Tasks

• Book
  • Complete book information form (We have not received the author bio and photo which will accompany the book)
  • Provide alt text descriptions for all figures in their book
  • In chapter 14 “??” appears for a Figure citation – can you let us know what figures this refers to?
• Start reworking Interactions article – email is in “articles and Speaking” folder.
• Ping Shimei and Jimmy on Tolulope O. Abiola email
• Library returns and ordering
• Bills

SBIRs

• 9:00 Standup
• 12:30 Tech summit something
• 3:00 – 5:00 start on WG browser plugin

That was a nice trip

Worship me at the office altar: Why narcissistic leaders resist remote work

• Leaders have displayed diverging reactions to remote work, with some supporting it and others resisting it. Surprisingly little research has examined the personality roots of leader opposition to virtual work. Integrating the extended agency model of narcissism with media richness theory, we hypothesize that narcissistic leaders resist remote work because it threatens their motivations for power and status. In Study 1, an archival analysis of 259 Fortune 500 CEOs, unobtrusive measures of narcissism via photo size, signature size, and relative compensation predicted greater resistance to remote work in public statements early in the COVID-19 pandemic. This relationship was partially explained by exploratory proxies for narcissistic leaders’ power and status motivations, contingent on their industry not depending on frontline workers. Study 2, a preregistered three-wave survey with 359 leaders, constructively replicated and extended these results. Leader narcissism predicted resistance to remote work, mediated by power and status motivations—even after controlling for trust, the Big Five, and the remaining Dark Triad traits. In Study 3, a preregistered experiment with 546 leaders, manipulating state narcissism evoked resistance to remote work via power but not status motivation. Our findings extend knowledge about remote work and narcissistic leadership.

Tasks

• Book
  • Complete book information form (We have not received the author bio and photo which will accompany the book)
  • Provide alt text descriptions for all figures in their book
  • In chapter 14 “??” appears for a Figure citation – can you let us know what figures this refers to?
• Start reworking Interactions article – email is in “articles and Speaking” folder.
• Ping Shimei and Jimmy on Tolulope O. Abiola email
• Library returns and ordering
• Laundry! Done
• Bills
  • Trip spreadsheet – Done!
• Order replacements for the things that I seem to have lost – done
• Ping Fidelity about SN’s annuity – done
• TE – done

SBIRs

• 4:00 Meeting
• Tech summit slides in the correct format. Found the format
• Set up a daily 2 hour slot for development with Aaron

Russian saboteurs hired third operative for UK attacks

• The FT identified the previously unknown person from footage posted by Direct Action, a fake English-language far-right group run by Russia-based actors. The footage showed a man with a distinctive barbed-wire tattoo on his right wrist, spray-painting an Islamic primary school in Leyton, east London, in January last year.

Tasks

• Fidelity – Done
• Driver’s license? Tomorrow
• Finish packing bike – done
• Library – done

SBIRs

• More clustering – still running

Went on a nice, fast ride on a lovely day. Reteired-ish is nice!

Tasks

• Started breaking down the Ritchey
• Voted!
• Working on the GL/R post some more

SBIRs

• Finished the run on hdbscan parameters:

• Kicked off a run with min_cluster_size = 3 and min_samples = 15

Tasks

• Groceries – done
• Break down Ritchey Tomorrow
• Logistics with SP – done
• Vote? Tomorrow

SBIRs

• My new Linux OS had no network adaptor support. Struggled for about 2 hours before figuring out how to roll back to the earlier version using GRUB on the boot screen
• Now I can start of adding clustering in. Running!
• Clustering optomization

The Puzzling Success of Overparameterization: Lottery Tickets or Escape Dimensions?

• Lotteries and tickets are often used as a didactical analogy to explain the success of overparameterized neural networks: “larger networks succeed because they more likely contain a well-initialized subnetwork that can learn the task in isolation, much like buying more tickets increases the chances of winning a lottery.” This explanation is intuitive but misleading: it suggests that subnetworks can be treated in isolation from the rest of the network. Following this reasoning leads to interpreting learning in wide networks as a multi-start optimization process, where gradient descent simply conducts a parallel search over subnetworks. We argue that this view is flawed since, among other reasons, winning tickets can be made to fail by perturbing the rest of the network. We put forward a more accurate intuitive picture for the success of overparameterization based on the geometry of loss landscapes: increasing width expands the set of available dimensions for optimization, making it easier to escape bad local minima. Moreover, as width grows, bad minima become increasingly rare relative to good minima. As the field grows mature, it is important to refine the analogies we use to explain foundational phenomena, such as the apparent redundancy of large networks, reconciling practitioners’ intuitions with modern theoretical insights.

Tasks

• Bills – done
• Chores – started
• Dishes – done
• Work on the GL/R post. Start adding in the end of R, where words transition to deeds and why that matter
• Library run – done
• 3:00 Fidelity – done

SBIRs

• Finish the embedding run and verify that everything worked. Then power down and do clustering Monday – done

Predictive Data Debugging: Reveal and Shape What Your Model Learns, Before You Train

• We introduce predictive data debugging: given a preference dataset, we can accurately predict which behaviors RL will amplify or suppress before you train, trace them back to the responsible data, and reshape the dataset and/or training process to prevent undesired effects.

Tasks

• Finish the GL/R post
• Good day to go up Hamburg – hot!
• Did the 2026 Form 1 thing!

SBIRs

• 9:00 standup – done
• Code up the full dimension reducer/clusterer – done and running!
• 4:00 ADS Meeting – cancelled

Looks like a rainy day. It was, but with a break in the middle!

Tasks

• More on the G/R post – progress, and I seem to have published the draft, which means I really need to finish it
• Library? Done. And another book showed up after
• Installed mirrors on the other road bikes

SBIRs

• The reducer code had some kind of memory leak and killed the IDE. Restarting where it left off. Done
• I’ll also need to re-run the entire code with a smaller table (100k) to be able to see which reducers to use – done. Interesting results, too!

• Code up the full dimension reducer/clusterer

Motor insurance frausters abusing AI to exaggerate claims

Tasks

• More G/L post
• Returned to sender

SBIRs

• Updated the doc and sent back to Tivern
• 9:00 Standup
• SEG lift stop work – done?
• 1:00 – 2:00 Discussion on WG – done
• 5:00 WG meeting? Done. Meh

My Moltbook post got published in the June edition of CACM!

Tasks

• Post office – need to try again
• Library – done
• Working on the Gaslight/Rashomon post

SBIRs

• Travel expenses – done
• Abstract got approved. Time to start thinking about slides

5 hours of driving for a one-hour meeting. I am so glad to be out of the car!

State media control influences large language models | Nature

• Millions of people around the world query large language models (LLMs) for
  information. Although several studies have compellingly documented the persuasive
  potential of these models, there is limited evidence of who or what influences the
  models themselves, leading to a flurry of concerns about which companies and
  governments build and regulate the models. Here we show through six studies that
  government control of the media across the world already influences the output of
  LLMs via their training data. We use a cross-national audit to show that LLMs exhibit
  a stronger pro-government valence in the languages of countries with lower media
  freedom than in those with higher media freedom. This result is correlational, so to
  triangulate the specific mechanism of how state media control can influence LLMs,
  we develop a multi-part case study on China’s media. We demonstrate that media
  scripted and curated by the Chinese state appears in LLM training datasets. To evaluate
  the plausible effect of this inclusion, we use an open-weight model to show that
  additional pretraining on Chinese state-coordinated media generates more positive
  answers to prompts about Chinese political institutions and leaders. We link this
  phenomenon to commercial models through two audit studies demonstrating that
  prompting models in Chinese generates more positive responses about China’s
  institutions and leaders than do the same queries in English. The combination of
  influence and persuasive potential across languages suggests the troubling conclusion
  that states and powerful institutions have increased strategic incentives to leverage
  media control in the hopes of shaping LLM output.

Tasks

• Chores – done
• Dishes – done
• Bills
• Ride up to Mike’s – done
• Suz at 2:15 – done

SBIRs

• Finish Q5 report – done
• 3:00 tagup meeting – cancelled
• 4:00 ADS – done

I got something published in CACM! Minimally Acceptable Systems: Tolerable at the Lowest Cost Possible – Communications of the ACM

Gemma 4 12B: The Developer Guide – Google Developers Blog

• 1.Native MacOS Apps: The mobile Google AI Edge Gallery is officially expanding to desktop platforms, running Gemma 4 12B offline, natively on Apple Silicon GPUs. It comes with a secure sandboxed Python execution loop to write, execute, and plot scientific charts inside the chat bubble. In parallel, the Google AI Edge Eloquent app on Mac launches support for Gemma 12B to power Voice Edit conversational inputs.

Hyperfascism’s sole imperative is to produce one horrible, transfixing image after another; the images themselves may be hyperreal, but the cost of creating them is all too real.

Tasks

• https://www.chaircaningbaltimore.com/contact-us/

SBIRs

• Q5 report – first draft is done
• Travel – done
• Dis something(?) for the Dahlgren trip

Source: Why AI Agents Fail in Production – by Paolo Perrone

Tasks

• 10:20 Dentist – done
• Send Sean updated book screenshots – done. Need to repackage the project

SBIRs

• 9:30 Meeting – technical problems. Became a 3:00 meeting
• Work on Q5 report. Need to finish COB Monday
• Travel

Tasks

• Some laundry – done
• Book edits – done

SBIRs

• Password? Can’t do anything. Filed a ticket. Fixed
• Travel
• 9:00 Sprint Review – done
• Stories – done and thrown out. we are on a fire drill for some reason
• 3:00 Sprint Planning