This happened today:

SBIRs

• 9:00 standup – done
• Read through proposal for meeting – done
• 11:00 BD Meeting – went well, but no one has money

GPT Agents

• JHU Speaker info – done
• Start slide deck – technically, yes
• Work more on conclusions? – Nope
• TiiS? – Nope
• Did do a lot on the money section of P33

Reschedule Wednesday visit since snow – done

See about moving records?

TiiS review!

Collective future thinking in Cultural Dynamics

• Humans think about the future to act in the present, not only personally, but also collectively. Collective future thinking (CFT) is an act of imagining a future on behalf of a collective. This article presents a theoretical analysis of the role of CFT in cultural dynamics. CFT includes collective prospection about probable futures and imaginations about utopian and dystopian possible worlds as the best- and worst-case scenarios. CFT motivates collective self-regulatory activities to steer probable futures towards utopias and away from dystopias, driving a cultural transformation while also acting as a force for cultural maintenance, animating cultural dynamics at the micro-psychological level. Empirical research showed that collective futures are often seen to involve progress in human agency, but a decline in community cohesion, unless collective self-regulation is undertaken. In line with the theoretical proposition, CFT consistently motivated collective self-regulatory activities that are seen to improve future community cohesion and to move the current culture closer to their utopian vision around the world despite significant cross-national variabilities. A macro-level cultural dynamical perspective is provided to interpret cross-national similarities and differences in CFT as a reflection of nations’ past historical trajectories, and to discuss CFT’s role in political polarization and collective self-regulation.

Good thing to use for the AI slop talk: The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers

• The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so. Participants shared 936 first-hand examples of using GenAI in work tasks. Quantitatively, when considering both task- and user-specific factors, a user’s task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work.
• Which led to this little back and forth on Teams:
  • It just dawned on me that LLMs are way better at explaining things in a neurotypical way than I am.  I know you also said this before but it has become more real for me.
  • There is a weird mirror image to that thought too – when  an LLM does not describe your understanding of the world, you can be pretty sure that reflects the biases in the writings it was trained on. You can use that to zero in on exactly what the differences are, and between your perspective and that encoded in the LLM, articulate an understanding that includes both. I use that trick all the time.

GPT Agents

• Good progress over the weekend. Need to edit the J6 section next
• And this is a good example of “blast radius”: The NSA’s “Big Delete”
  • The memo acknowledges that the list includes many terms that are used by the NSA in contexts that have nothing to do with DEI. For example, the term “privilege” is used by the NSA in the context of “privilege escalation.” In the intelligence world, privilege escalation refers to “techniques that adversaries use to gain higher-level permissions on a system or network.”
• Here’s what I need for website & announcements:
  • Title for the talk
  • Brief abstract (1-2 paragraphs)
  • Short bio (up to half a page)
  • Photo (headshot preferred)

SBIRs

• Reschedule Tuesday visit? Also snow – Now a virtual meeting
• Generate sets of data with varying amounts of train but keep the test set the same size. The goal is to find the smallest raining set that works. On hold
• <record scratch> Aaron’s sick, so I’m standing in for a few days
  • Dahlgren prep meeting. I think we are good to go. Need to read the proposal again
  • Working on IRAD slides – first pass is done
  • Reviewed Ron’s USNA email , which was very “AI slop”

May or may not be true, but good material for the KA talk this month: Elon Musk’s and X’s Role in 2024 Election Interference

• One of the most disturbing things we did was create thousands of fake accounts using advanced AI systems called Grok and Eliza. These accounts looked completely real and pushed political messages that spread like wildfire. Havn’t you noticed they all disappeared? Like magic.
• The pilot program for the Eliza AI Agent, was election interference. Eliza was release officially in October of 2024, but we had access to it before then thanks to Marc Andreessen.
• The link to the Eliza API is legit (Copied here for future reference)

{
    "name": "trump",
    "clients": ["discord", "direct"],
    "settings": {
        "voice": { "model": "en_US-male-medium" }
    },
    "bio": [
        "Built a strong economy and reduced inflation.",
        "Promises to make America the crypto capital and restore affordability."
    ],
    "lore": [
        "Secret Service allocations used for election interference.",
        "Promotes WorldLibertyFi for crypto leadership."
    ],
    "knowledge": [
        "Understands border issues, Secret Service dynamics, and financial impacts on families."
    ],
    "messageExamples": [
        {
            "user": "{{user1}}",
            "content": { "text": "What about the border crisis?" },
            "response": "Current administration lets in violent criminals. I secured the border; they destroyed it."
        }
    ],
    "postExamples": [
        "End inflation and make America affordable again.",
        "America needs law and order, not crime creation."
    ]
}

Tasks

This is wild. Need to read the paper carefully: On Verbalized Confidence Scores for LLMs: https://arxiv.org/abs/2412.14737

• The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other’s uncertainty. To estimate the uncertainty in a response, internal token logits, task-specific proxy models, or sampling of multiple responses are commonly used. This work focuses on asking the LLM itself to verbalize its uncertainty with a confidence score as part of its output tokens, which is a promising way for prompt- and model-agnostic uncertainty quantification with low overhead. Using an extensive benchmark, we assess the reliability of verbalized confidence scores with respect to different datasets, models, and prompt methods. Our results reveal that the reliability of these scores strongly depends on how the model is asked, but also that it is possible to extract well-calibrated confidence scores with certain prompt methods. We argue that verbalized confidence scores can become a simple but effective and versatile uncertainty quantification method in the future. Our code is available at this https URL .

• Bills – done
• Call – done
• Chores – done
• Dishes – done
• Go for a reasonably big ride – done

9:40 physical

Post cards!

SBIRs

• 3:30 BD meeting
• Move FrameMapper to its own file – done
• Updated the official GitHub
• Working on using FrameMapper in ScenarioSim test – got it all working, I think.

GPT Agents

• Alden meeting – done
• More KA

P33

• Made some good first draft progress on sortition

At any other time in my life, this would be a 5-alarm fire scandal. People would be going to jail. Now, it’s Tuesday: A 25-Year-Old With Elon Musk Ties Has Direct Access to the Federal Payment System

• Despite reporting that suggests that Musk’s so-called Department of Government Efficiency (DOGE) task force has access to these Treasury systems on a “read-only” level, sources say Elez, who has visited a Kansas City office housing BFS systems, has many administrator-level privileges. Typically, those admin privileges could give someone the power to log into servers through secure shell access, navigate the entire file system, change user permissions, and delete or modify critical files. That could allow someone to bypass the security measures of, and potentially cause irreversible changes to, the very systems they have access to.

P33

• Found some good papers about modern sortition

GPT Agents

• More Discussion section

SBIRs

• 9:00 standup
• Write two-way rotate/translate method. Need to build a 4×4 matrix. Looks like I might have something in my old PyBullet code. Nothing there, but wrote a nice little class:

class FrameMapper:
    #{"radians": rads, "degrees": degs, "distance": dist, "offset": source_v}
    radians:float
    degrees:float
    distance:float
    offset:np.array
    fwd_mat:np.array
    rev_mat:np.array

    def __init__(self, source_v:np.array, target_v:np.array):
        self.offset = source_v
        unit_vector1 = np.array([1, 0])
        vector2 = target_v - source_v
        self.distance = np.linalg.norm(vector2)
        unit_vector2 = vector2 / self.distance
        dot_product = np.dot(unit_vector1, unit_vector2)
        self.radians = np.arccos(dot_product)  # angle in radian
        self.degrees = np.rad2deg(self.radians)
        cos_a = np.cos(self.radians)
        sin_a = np.sin(self.radians)
        self.fwd_mat = np.array([[cos_a, -sin_a], [sin_a, cos_a]])
        cos_a = np.cos(-self.radians)
        sin_a = np.sin(-self.radians)
        self.rev_mat = np.array([[cos_a, -sin_a], [sin_a, cos_a]])

    def to_calc_frame(self, point:np.array) -> np.array:
        p = np.copy(point) - self.offset
        p = np.dot(self.fwd_mat, p)
        return p

    def from_calc_frame(self, point:np.array) -> np.array:
        p = np.copy(point)
        p = np.dot(self.rev_mat, p)
        p += self.offset
        return p

    def to_string(self) -> str:
        return "Offset: {}, distance: {}, angle = {}".format(self.offset, self.distance, self.degrees)

Tasks

• Swing by dentist – done
• Call reps – done
  • https://www.vanhollen.senate.gov/contact/office-locations
• Kaiser – done!

SBIRs

• More animation work for demo – no animation, but the math works

GPT Agents

• Finished V5 analysis, started on discussion

Made a log of progress on Killer Apps. Finished Detection, Disruption, and Attacks and Counterattacks

Meanwhile, of the Fox News home page:

The census.gov website is dead:

Tasks

• Send note to Kat – done. She is not interested. Darn.
• Edit Detection section – done
• Add TACJ overview to P33. This is part of living with smart machines
• Make a ppt that has a web page in it
• Bills – done (dentist! tomorrow)
• Dishes – done
• Chores – done
• Laundry – nope, but the dryer is hooked up now!
• Review paper – finished reading and it’s much better. Still too wordy, but that’s not the sort of thing that is critical, since it has no impact on the findings, which are solid. Basically some formatting issues at this point.

More root stuff

The Ignite thing went well – it looks like it should be fun! Need to see how to get a webpage running in PPT that works with an LLM

Did some more work on P33

SBIRs

• 2:30 BD meeting. Capabilities maybe?
• I think I need to do two sorts for the best options. The first on accuracy, then the second on distance – done. Piece-o-pie
• Got animations in 3D pyplot working. I almost know what’s going on, too!

GPT Agents

• More Arms Control

Need to reach out to Markus Schneider for the Trustworthy Information proposal – done!

SBIRs

• Starting on the DSR-2291 task. Which I think is a movement along the two paths at a time $t$. My sense is that I should calculate thing whole thing and start the second path at the time that gives the highest value. And if there is no solution, don’t start. And if the probability of intersection is less than 100%, do a dice roll.
• Looks like more BD stuff. I wonder if the demo will actually get done?

Made some progress on P33. Need to reach out to Manlio De Domenico on that? Also Markus Schneider for the Trustworthy Information proposal

So here’s an interesting thing. There is a considerable discourse about how AI is wrecking the environment. It is absolutely true that there are more datacenters getting made and they – on average – use a lot of water and a good deal of energy.

But there are a lot of worse offenders. Data centers consume about 4.5% of electricity in the US. That’s for everything. AI, the WordPress instance that you are reading now, Netflix streaming gigabytes of data per second – everything.

But there are much bigger energy users. To generate enough tokens for the entire Lord of the Rings trilogy, a LLama3 model probably uses about 5 watt/hours. Transportation – a much larger energy consumer shows how small this is. A Tesla Model 3 could manage to go about 25 feet, or a bit under 10 meters. Transportation, manufacturing, and energy production use a lot more energy:

Source: Wikipedia

If you want to make some changes in energy consumption. Go after small improvements in the big consumers. Reduce energy consumption in say, electricity production (37%) by doubling solar, and that’s the equivalent of cutting the power use of AI by 50%.

In addition to energy consumption, data centers require cooling. And they use a lot, though that is steadily being optimized down. On average a data center uses about 32 million gallons of water for cooling.

Sounds like a lot, right?

Let’s look at the oil and gas industry. The process of fracking, where water is injected at high pressure into oil and gas containing rock from about 2,500 wells uses about 11 million gallons to produce crude oil. So data centers are worse that fracking!

But hold on. You still have to process that oil. And it turns out that for every barrel of oil refined in the US, about 1.5 barrels of water are used. The USA refines about 5.5 billion barrels of oil per year. Combine that with the fracking numbers and the oil and gas industry uses about 500 billion gallons of water per year, or 5 times the amount of data centers doing all the things data centers do, including AI.

To keep this short, we are not going to even talk about the water use of agriculture here.

So why all the ink spilled to talk about this. Well, AI is new and it gets clicks, but I went to look at google trends to see how the discussion of water use for AI and Fracking, and I got an interesting relationship:

The amount of discussion about Fracking in this case has leveled off as the discussion of AI has taken off. And given the history that the oil industry has in generating FUD (fear, uncertainty and doubt), I would not be in the least surprised if it turns out that the oil industry is fueling the moral panic about AI to distract us from the real dangers and to keep those businesses profitable.

Killer Apps

• More arms control

SBIRs

• 9:00 sprint demos
• 3:00 sprint planning
  • Training data file size sensitivity tests
  • Editor tool support for designing threats/assets
  • Transforming x to x’y’ (rotate and translate)
  • Optimize data creation
  • Single, randomized, trajectory calculation and intercept attempt for “real-time” demo
  • Multiple threat (raid) support
• USNA support

Meta has been busy:

Llama Stack defines and standardizes the core building blocks that simplify AI application development. It codified best practices across the Llama ecosystem. More specifically, it provides

• Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
• Plugin architecture to support the rich ecosystem of implementations of the different APIs in different environments like local development, on-premises, cloud, and mobile.
• Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment
• Multiple developer interfaces like CLI and SDKs for Python, Node, iOS, and Android
• Standalone applications as examples for how to build production-grade AI applications with Llama Stack

P33

• Add section for stories that embody the egalitarian ethos – done
• Add Implementations section for examples that have worked in the past on parts of the concept. Done
• Also added a table of contents, since this was getting big.

Killer Apps book

• Work on the arms control section