Yesterday was lovely! And it looks like the New Normal of warm fall is continuing. Next week is dry and high’s in the 60’s.

SBIRs

• Shared Overleaf template for Steve
• 9:00 standup
• Share template with Steve
• 3:00 Tradeshow status
• Print docs for Huntsville, and leave after meeting

GPT Agents

• Work on proposal and email to Carlos

Today is going to be crazy nice, particularly for November!

So I’m going to ride Skyline Drive

Need to start the writeup for Carlos today

Misunderstanding Democratic Backsliding

• One of the most common explanations of the ongoing wave of global democratic backsliding is that democracies are failing to deliver adequate socioeconomic goods to their citizens, leading voters to forsake democracy and embrace antidemocratic politicians who undermine democracy once elected. Yet a close look at twelve important cases of recent backsliding casts doubt on this thesis, finding that while it has some explanatory power in some cases, it has little in others, and even where it applies, it requires nuanced interpretation. Backsliding is less a result of democracies failing to deliver than of democracies failing to constrain the predatory political ambitions and methods of certain elected leaders. Policymakers and aid providers seeking to limit backsliding should tailor their diplomatic and aid interventions accordingly.

Tasks

• Verizon never showed. Maybe just get a battery backup for the fiber – ordered
• Lawn – done
• Remove water timer (any last peppers/tomatoes?) – done
• Groceries (kitty litter, fruit, dinner)
• Look at countertops – done
• Submit concept for Ignite – done

This is good and thoughtful: The Lesson: The real lesson we should draw from what occurred Tuesday. I also think there are some tactical issues: Undecided voters didn’t believe that some of the highest profile things that happened during Trump’s presidency—even if they saw these things negatively—were his fault.

Perplexity is now returning citations: “Effective immediately, all API users will see citations returned as part of their requests by default. This is not a breaking change. The *return_citations* parameter will no longer have any effect. Refer to our docs.“

Chores

• 8:00 chat with Matt – done
• Clean house – done
• Dishes – done
• Bills – done
• Yard – nope, tomorrow
• Work on book?
• Ping Carlos about HQA collaboration – Monday, but maybe write up some notes first
• Ping Nathan – done
• 5:00 Verizon
• Call Barbara – pinged

The link between media consumption and public opinion

Tasks

• Scheduled verizon tech for tomorrow
• Call Jim Donnies with VIN – done

SBIRS

• 9:00 Standup – done
• 12:50 USNA – good meeting
• 4:30 Book club – cancelled for today since Ron couldn’t make it
• Work on LM proposal – pretty much finished the first pass. I need to make some GANNT charts

GPT Agents

• 2:45 Meeting – cancelled

Wait, and now you expect me to work today?

Medicare coverage for those who live permanently outside the United States

SBIRs

• Write up notes from yesterday

GPT Agents

• Prep slides. Start with Ukraine, finish with killer apps

Welp, here we go:

Tasks:

• Caséta Wireless Fan Control – PD-FSQN (amazon $64.95)
• WiiM Pro Plus (Wirecutter), or maybe an iFi Audio ZEN Air Blue Bluetooth DAC (AudioAdvisor)
• Jim Donnies – half done

SBIRs

• Up to NJ today. Maybe chat with Aaron while I’m driving. Good meeting! I think we have a plan. Need to write things up this week.

GPT Agents

• Work on slide deck. Add the sea drone article and the nuke AI article.

Responsible Generative AI Toolkit: Tools and guidance to design, build and evaluate open AI models responsibly.

Need to apply for this: https://ejobs.umd.edu/postings/123920

SBIRs

• 9:00 Sprint demos cool gif of the day:

• 3:00 Sprint planning, which conflicts with the tradeshow tagup
• Good progress on the new book!

GPT Agents

• Work on slide deck. Add the sea drone article and the nuke AI article.

Awesome new thing:

https://lmcinnes.github.io/datamapplot_examples/arXiv/

November!

How a 19th-century news revolution sparked activists, influencers, disinformation, and the Civil War

Why the US General In Charge of Nuclear Weapons Said He Needs AI

The Man who Broke Politics

Tasks

• Call Jim Donnies
• Clean House – done
• Dishes – done
• Bills – done
• Handlebar tape
• Pull audio wiring from basement – done

Tasks

• Call Jim Donnie’s

SBIRs

• When building the randomizer:
  • Sign of the weave trig functions
  • Size of the envelope
  • range +/-
  • height +/-
• 9:00 standup – done
• Write a nice reply to RSA to see if he could provide any introductions to people who might be interested in supporting NNM research – done
• 4:30 Book club – done

I watched KH’s “closing argument” speech and it was quite good. At the same time, Aaron Rupar put together a back-to-back sample of DJT speeches from the beginning of his first campaign and his speech from yesterday. The change in Trump’s energy is stunning.

I’ve also been thinking about ways to detect manipulative images for WH/BH/AI. It could be easier to reverse engineer a prompt, then have an LLM examine that for manipulative intent. It looks like the tools exist in some form. Here’s the CLIP-based prompt generator:

Tasks

• Call Jim Donnie’s
• Halloween treats
• LED Railing

SBIRs

• 9:00 RayTune
• Continue with trajectory experimentation. I realize that I can break up a trajectory by parts. Also, I need to start using 3D
• Looks like I can generate 50k trajectories of 1,000 samples in a bit over 3 seconds! This may work.

• And I was able to split the trajectory into parts and work on them separately:

Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling | Humanities and Social Sciences Communications

• The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2023 by merging methods from narratology and inferential statistics. Both crowdworkers and large language models responded to identical prompts about creating and falling in love with an artificial human. The proposed experimental paradigm allows a direct and controlled comparison between human and LLM-generated storytelling. Responses to the Pygmalionesque prompts confirm the pervasive presence of the Pygmalion myth in the collective imaginary of both humans and large language models. All solicited narratives present a scientific or technological pursuit. The analysis reveals that narratives from GPT-3.5 and particularly GPT-4 are more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives with default settings and no additional prompting can occasionally provide innovative plot twists, they offer less imaginative scenarios and rhetoric than human-authored texts. The proposed framework argues that fiction can be used as a window into human and AI-based collective imaginary and social dimensions.

Tasks

• Call Jim Donnie’s
• Halloween treats

SBIRs

• Start on trajectory experimentation
• 9:00 Standup
• 10:00 LM/SA chat

Just a bit over a week until they start counting votes.

This looks like a nice way of creating code documentation first pass: lmdocs: Generative AI for code documentation

Tasks

• Call Jim Donnies
• Vote! Plenty of time between the morning and afternoon meetings – done!

SBIRs

• Start looking at the trade show project. I think the first thing I’ll do is set up an overleaf project. Then create a data generator to ease back into coding
  • Underlying curve with additional horizontal and vertical weave patterns.
  • Goal is to generate at least 10,000 samples fast)
  • Calculate intersections of a straight line to points on the curve. For each point, calculate the time for iterators on the two lines to intersect. It might be possible to project this into a 2D space, since in this case the lines are functions, which means the intersection is a function, too.
  • Or maybe, just have the data generator extrapolate a straight line, calculate the intercept to that, and see if at that time, the two source lines are within a threshold. I think I like that. This should be pretty fast and generate nice data.
• Do a getting started on PyTorch 2.5.
• Train a model to predict something that supports the heat map display. I think it could simply be the distance between the points at the time of intersection with the projected line.
• 10:00 – 11:30 SimAccel review. Some nice stuff! I need to talk to Ron about using some of the (RayTune at least?) pipeline for the demo project. Because I kind of like being able to specify datasets, a range of architectures, and let it decide what the best/fastest model for learning a new trajectory/intersection dataset.
• Uploaded the proposal to the ASRC overleaf. Some last-second tweaks, so redid that.
• 3:00 Tradeshow demo tagup.