It was a lovely early fall day 23 years ago. I don’t remember a cloud in the sky. Man, those memories are vivid.

Catonsville cleanup day 12:00 – 2:00. Nope, it’s the 14th. Don’t know how I got confused.

SBIRs

• 12:00 CEO Employee town hall
• 1:00 AI demo. I think this is just a capability thing?
• Finished the first pass of the white paper!

Baiting the bot

• LLM chatbots can be engaged in endless “conversations” by considerably simpler text generation bots. This has some interesting implications.

SBIRs

• 9:00 Standup
• More white paper – got through the research objectives

SBIRs

• Added a bunch of links to the USNA sources for the capstone project
• 10:30 NG demo meeting?
• Made good progress on the white paper

Also took a big load of basement to the local acceptance facility. They don’t take paint, but the big one in Cockysville takes… well, pretty much everything. I’ll load up today and make another run tomorrow.

Unexpected Benefits of Self-Modeling in Neural Systems

• Self-models have been a topic of great interest for decades in studies of human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we show that when artificial networks learn to predict their internal states as an auxiliary task, they change in a fundamental way. To better perform the self-model task, the network learns to make itself simpler, more regularized, more parameter-efficient, and therefore more amenable to being predictively modeled. To test the hypothesis of self-regularizing through self-modeling, we used a range of network architectures performing three classification tasks across two modalities. In all cases, adding self-modeling caused a significant reduction in network complexity. The reduction was observed in two ways. First, the distribution of weights was narrower when self-modeling was present. Second, a measure of network complexity, the real log canonical threshold (RLCT), was smaller when self-modeling was present. Not only were measures of complexity reduced, but the reduction became more pronounced as greater training weight was placed on the auxiliary task of self-modeling. These results strongly support the hypothesis that self-modeling is more than simply a network learning to predict itself. The learning has a restructuring effect, reducing complexity and increasing parameter efficiency. This self-regularization may help explain some of the benefits of self-models reported in recent machine learning literature, as well as the adaptive value of self-models to biological systems. In particular, these findings may shed light on the possible interaction between the ability to model oneself and the ability to be more easily modeled by others in a social or cooperative context.

Chores

• House – Done
• Bills – Done
• Lawn – done
• Groceries – done
• See if I can fix the door on the truck.
• Start moving things out of the basement and into the garage – ordered boxes
• T.W. Ellis – done

Dialect prejudice predicts AI decisions about people’s character, employability, and criminality

• Hundreds of millions of people now interact with language models, with uses ranging from serving as a writing aid to informing hiring decisions. Yet these language models are known to perpetuate systematic racial prejudices, making their judgments biased in problematic ways about groups like African Americans. While prior research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice: we extend research showing that Americans hold raciolinguistic stereotypes about speakers of African American English and find that language models have the same prejudice, exhibiting covert stereotypes that are more negative than any human stereotypes about African Americans ever experimentally recorded, although closest to the ones from before the civil rights movement. By contrast, the language models’ overt stereotypes about African Americans are much more positive. We demonstrate that dialect prejudice has the potential for harmful consequences by asking language models to make hypothetical decisions about people, based only on how they speak. Language models are more likely to suggest that speakers of African American English be assigned less prestigious jobs, be convicted of crimes, and be sentenced to death. Finally, we show that existing methods for alleviating racial bias in language models such as human feedback training do not mitigate the dialect prejudice, but can exacerbate the discrepancy between covert and overt stereotypes, by teaching language models to superficially conceal the racism that they maintain on a deeper level. Our findings have far-reaching implications for the fair and safe employment of language technology.

SBIRs

• Finished and sent ONR email
• Worked on the white paper. Mostly collecting things and fleshing out the project.
• And I made a picture!

• 2:00 SimAccel meeting
• 3:05 LM collaboration meeting
• It’s interesting to me how these meetings went. Lots of discussion on how to integrate the work discussed in the white paper, but really, it was an excuse for them to “put AI in the system.” I think this is going to be hard to keep on track and the amount of money will pull everyone onto the project. And that will be the end of our IRAD department.
• 4:30 Book club

GPT-Agents

• 2:45 meeting. Will need to drop at 3:05. Made some organizational progress, and found out that there is no page limits, so the summaries don’t have to be so strict.

Beijing-Backed Trolls Target U.S. Voters as Election Nears (MSN paywall-free link)

• “One of the world’s largest covert online influence operations, an operation run by Chinese state-linked actors, has become more aggressive in its efforts to infiltrate and sway U.S. political conversations ahead of the election,” said Jack Stubbs, chief intelligence officer at the research firm Graphika, which published the report Tuesday on Spamouflage’s alleged activities.

Two RT Employees Indicted for Covertly Funding and Directing U.S. Company that Published Thousands of Videos in Furtherance of Russian Interests

• The indictment states the company described itself on its website as “a network of heterodox commentators that focus on Western political and cultural issues.” Tennessee-based company Tenet Media has the same message on its homepage. The indictment states the Tennessee-based company was incorporated around Jan. 19, 2022, which matches records from the Tennessee Secretary of State’s Office. The indictment says the company applied to the Tennessee Department of State to conduct business on May 22, 2023.

SBIRs

• Need to send an email to here. the email has to go out very soon, and a response needs to come back ASAP. Need to integrate the PMs interests, the Capstone goals, and an overarching LLMs as underutilized latent knowledge systems. Once that’s done, see if we go direct to proposal regardless. Need to look through what’s required. Written. Need to get approval/edits
• 10:30 Trade show demo planning? Yup. Fun!
• Meeting with Aaron about prompt swarms?

That is looking like a very pretty week. Except for Saturday, that is.

Work on content for Wolfram

SBIRs

• 9:00 Sprint demos
• 3:00 Sprint planning – Well, it looks like I’m probably not going to get to work on NNMs unless some funding comes in. I’m tasked to find opportunities for other projects, and to write control code for another opportunity. This is not exactly motivating. I’ve mapped out the weeks I can take off, and I’m not going to be heroic on this.
• I did find a good potential opportunity that is worth reaching out to, and if they want a proposal, will kill some time through the end of the month. So the email has to go out very soon, and a response needs to come back ASAP. I’ll work on that tomorrow. Need to integrate the PMs interests, the Capstone goals, and an overarching LLMs as underutilized latent knowledge systems.

GPT Agents

• Add the new critique. Done. Still half-baked though

It’s Labor Day, so I think a local ride, get some groceries, and clean up a few outstanding tasks.

Also, I need to pick out some stuff for the basement and finish laundry

And, it’s a good day to get stuff done for Wolfram