My contribution to the mass shooting discussion. Let’s try placing taxes on 2nd amendment products (guns, ammo, etc.) based on the number killed and wounded in the last, say, 100 days. For each person (or child) killed, add 10% each, and for each person (or child) wounded add 5% maybe? Seems reasonable, no? If no one is killed or injured in a mass shooting in the last 100 days, no taxes! Just leave it up to the manufacturers and gun owners to decide what they need to do to keep their taxes down. After all, they keep telling us they understand the problem better than anyone.

Maybe we use the proceeds for funding free mental healthcare for all? After all, that’s the current excuse for gun violence.

What would that look like? Well, using the handy list of mass shootings in the USA in 2022, we can work this out:

• 221 people were killed and 824 wounded in 191 mass shootings in the last 100 days.
• That means the tax right now for guns and ammo should be 6,330%.
• A typical .223 bullet, like the ones used in the AR-15, normally costs about $1.75. With the murder tax, those bullets would cost $110.78 each.

I think the problem would be fixed. Probably within 100 days. No other laws required.

Book

• Finished Interview with a biased Machine and started on The Spacecraft of Babel
• Made a cool cover

SBIRs

• Finish writing up RCSNN progress – done!

GPT-Agents

• 1:00 Librarian meeting about keyword search

Exposing the fetish of right-wing politics and how liberals can fight back

• Liberals should pay less attention to what right-wingers say and more attention to what they mean. Liberals should presume the principle there is the one that isn’t there. And they should spell it out for normal people in order to ask if this is the kind of country they want to live in.
• This article is right in line with my thinking on dominance displays

SBIRs

• Standup
• Meeting with Rukan. Went over the design of the config mgr and set up for adding autoencoding sections to the quarterly report
• Refactored the quarterly report to match previous submissions
• Sent email to Dr. J to see if he’d like a presentation as well
• JSC meeting with Aaron this afternoon

Book

• Finished section 1! Need to send it out to some folks

Need to write some code that lets me play with a bullet tax based on this insanity

GPT Agents

• Nice meeting with Jimmy and Shimei. Need to contact a librarian to get insights for overall keyword search, and get a first pass of the protocol done to try next week
• Got one of those text-to-image accounts:

Book

• Working on Hierarchies, Networks, and Technology. Last section of Part I!

SBIRs

• Send email to Dr. J – oops! Tomorrow
• Continue on JSC proposal – about 2 hours
• Read the CDRLs and start the quarterly report – done

SBIRs

• Sprint planning
• Contact D. Asbury
• Start planning out JSC paper
• Start coordinating report

GPT agents

• 3:30 Meeting

Book

• Finished the influence + dominance chapter

Waikato

• Start reading Jarod’s paper

Just finished up a nice bike vacation:

And I got a chapter cleaned up in the book! Need to photoshop a few pix

Simple Annotated implementation of GPT-NeoX in PyTorch

• This is a simpler implementation of GPT-NeoX in PyTorch. We have taken out several optimizations in GPT-NeoX for simplicity.

SBIRs

• Catch up with Rukan
• Sprint review
• Try to remember what I did and what to do next. Maybe work with Aaron on Embeddings?

Book

• Work on

Waikato

• Read and comment on Jarod’s thesis

Radical American Partisanship: Mapping Violent Hostility, Its Causes, and the Consequences for Democracy

• Mapping? Gotta look more at this

Book

• Working through Human Belief Spaces. It is soooooo first draft!

New UL2 model

• UL2 20B beats GPT-3 175B on zero-shot SuperGLUE
• Public 20B model
• Also finetunes really well
• Paper: https://arxiv.org/abs/2205.05131

Book

• Maps and Loss of Self
• Thinking about adding something about music, but that may be a separate thing
• Asymmetrical perceptions of partisan political bots
  • Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study investigates the ability to differentiate bots with partisan personas from humans on Twitter. Our online experiment (N = 656) explores how various characteristics of the participants and of the stimulus profiles bias recognition accuracy. The analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.

SBIRs

• 9:15 standup
• Make RCSNode

Book

• Added more on Lists

SBIRs

• Add markings to report
• Work on tree graph recursion. Getting there:

• Need to try different hierarchies and make sure the spacing still works. A win for recursion!

• Need to add text for the command, cur_state, and response. That means making an RCSNode that inherits from MovableNode and has more text handles
• We’re having funding problems for IRAD, so it’s back to the unofficial hidden agenda for important things?

Inferring strategies from observations in long iterated Prisoner’s dilemma experiments

• While many theoretical studies have revealed the strategies that could lead to and maintain cooperation in the Iterated Prisoner’s dilemma, less is known about what human participants actually do in this game and how strategies change when being confronted with anonymous partners in each round. Previous attempts used short experiments, made different assumptions of possible strategies, and led to very different conclusions. We present here two long treatments that differ in the partner matching strategy used, i.e. fixed or shuffled partners. Here we use unsupervised methods to cluster the players based on their actions and then Hidden Markov Model to infer what the memory-one strategies are in each cluster. Analysis of the inferred strategies reveals that fixed partner interaction leads to behavioral self-organization. Shuffled partners generate subgroups of memory-one strategies that remain entangled, apparently blocking the self-selection process that leads to fully cooperating participants in the fixed partner treatment. Analyzing the latter in more detail shows that AllC, AllD, TFT- and WSLS-like behavior can be observed. This study also reveals that long treatments are needed as experiments with less than 25 rounds capture mostly the learning phase participants go through in these kinds of experiments.

Book

• Finished up alignment in belief space, started on lists, stories, games and maps. I also downloaded the whole project and stuck it in subversion. Don’t want to lose it

SBIRs

• Monday
  • Sprint demos
  • MDA meeting
  • Discussion with Ron about Stories
• Stories!
• Meeting with Rukan
• 9:15 Sprint planning
• 11:00 Meeting with Dr. Edwards

GPT Agents

• 3:30 meeting

Found this today: transdiffusion.org: Founded in 1964, Transdiffusion’s huge archive of television and radio material is provided free to people wishing to learn more about the history of broadcasting in the UK

Book

• Working on the Humans and Information chapter. Needs a LOT of work

SBIRs

• Yet more timesheet crap
• Got the graphics loading from the config file. Need to arrange in a hierarchy and draw a module that gets its information from the data dictionary
• Chat with Rukan
• Slides

Went to the USNA Capstone day yesterday which was quite cool

Book

• Working my way through Deep bias. There is definitely more to tweak in the later text

GPT Agents

• Reach out to Drew Alfgren – done
• Reach out to April Edwards – done

SBIRs

• GPT chat with Ron
• More work on hierarchy drawing – nope, working on the simaccel doc instead
• Good RCSNN discussion with Aaron

Just discovered AI21 and got an account

Book

• Going through Deep Bias, which seems to be pretty good!

SBIRs

• Adding hierarchy to the canvas
• Need to add zoom and pan to the base class
• Show state/execution/etc. in visualization

GPT Agents

• 3:30 Meeting
• Get preliminary study design, email, and flyer done before meeting!

Book

• Finished From the Serengeti to the Ecclesia. On to Deep Bias, which is loooooooooooong

SBIRs

• Started organizing the package on MDA 2
• Starting to work on drawing the running hierarchy
• Broke out the canvas as a standalone part

• Need to put it in a tab now