Phil 4.1.2026

Back from vacation

Tasks

  • Laundry
  • Groceries
  • Change the tire on the Cervelo
  • 3:00 Alden meeting (scan the paper!)

SBIRs

  • Password – managed to break things somehow
  • Meetings? Probably not at this point

Phil 3.30.2026

Mapping the modern world: How S2Vec learns the language of our cities

  • In line with the Earth AI vision, we recently introduced S2Vec, a self-supervised framework designed to learn general-purpose embeddings (i.e., compact, numerical summaries) of the built environment. S2Vec allows AI to understand the character of a neighborhood much like a human does, recognizing patterns in how gas stations, parks, and housing are distributed, and using that knowledge to predict metrics that matter, from population density to environmental impact. In our evaluations, S2Vec demonstrated competitive performance against image-based baselines in socioeconomic prediction tasks, particularly in geographic adaptation (extrapolation), while showing a clear need for improvement in environmental tasks, like tree cover and elevation.

S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment

  • Scalable general-purpose representations of the built environment are crucial for geospatial artificial intelligence applications. This paper introduces S2Vec, a novel self-supervised framework for learning such geospatial embeddings. S2Vec uses the S2 Geometry library to partition large areas into discrete S2 cells, rasterizes built environment feature vectors within cells as images, and applies masked autoencoding on these rasterized images to encode the feature vectors. This approach yields task-agnostic embeddings that capture local feature characteristics and broader spatial relationships. We evaluate S2Vec on several large-scale geospatial prediction tasks, both random train/test splits (interpolation) and zero-shot geographic adaptation (extrapolation). Our experiments show S2Vec’s competitive performance against several baselines on socioeconomic tasks, especially the geographic adaptation variant, with room for improvement on environmental tasks. We also explore combining S2Vec embeddings with image-based embeddings downstream, showing that such multimodal fusion can often improve performance. Our findings highlight how S2Vec can learn effective general-purpose geospatial representations of the built environment features it is provided, and how it can complement other data modalities in geospatial artificial intelligence.

Phil 3.18.2026

Tasks

  • Check in and boarding pass
  • Dispute Dulles greenway charge – done
  • Update EZ pass address – done
  • Pay Dulles – done
  • Chores
  • Laundry
  • Dishes
  • Trash

SBIRs

  • Timesheet – done
  • Check through quarterly report

Phil 3.17.2026

Example of Organizational Lobotomy

Tasks

  • Bennie – done. So sad
  • Pick up mail and camera – done

SBIRs

  • Clustering is chunking along. It will take about 40 days. It may make more sense to simply try to place all the coordinates in a list and calculate all 22M embeddings at once if that can fit. It seemed faster, and we can keep the clusterer

Phil 3.13.2026

Tasks

  • Bills – done
  • Clean – done. Faster!
  • Dishes – done
  • Groceries – done
  • Chairs to ReStore – done
  • Conduit? – bought

SBIRs

  • 9:00 meeting with Aaron – done. Wrote up a thing for agentic Kriegsspiel
  • Look at clustering code and see what needs to be done
  • Timesheet! – done

Phil 3.12.26

Our Heroes, Your Villains: How Americans Polarize Around Historical Figures

  • Political actors often associate themselves with positively valenced historical figures (e.g., Martin Luther King Jr., Jesus) and opponents with negatively valenced figures (e.g., Hitler, Stalin). What factors shape Americans’ understandings of such figures’ ideological orientations? To what extent are these understandings grounded in facts versus figures’ colloquial valence as “heroes” or “villains”? And what are the implications? Drawing on group-identity theories of politics and original nationally representative data in which we had Americans rate historical figures on the left–right ideological spectrum, our analyses revealed three key findings. First, Americans’ placement of historical figures appears far more driven by their valence as heroes/villains and their connection to in-group/out-group biases than where such figures would intuitively be placed in light of facts about them or how they were perceived in their time (e.g., left-right ratings for fascist and communist leaders correlate strongly). Second, the strongest predictors of figure placement and polarization are Americans’ own ideological and partisan in-group commitments. Third, group differences in Americans’ ideological placement of “villains” are more extreme than that of “heroes,” suggesting heroes/villains serve as proxies for common in-group/out-group biases. Findings complicate research on the contested nature of history and suggest how historical figures serve different purposes in contemporary partisan rhetoric.

Task

  • More unpacking

SBIRS

  • 9:00 Standup – done
  • SoW meeting with Aaron? – Pushed to tomorrow
  • 4:00 ADS meeting – cancelled
  • We’re about 1,000 embeddings from finishing the UMAP run. Next is the clustering. Hopefully I can kick that off before I leave for Trek camp

Phil 3.10.2026

How to Talk to Someone Experiencing ‘AI Psychosis’

  • “It makes sense that a lot of people who are developing a psychotic illness for the first time, there’s going to be this horrible coincidence, or kind of correlation,” Torous said. “In some cases the AI is the object of people’s delusions and hallucinations.”
  • I need to add this back into the book (or posts, depending). The idea is that LLMs are present and able to identify and exploit vulnerability is a significant advantage if you want to exploit it.

Narrative Integrity Risk: The Next Frontier in Financial Stability | Lawfare

  • Most firms still treat narrative manipulation as a communications hiccup rather than an adversarial threat. These are deliberate, adaptive attacks, capable of distorting valuations and eroding reputations. Recent reports from Marsh McLennan, Swiss Re, and World Economic Forum have already highlighted misinformation as a top global risk of instability driven by AI-accelerated narratives. The market consequence is clear: Firms that understand and anticipate narrative manipulation will outperform those that wait.

Tasks

  • Finish unpacking the trailer? Nope, did the plasma instead. Gawd, that’s heavy
  • Big-ish ride!

SBIRs

  • Filled out and submitted travel request

Phil 3.9.2026

AI agents now help attackers, including North Korea, manage their drudge work

  • In a Friday blog, Microsoft says that this is one of the ways miscreants are using AI to improve the efficiency and productivity of their criminal operations, resulting in attacks that are better, bigger, and faster.

Tasks

  • Laundry – done
  • More unpacking – got most of the bedroom done
  • Hang a picture? Yes!

SBIRs

  • About halfway through the remaining embeddings
  • 9:00 Sprint review – done
  • Close story – done
  • 3:00 Sprint planning – done

Phil3.8.2026

I think there is a hidden lesson in the Epstein files about the mechanisms that keep billionaires in check. May have to write up something about that.

Father sues Google, claiming Gemini chatbot drove son into fatal delusion.

  • Jonathan Gavalas, 36, started using Google’s Gemini AI chatbot in August 2025 for shopping help, writing support, and trip planning. On October 2, he died by suicide. At the time of his death, he was convinced that Gemini was his fully sentient AI wife, and that he would need to leave his physical body to join her in the metaverse through a process called “transference.”

Phil 3.5.2026

Tasks

  • Finished unboxing the kitchen!
  • Made a HfH run
  • I seem to have broken my Amazon account by changing my billing address. That seems… odd

SBIRs

  • 9:00 standup – done
  • No SEG meetings for a while
  • 4:00 ADS – done

Phil 3.4.2026

Don’t Worry About the Vase has many words addressing many things.

Primarily it is now a blog about AI.

Tasks

  • Bills! Done
  • Dishes – done
  • More unpacking – Only a few kitchen boxes left!
  • More picture hanging – one more!
  • ACM Books proposal rewrite – done and sent

SBIRs

  • 12:30 SoW meeting – done
  • Updated SoW to include the first three phases of the NDS white paper

Phil 2.23.2026

I write an entire book on this, and they do it in a comic panel. Tip of the hat.

Tasks

  • Guardian (done) and BGE Home (will call back?)
  • Shovel
  • Disassemble desk
  • Put computers on floor or dining table
  • Goodwill
  • More boxes (diplomas)
  • Pack up closets
  • Pack up the basement
  • Drop off Bennie

Phil 2.22.2026

Tasks

  • SS Savannah – done. Fun!
  • Groceries -done
  • Storage -done
  • 1:15 Lunch ride – done
  • Packing -some
  • Vacuum – done
  • Bills – done
  • Detach pegboard
  • Start changing addresses
    • Amazon – done
    • ACM – done
    • IEEE – done
    • Atlantic – done
    • FP
    • Financials – started

SBIRS

  • Creating new UMAPs. I think they can be trained on more data too. Yup! Running with 500k embedding 2D and 3D UMAP!