Selling the American People: Advertising, Optimization, and the Origins of Adtech

• Algorithms, data extraction, digital marketers monetizing “eyeballs”: these all seem like such recent features of our lives. And yet, Lee McGuigan tells us in this eye-opening book, digital advertising was well underway before the widespread use of the Internet. Explaining how marketers have brandished the tools of automation and management science to exploit new profit opportunities, Selling the American People traces data-driven surveillance all the way back to the 1950s, when the computerization of the advertising business began to blend science, technology, and calculative cultures in an ideology of optimization. With that ideology came adtech, a major infrastructure of digital capitalism.

SBIRs

• Phase 2.5 meeting
• Maybe a meeting about our need for a simple method that allows for a single trajectory point input with a single analytic output. Corresponding with Bob to figure out what to do.
• Working on the paper. Need to do the motive, means and opportunity, illustrated by MI6 in the early 1940’s and Jan 6 to show how manipulation through the use of technology always targets the same human nature, and goal is to detect and disrupt those.
• Found a helpful RAND paper from 2020: Whose Story Wins: Rise of the Noosphere, Noopolitik, and Information-Age Statecraft
  • In this Perspective, the authors urge strategists to consider a new concept for adapting U.S. grand strategy to the information age—noopolitik, which favors the use of “soft power”—as a successor to realpolitik, with its emphasis on “hard power.” The authors illuminate how U.S. adversaries are already deploying dark forms of noopolitik—e.g., weaponized narratives, strategic deception, epistemic attacks. The authors propose new ways to fight back and discuss how the future of noopolitik might depend on what happens to the global commons—i.e., the parts of the Earth and space that fall outside national jurisdictions and to which all nations are supposed to have access.
• AI Ethics?
• Added the ability to filter projects by group, since there are now so many of them:

GPT Agents

• Alden meeting
• Social analytics meeting. Demo!

Order replacement speaker – sent an email

This is very cool: A Manifold View of Connectivity in the Private Backbone Networks of Hyperscalers

SBIRs

• Get some more training done – finished ethics training
• Aarons letter of recommendation
• Ping Eric for Fellow recommendation? He’s on PTO – revisit on September 7
• 9:00 Standup – mention Rukan’s struggles and how that should not be his job
• 2:00 MDA – wrote up a description of the problem and sent it off to Clay, since Lauren is missing?
• Work on lobotomy analysis – finished!

GPT Agents

• I went through and validated that everything works last night. Need to start trying it out on people:

Upgrading my IntelliJ. Hate this part

GPT Agents

• Add “unhelpful” to context and no_context bar chart – done
• Hide “re-run” button done

SBIRs

• Submit MORS abstract – done!
• Create spreadsheet of tasks, FTEs, and milestones for 2:00 meeting – done-ish. More on Wednesday
• 10:00 RFAST meeting – done
• 11:30 LM meeting with Dave M – done

Nice (hard!) ride today!

I have no idea what to make of this:

Cats learn the names of their friend cats in their daily lives

• Humans communicate with each other through language, which enables us talk about things beyond time and space. Do non-human animals learn to associate human speech with specific objects in everyday life? We examined whether cats matched familiar cats’ names and faces (Exp.1) and human family members’ names and faces (Exp.2). Cats were presented with a photo of the familiar cat’s face on a laptop monitor after hearing the same cat’s name or another cat’s name called by the subject cat’s owner (Exp.1) or an experimenter (Exp.2). Half of the trials were in a congruent condition where the name and face matched, and half were in an incongruent (mismatch) condition. Results of Exp.1 showed that household cats paid attention to the monitor for longer in the incongruent condition, suggesting an expectancy violation effect; however, café cats did not. In Exp.2, cats living in larger human families were found to look at the monitor for increasingly longer durations in the incongruent condition. Furthermore, this tendency was stronger among cats that had lived with their human family for a longer time, although we could not rule out an effect of age. This study provides evidence that cats link a companion’s name and corresponding face without explicit training.

Home energy audit at 2:00

Friday looks like the day to mow the lawn

Driverless Car Gets Stuck in Wet Concrete in San Francisco

SBIRs

• 9:00 Standup
• CSC touchpoint
• Work on abstract if Aaron is out for the rest of the week.
• Start task/milestone/FTE spreadsheet. One of the variables should be the number of models that we expect to train and validate
• Working on the organizational lobotomy analysis

GPT Agents

• Get SMTP running
• Get ‘remove from study’ working
• Need an additional test to see if someone has completed the study already. If so, put a variant of the ‘Page Not Available’ up?
• Write up a bit of instruction on what a hallucination is, so people can identify them.
• Add tooltips?
• Got nearly everything working. Tomorrow we make a dashboard and add remove from study

This is kind of interesting, particularly from the perspective of how taking advantage of an authoritarian leader’s bias could be an effective weapon: Why didn’t the Nazis beat Oppenheimer to the nuclear bomb?

• “Hitler had difficulties understanding the project” and cut support of it in 1942, Melber said. Without this backing, the nuclear program had very few resources to draw on, especially compared to the US Manhattan Project, which employed 500,000 people, about 1% of the US workforce, and cost the US government around $2 billion (today around $24 billion, or €22 billion).

SBIRs

• Going to spend the morning writing the Q6 report – done, I think
• Also need to do annual training! Got the security course done

GPT Agents

• 1:30 – 3:00 ContextTest dev. Maybe the first version? Will still need to get the Google SMPT working. Very close!
• 4:00 UMBC Social Analytics – postponed till next week since I was still working on getting the code running

SBIRs

• All day meeting in VA. I think it went well? Basically a capabilities briefing.
• Had a good chat with Aaron on the way back. We’re going to add a 4th vignette, which is from the operator’s perspective.

AC service today? Yes! Turned out to be a blown capacitor

A Huge Scam Targeting Kids With Roblox and Fortnite ‘Offers’ Has Been Hiding in Plain Sight

• Thousands of websites belonging to US government agencies, leading universities, and professional organizations have been hijacked over the last half decade and used to push scammy offers and promotions, new research has found. Many of these scams are aimed at children and attempt to trick them into downloading apps, malware, or submitting personal details in exchange for nonexistent rewards in Fortnite and Roblox.

SBIRs

• 9:00 Sprint demos
• 2:00 MDA meeting
• 3:00 Sprint planning
• Working on the abstract

GPT Agents

• 4:00 more dev. Talking to the GPT 4! Maybe have a first stab on Wednesday

Algorithms and agenda-setting in Wikileaks’ #Podestaemails release

• In the month before the 2016 U.S. Presidential election, Wikileaks released 37 serialized batches of e-mails authored by former Clinton campaign manager John Podesta. Each release was announced using a unique PodestaEmail related hashtag (#PodestaEmails2, #PodestaEmails3, etc.). In total, Podesta e-mail related hashtags hit town-wide, country-wide, or worldwide Trending topics lists a total of 1,917 times, remaining on Trending Topic lists everyday within the U.S. for 30 days before election day. In this article, we discuss how Wikileaks’ release methodology increased the potential reach of Podesta E-mail related content. We describe how Wikileaks’ tweets spoke to two audiences: Twitter users and Twitter algorithms. In serializing its content and using new hashtags for each release, Wikileaks increased the potential persistence, visibility, spreadability, and searchability of this content. By creating the possibility for this content to remain persistently visible on the Trending Topics list, Wikileaks was able to potentially realize a greater degree of agenda-setting than would have been possible through singular hashtag use.

Doxfare: Politically Motivated Leaks and the Future of the Norm on Non-Intervention in the Era of Weaponized Information.

• Alleged Russian digital interference during the 2016 U.S. presidential election presented international law with the challenge of characterizing the phenomenon of politically motivated leaks by foreign actors, carried out in cyberspace. Traditionally, international law’s norm of non-intervention applies only to acts that are coercive in nature, leaving disruptive acts outside the scope of prohibited intervention. This notion raises a host of questions on the relevancy and limited flexibility of traditional international law in relation to new threats and challenges emanating from the use of cyberspace capabilities. The discourse on transnational cyberspace operations highlights how it has become increasingly difficult to deal with nuanced activities that may cause unprecedented harms, such as the hack of the Democratic National Committee, as well as disinformation campaigns on social media, online propaganda, and sensitive information leaks. This Article argues that state interference with a legitimate political process in another state through cyberspace ought to be considered a violation of the norm of non-intervention. Although the constitutive coercion element is seemingly absent, international law should adapt to the digital era’s threats and consider non-coercive interferences that constitute “doxfare”–the public release of sensitive documents with the intent of disrupting legitimate domestic processes–as violations of the norm. As this paper contends, cyberspace operations are distinct in their effects from their physical counterparts, so a traditional standard of coercion for the norm on non-intervention is outdated and requires the introduction of a more nuanced approach, that takes into account interventions that are non-coercive in nature.

Effects of Algorithmic Trend Promotion: Evidence from Coordinated Campaigns in Twitter’s Trending Topics

• In addition to more personalized content feeds, some leading social media platforms give a prominent role to content that is more widely popular. On Twitter, “trending topics” identify popular topics of conversation on the platform, thereby promoting popular content which users might not have otherwise seen through their network. Hence, “trending topics” potentially play important roles in influencing the topics users engage with on a particular day. Using two carefully constructed data sets from India and Turkey, we study the effects of a hashtag appearing on the trending topics page on the number of tweets produced with that hashtag. We specifically aim to answer the question: How many new tweeting using that hashtag appear because a hashtag is labeled as trending? We distinguish the effects of the trending topics page from network exposure and find there is a statistically significant, but modest, return to a hashtag being featured on trending topics. Analysis of the types of users impacted by trending topics shows that the feature helps less popular and new users to discover and spread content outside their network, which they otherwise might not have been able to do.

CORE-GPT: Combining Open Access research and AI for credible, trustworthy question answering

• Looks like more context prompting with their own (maybe Llama?) LLM?
• Has been accepted to TPDL2023

SBIR’s

• Soooo many meetings today. I may do my ride at 8:30 – done!
• 10:30 Dahlgren
• 11:00 Intern presentation – much better than the dry run
• 12:00 Technical fellows – as dull as possible
• 1:00 Meeting with Aaron on white paper. We added some LOE information for Monday’s meeting
• Finish sprint, including abstract and slides – never got a chance. Need to do that before Monday morning though

GPT Agents

• More dev with Zach. Lot’s done. Need to add a “role” to the subject table that has “researcher,” “faculty,” “professor,” etc. Maybe as dropdown?

SBIRs

• Day trip to NJ for interns. It’s going to be a looooong day
• Tweaked the code example a bit to include the Obfuscated C Code Competition, since that always seems to come up
• More editing of scenario three

GPT Agents

• Lot of progress yesterday. I showed Jimmy the current state of things and he suggested making the error counting show just one item at a time. That should work nicely because the low-token prompt could go out first, and we could wait for the context prompt to finish while working on the first prompt.

Got an invite to be on the IUI 2024 program committee. I think I have to accept.

Order batteries!

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

• Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure political biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings that reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness.

SBIRs

• 2:00 BMD status
• Sent a bunch of papers over to the interns for the background section
• Started on the Q6 report

GPT Agents

• 8:30 – 9:30 more app development. And have the email domains rippled out yet?
  • Great progress!
• 3:00 – 4:00 more app development. Need to get the public version running before the meeting.
• 2:30 Alden meeting?
• 4:00 LLM meeting

Love this:

Looks like ASRC Federal is going to create a technical fellows program. Need to schedule some time to fill out the application

SBIRs

• 9:00 Standup
• 3:00(?) MDA meeting

GPT Agents

• More dev. Next is to isolate the UUID and get the LangChain calls working. Nope, worked on getting the UUID checked and placing all the experiment data in a class. Not sexy, but very cool. More work tomorrow

SBIRs

• Lots of meetings today. Like, 5 of them
• Working on the paper on the gaps – good progress!
• Some back and forth with Bob S. on generating data

GPT Agents.

• More work on the app. Got the email sending properly, which turned out to be MUCH more complicated that we thought. You need to have a domain that the email can be sent from. Anyway, got that set up but waiting a day for the domain to ripple
• Got the context root working so the app is live, if not actually working. You can see the current state here
• Next is to isolate the UUID and get the Langchain calls working

Great ride yesterday!

A New Frontier for Travel Scammers: A.I.-Generated Guidebooks

• Though she didn’t know it at the time, Ms. Kolsky had fallen victim to a new form of travel scam: shoddy guidebooks that appear to be compiled with the help of generative artificial intelligence, self-published and bolstered by sham reviews, that have proliferated in recent months on Amazon.