine upload dist/* -r pypi

• NEW! As of June 1, 2023, PyPi requires 2FA and tokens to upload:
  • Set your username to __token__
  • Set your password to the (long!) token value, including the pypi- prefix (e.g. pypi-AgEIcHl…)
  • Where you edit or add these values will depend on your individual use case. For example, some users may need to edit their .pypirc file, while others may need to update their CI configuration file (e.g. .travis.yml if you are using Travis).
  • Advanced users may wish to inspect their token by decoding it with base64, and checking the output against the unique identifier displayed on PyPI.
• Ran into a problem on a subsequent install: “Unknown distribution option: ‘install_requires’” Here’s the fix:

• 3:30 Meeting. Went over the DARPA RFP, discovered that movie stars might be a good thing to search for. Added a max_chars limit to the parser

Book

• Finished up Fire, and started pulling all that back to egalitarianism

Book

• Wrote about a page and a half on Language and Weapons. Still need to do fire

SBIRs

• Need to re-read the DARPA proposal to see if there will be any notification about the abstract, and if non-citizens can work on it.
  • As near as I can tell, we are only guaranteed a notification of nonconformity
  • Non-citizens can work on the project, but they can’t(?) be in senior positions
• Start the GUI for the builder app
• 10:00 equipment meeting. Gotta get some quotes. Trying Dell and Nvidia
• 2:00 Weekly meeting with Lauren. If he can charge, then start to schedule a government monthly meeting

Book

• Moved interview with a biased machine to the front of the book since it could be a nice attention grabber
• Started on the Egalitarianism section
• Added some stuff to the technology section – Paleolithic and Early modern Human technologies. I’ll probably move the Paleolithic tech to the Egalitarianism section since it explains a lot about that behavior and the amount of evolutionary adaptation that took place around it. We do not look like the other great apes. This is why.

SBIRs

• Good chat with Steve. Suggested how to create and visualize multiple attribute maps to see how the loss function is working
• Worked out next steps with Rukan:
  • try predicting more than one point
  • try sine wave
  • loss function over 256 collecting loss, but maybe try 16 or some smaller number first
  • try to train for better convergence to see if it fixes the indexing issue
  • fix the indexing issue
  • try pytorch hyperparameter optimizer, probably just LR
• And I’m generating good RCS code!

    def init_task(self):
        CMD_ship_controller_to_navigate_controller = self.ddict.get_entry('CMD_ship_controller_to_navigate_controller').data
        RSP_navigate_controller_to_ship_controller = self.ddict.get_entry('RSP_navigate_controller_to_ship_controller').data
        CMD_ship_controller_to_missile_controller = self.ddict.get_entry('CMD_ship_controller_to_missile_controller').data
        RSP_missile_controller_to_ship_controller = self.ddict.get_entry('RSP_missile_controller_to_ship_controller').data
        if self.cur_state == States.NEW_COMMAND:
            print("ship_controller:INIT NEW_COMMAND ")
            self.cur_state = States.S0
            self.rsp.set(Responses.EXECUTING, self.cmd.serial)
            CMD_ship_controller_to_navigate_controller.set(Commands.INIT, CMD_ship_controller_to_navigate_controller.next_serial()+1)
        elif self.cur_state == States.S0 and RSP_navigate_controller_to_ship_controller.test(Responses.DONE):
            self.cur_state = States.S1
            CMD_ship_controller_to_missile_controller.set(Commands.INIT, CMD_ship_controller_to_missile_controller.next_serial()+1)
        elif self.cur_state == States.S1 and RSP_missile_controller_to_ship_controller.test(Responses.DONE):
            self.cur_state = States.S2
        elif self.cur_state == States.S2:
            print("ship_controller:DONE")
            self.cur_state = States.S3
            self.rsp.set(Responses.DONE)

Tasks

• Ellis schedule?
• Bloodwork – done

The future is coming:

Is It Good to Cooperate? Testing the Theory of Morality-as-Cooperation in 60 Societies

• What is morality? And to what extent does it vary around the world? The theory of “morality-as-cooperation” argues that morality consists of a collection of biological and cultural solutions to the problems of cooperation recurrent in human social life. Morality-as-cooperation draws on the theory of non-zero-sum games to identify distinct problems of cooperation and their solutions, and it predicts that specific forms of cooperative behavior—including helping kin, helping your group, reciprocating, being brave, deferring to superiors, dividing disputed resources, and respecting prior possession—will be considered morally good wherever they arise, in all cultures. To test these predictions, we investigate the moral valence of these seven cooperative behaviors in the ethnographic records of 60 societies. We find that the moral valence of these behaviors is uniformly positive, and the majority of these cooperative morals are observed in the majority of cultures, with equal frequency across all regions of the world. We conclude that these seven cooperative behaviors are plausible candidates for universal moral rules, and that morality-as-cooperation could provide the unified theory of morality that anthropology has hitherto lacked.

Book

• Start on the reverse hierarchy section

SBIRs

• Sprint review
• More code generator – first pass is DONE!

Book

• Working on comparing political views of adults and tweens – done?
• Updated the scans of HitF

SBIRs

• Chat with Rukan. Basically bug hunting. It turned out that he hadn’t zeroed his gradients
• Set up Friday meeting with Steve
• Working on code generator
  • bdmon is generated (I think????)
  • Working on module code

I think that this will turn out to be very important

Book

• Canada National Centre for Truth and Reconciliation

SBIRs

• Had a good chat with Don last night about potential partnering for the DARPA proposal
• Finished(?) the DARPA abstract
• Sprint planning

GPT Agents

• Need to get setup.py working. Try this? the-hitchhikers-guide-to-packaging.readthedocs.io/en/latest/quickstart
• 3:30 Meeting

Tasks

• Call security to reset alarm – done using this video!
• Call JD to schedule appt – done. May 9

SBIRs

• Sprint demos
• Work on DARPA proposal

GPT Agents

• Need to get setup.py working. Try this? the-hitchhikers-guide-to-packaging.readthedocs.io/en/latest/quickstart

Can Instagram ads fight disinformation about climate and COVID vaccines?

• 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

Firebird tonight!

Book

• add examples of age dominance likeComic book moral panic of the 1950’s – done
• Videogame moral panic of the 1980s(?) – done
• School boards
• Child removal
• The Salem Witch Trials – done
• McMartin Preschool – done
• Pizzagate – done
• QAnon – done
• Etc

SBIRs

• 9:15 standup
• Work on abstract
• More tool
  • Generating all the controllers and cmd/rsp objects
  • Stepping controllers in the loop and advancing the counter
  • Still need to:
    • State tables for BDMON
    • Fill in the task stubs for controller code
• Get Rukan started on autoregressive model – done
• Guided Ron a bit on MinGPT
• Pinged Vika about our box. She’s looking into it

Book

• Finished the first pass at age bias. I still want to add examples of age dominance like
  • Comic book moral panic of the 1950’s
  • Videogame moral panic of the 1980s(?)
  • School boards
  • Child removal
  • McMartin Preschool
  • Pizzagate
  • QAnon
  • Etc

SBIRs

• 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
• Started work on DARPA medical abstract

Unveiling the higher-order organization of multivariate time series

• 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

    def __init__(self, d:Dict):
        self.quantity = 1 #default
        self.__dict__.update(d)

    def to_string(self) -> str:
        return "name = {}\n\tquantity = {}\n\tparent = {}\n\tcommands = {}".format(self.name, self.quantity, self.parent, self.commands)

• Pretty pleased with how this is going:

Book

• New Orleans school desegregation crisis
• Ruby Bridges (Through My Eyes)

Book

• Worked on the age bias section

SBIRs

• 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

TriMap is 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:

1. reflect the relative placement of the clusters in high-dimension,
2. reveal possible outliers and anomalies in the data,
3. generate embeddings that are more robust to certain transformations (see here for more details).