muckrAIkers

OpenAI is pivoting to porn while public sentiment turns decisively against AI. Pew Research shows Americans are now concerned over excited by a 2:1 margin. We trace how we got here: broken promises of cancer cures replaced by addiction mechanics and expensive APIs. Meanwhile, data centers are hiding a near-recession, straining power grids, and literally breaking your household appliances. Drawing parallels to the 1970s AI winter, we argue the bubble is shaking and needs to pop now, before it becomes another 2008. The good news? Grassroots resistance works. Protests have already blocked $64 billion in data center projects.

NOTE: The project that we cite for the $64 billion blockage is actually a pro-data-center campaign. The numbers still seem ok, but it's worth being aware of.

Chapters

• (00:00) - - Introduction

• Pew Research report - How People Around the World View AI
• Pew Research report - How the U.S. Public and AI Experts View Artificial Intelligence
• Pew Research report - How Americans View AI and Its Impact on People and Society
• University of Toronto report - Trust, attitudes and use of artificial intelligence: A global study 2025
• Melbourne Business School report - Key findings on public attitudes towards AI
• The Washington Post article - Americans have become more pessimistic about AI. Why?
• The New York Times article - From Mexico to Ireland, Fury Mounts Over a Global A.I. Frenzy
• The Guardian article - ‘It shows such a laziness’: why I refuse to date someone who uses ChatGPT
• The Register article - OpenAI's ChatGPT is so popular that almost no one will pay for it

AI and Claims of Curing Cancer

• Rachel Thomas, PhD blogpost - “AI will cure cancer” misunderstands both AI and medicine
• The Atlantic article - OpenAI Wants to Cure Cancer. So Why Did It Make a Web Browser?
• Independent article - ChatGPT boss predicts when AI could cure cancer
• The Atlantic article - AI Executives Promise Cancer Cures. Here’s the Reality

AI Porn and the Addiction Economy

• Forbes article - ChatGPT Will Allow ‘Erotica’ After Easing Mental Health Restrictions, Sam Altman Says
• The Addiction Economy website
• PPC article - OpenAI is staffing up to turn ChatGPT into an ad platform
• Tom Nicholas video - Vape-o-nomics: Why Everything is Addictive Now

AI Bubble

• Fast Company article - AI isn’t replacing jobs. AI spending is
• Pivot to AI article - The finance press finally starts talking about the ‘AI bubble’
• Fortune article - Without data centers, GDP growth was 0.1% in the first half of 2025, Harvard economist says
• The Atlantic article - Just How Bad Would an AI Bubble Be?
• The New York Times article - Debt Has Entered the A.I. Boom
• Will Lockett's Newsletter article - AI Pullback Has Officially Started
• Reuters article - Michael Burry of 'Big Short' fame is closing his hedge fund
• Business Insider article - The guy who shorted Enron has a warning about the AI boom

Datacenters

• Bloomberg article - AI Needs So Much Power, It’s Making Yours Worse
• Data Center Watch report - $64 billion of data center projects have been blocked or delayed amid local opposition
• More Perfect Union video - We Found the Hidden Cost of Data Centers. It's in Your Electric Bill
• DataCenter Knowledge article - Why Communities Are Protesting Data Centers – And How the Industry Can Respond

Fighting Back

• Knight First Amendment Institute essay - AI as Normal Technology
• Pranksters vs. Autocrats chapter - Laughtivism: The Secret Ingredient
• SPSP article - Playing with Power: Humor as Everyday Resistance
• Blood in the Machine article - The Luddite Renaissance is in full swing

Jacob and Igor argue that AI safety is hurting users, not helping them. The techniques used to make chatbots "safe" and "aligned," such as instruction tuning and RLHF, anthropomorphize AI systems such they take advantage of our instincts as social beings. At the same time, Big Tech companies push these systems for "wellness" while dodging healthcare liability, causing real harms today We discuss what actual safety would look like, drawing on self-driving car regulations.

Chapters

• (00:00) - Introduction & AI Investment Insanity

AI Ethics & Philosophy

• Foreign affairs article - The Cost of the AGI Delusion
• Nature article - Principles alone cannot guarantee ethical AI
• Xeiaso blog post - Who Do Assistants Serve?
• Argmin article - The Banal Evil of AI Safety
• AI Panic News article - The Rationality Trap

AI Model Bias, Failures, and Impacts

• BBC news article - AI Image Generation Issues
• The New York Times article - Google Gemini German Uniforms Controversy
• The Verge article - Google Gemini's Embarrassing AI Pictures
• NPR article - Grok, Elon Musk, and Antisemitic/Racist Content
• AccelerAId blog post - How AI Nudges are Transforming Up-and Cross-Selling
• AI Took My Job website

AI Mental Health & Safety Concerns

• Euronews article - AI Chatbot Tragedy
• Popular Mechanics article - OpenAI and Psychosis
• Psychology Today article - The Emerging Problem of AI Psychosis
• Rolling Stone article - AI Spiritual Delusions Destroying Human Relationships
• The New York Times article - AI Chatbots and Delusions

Guidelines, Governance, and Censorship

• Preprint - R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model
• Minds & Machines article - The Ethics of AI Ethics: An Evaluation of Guidelines
• SSRN paper - Instrument Choice in AI Governance
• Anthropic announcement - Claude Gov Models for U.S. National Security Customers
• Anthropic documentation - Claude's Constitution
• Reuters investigation - Meta AI Chatbot Guidelines
• Swiss Federal Council consultation - Swiss AI Consultation Procedures
• Grok Prompts Github Repo
• Simon Willison blog post - Grok 4 Heavy

We dig into how the concept of AI "safety" has been co-opted and weaponized by tech companies. Starting with examples like Mecha-Hitler Grok, we explore how real safety engineering differs from AI "alignment," the myth of the alignment tax, and why this semantic confusion matters for actual safety.

• (00:00) - Intro

• JMLR article - Underspecification Presents Challenges for Credibility in Modern Machine Learning
• Trail of Bits paper - Towards Comprehensive Risk Assessments and Assurance of AI-Based Systems
• SSRN paper - Uniqueness Bias: Why It Matters, How to Curb It

Additional Referenced Papers

• NeurIPS paper - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
• ICML paper - AI Control: Improving Safety Despite Intentional Subversion
• ICML paper - DarkBench: Benchmarking Dark Patterns in Large Language Models
• OSF preprint - Current Real-World Use of Large Language Models for Mental Health
• Anthropic preprint - Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Inciting Examples

• ars Technica article - US government agency drops Grok after MechaHitler backlash, report says
• The Guardian article - Musk’s AI Grok bot rants about ‘white genocide’ in South Africa in unrelated chats
• BBC article - Update that made ChatGPT 'dangerously' sycophantic pulled

Other Sources

• London Daily article - UK AI Safety Institute Rebrands as AI Security Institute to Focus on Crime and National Security
• Vice article - Prominent AI Philosopher and ‘Father’ of Longtermism Sent Very Racist Email to a 90s Philosophy Listserv
• LessWrong blogpost - "notkilleveryoneism" sounds dumb (see comments)
• EA Forum blogpost - An Overview of the AI Safety Funding Situation
• Book by Dmitry Chernov and Didier Sornette - Man-made Catastrophes and Risk Information Concealment
• Euronews article - OpenAI adds mental health safeguards to ChatGPT, saying chatbot has fed into users’ ‘delusions’
• Pleias website
• Wikipedia page on Jaywalking

In this episode, we redefine AI's "reasoning" as mere rambling, exposing the "illusion of thinking" and "Potemkin understanding" in current models. We contrast the classical definition of reasoning (requiring logic and consistency) with Big Tech's new version, which is a generic statement about information processing. We explain how Large Rambling Models generate extensive, often irrelevant, rambling traces that appear to improve benchmarks, largely due to best-of-N sampling and benchmark gaming.

Words and definitions actually matter! Carelessness leads to misplaced investments and an overestimation of systems that are currently just surprisingly useful autocorrects.

• (00:00) - Intro

• Apple paper - The Illusion of Thinking
• ICML 2025 paper - Potemkin Understanding in Large Language Models
• Preprint - Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Theoretical understanding

• Max M. Schlereth Manuscript - The limits of AGI part II
• Preprint - (How) Do Reasoning Models Reason?
• Preprint - A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
• NeurIPS 2024 paper - How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad

Empirical explanations

• Preprint - How Do Large Language Monkeys Get Their Power (Laws)?
• Andon Labs Preprint - Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents
• LeapLab, Tsinghua University and Shanghai Jiao Tong University paper - Does Reinforcement Learning Really Incentivize Reasoning Capacity
• Preprint - RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs
• Preprint - Mind The Gap: Deep Learning Doesn't Learn Deeply
• Preprint - Measuring AI Ability to Complete Long Tasks
• Preprint - GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Other sources

• Zuck's Haul webpage - Meta's talent acquisition tracker
  • Hacker News discussion - Opinions from the AI community
• Interconnects blogpost - The rise of reasoning machines
• Anthropic blog - Project Vend: Can Claude run a small shop?

In this episode, we break down Trump's "One Big Beautiful Bill" and its dystopian AI provisions: automated fraud detection systems, centralized citizen databases, military AI integration, and a 10-year moratorium blocking all state AI regulation. We explore the historical parallels with authoritarian data consolidation and why this represents a fundamental shift away from limited government principles once held by US conservatives.

• (00:00) - Intro

• Congress page on the One Big Beautiful Bill Act
• NYMag article - Republicans Admit They Didn’t Even Read Their Big Beautiful Bill
• Everything is Horrible Blogpost - They Did Vote For This (GOP House Edition)

Authoritarianism

• Historical context
  • Holocaust Encyclopedia article - Gleichschaltung: Coordinating the Nazi State
  • Wikipedia article - 1943 Amsterdam civil registry office bombing
  • Wikipedia article - Four Ds
• Conservative leaning, pro-privacy, anti-government
  • Data Governance Hub blogpost - Review and Literature Guide of Trump’s “One Big Beautiful Dataset”
  • Cato Institute blogpost - If You Value Privacy, Resist Any Form of National ID Cards
  • American Enterprise Intitute blogpost - The Dangerous Road to a “Master File”—Why Linking Government Databases Is a Terrible Idea
  • EFF blogpost - The Dangers of Consolidating All Government Information
• ACLU against national ID cards
  • ACLU main page on national ID cards
  • ACLU blogpost - National Identification Cards: Why Does the ACLU Oppose a National I.D. System?
  • ACLU blogpost - 5 Problems with National ID Cards
• Inherent unfairness of ML
  • Lighthouse Reports investigation - The Limits of Ethical AI
  • Lighthouse Reports investigation - Suspicion Machines
  • Amazon Science publication - Bias preservation in machine learning: The legality of fairness metrics under EU non-discrimination law
  • Michigan Technology Law Review article - The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
  • Wired article - Health Care Bias Is Dangerous. But So Are ‘Fairness’ Algorithms

Military

• WallStreet Journal article - The Army’s Newest Recruits: Tech Execs From Meta, OpenAI and More
• Trump executive order - Unleashing American Drone Dominance
• Anthropic press release - Claude Gov Models for U.S. National Security Customers

Moratorium on State AI Regulation

• TechPolicy.Press article - The State AI Laws Likeliest To Be Blocked by a Moratorium
• Forbes article - Colorado’s AI Law Still Stands After Update Effort Fails

Other Sources

• KPMG report - Incentives and credits tax provisions in “One Big Beautiful Bill Act”
• The Register article - Trump team leaks AI plans in public GitHub repository
• WallStreet Journal article - To Feed Power-Wolfing AI, Lawmakers Are Embracing Nuclear
• CBS Austin article - IRS direct file program exceeded its expectations but faces uncertain future

Jacob and Igor tackle the wild claims about AI's economic impact by examining three main clusters of arguments: automating expensive tasks like programming, removing "cost centers" like call centers and corporate art, and claims of explosive growth. They dig into the actual data, debunk the hype, and explain why most productivity claims don't hold up in practice. Plus: MIT denounces a paper with fabricated data, and Grok randomly promotes white genocide myths.

• (00:00) - Recording date + intro

• University of Chicago working paper - Large Language Models, Small Labor Market Effects
• OECD working paper - Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence
• Epoch AI blogpost - Explosive Growth from AI: A Review of the Arguments
• Business Insider article - Anthropic CEO: AI Will Be Writing 90% of Code in 3 to 6 Months
• Preprint - Transformative AGI by 2043 is <1% likely

Automating profit centers

• Pivot to AI blogpost - If AI is so good at coding … where are the open source contributions?
• Ben Evans' Mastodon post - "Show me the pull requests"
• NY Times article - Your A.I. Radiologist Will Not Be With You Soon
• FastCompany article - More companies are adopting 'AI-first' strategies. Here's how it could impact the environment
• Forbes article - Business Tech News: Shopify CEO Says AI First Before Employees
• Newsroom article - IBM Study: CEOs Double Down on AI While Navigating Enterprise Hurdles
• PNAS research article - Evidence of a social evaluation penalty for using AI
• Ars Technica article - AI use damages professional reputation, study suggests

Removing cost centers

• The Register article - Anthopic's law firm blames Claude hallucinations for errors
• Fortune article - Klarna plans to hire humans again, as new landmark survey reveals most AI projects fail to deliver
• Wikipedia article - The Market for Lemons

AlphaEvolve

• Deepmind press release - AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
• Deepmind white paper - AlphaEvolve: A coding agent for scientific and algorithmic discovery

Off Topic

• VelvetShark blogpost - Why do AI company logos look like buttholes?
• MIT Economics press release - Assuring an accurate research record
• Pivot to AI blogpost - How to make a splash in AI economics: fake your data
• Pivot to AI blogpost - Even Elon Musk can’t make Grok claim a ‘white genocide’ in South Africa

DeepSeek has been out for over 2 months now, and things have begun to settle down. We take this opportunity to contextualize the developments that have occurred in its wake, both within the AI industry and the world economy. As systems get more "agentic" and users are willing to spend increasing amounts of time waiting for their outputs, the value of supposed "reasoning" models continues to be peddled by AI system developers, but does the data really back these claims?

Check out our DeepSeek minisode for a snappier overview!

EPISODE RECORDED 2025.03.30

• (00:40) - DeepSeek R1 recap

• DeepSeek website
• DeepSeek paper
• DeepSeek docs - Models and Pricing
• DeepSeek repo - 3FS

Understanding DeepSeek/DeepResearch

• Explainers
  • Language Models & Co. article - The Illustrated DeepSeek-R1
  • Towards Data Science article - DeepSeek-V3 Explained 1: Multi-head Latent Attention
  • Jina.ai article - A Practical Guide to Implementing DeepSearch/DeepResearch
  • Han, Not Solo blogpost - The Differences between Deep Research, Deep Research, and Deep Research
• Analysis and Research
  • Preprint - Understanding R1-Zero-Like Training: A Critical Perspective
  • Blogpost - There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study
  • Preprint - Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
  • Preprint - Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Fallout coverage

• TechCrunch article - OpenAI calls DeepSeek 'state-controlled,' calls for bans on 'PRC-produced' models
• The Verge article - OpenAI has evidence that its models helped train China’s DeepSeek
• Interesting Engineer article - $6M myth: DeepSeek’s true AI cost is 216x higher at $1.3B, research reveals
• Ars Technica article - Microsoft now hosts AI model accused of copying OpenAI data
• The Signal article - Nvidia loses nearly $600 billion in DeepSeek crash
• Yahoo Finance article - The 'Magnificent 7' stocks are having their worst quarter in more than 2 years
• Reuters article - Microsoft pulls back from more data center leases in US and Europe, analysts say

US governance

• National Law Review article - Three States Ban DeepSeek Use on State Devices and Networks
• CNN article - US lawmakers want to ban DeepSeek from government devices
• House bill - No DeepSeek on Government Devices Act
• Senate bill - Decoupling America's Artificial Intelligence Capabilities from China Act of 2025

Leaderboards

• aider
• LiveBench
• LM Arena
• Konwinski Prize
• Preprint - SWE-Bench+: Enhanced Coding Benchmark for LLMs
• Cybernews article - OpenAI study proves LLMs still behind human engineers in over 1400 real-world tasks

Other References

• Anthropic report - The Anthropic Economic Index
• METR Report - Measuring AI Ability to Complete Long Tasks
• The Information article - OpenAI Discusses Building Its First Data Center for Storage
  • Deepmind report backing up this idea
• TechCrunch article - OpenAI adopts rival Anthropic's standard for connecting AI models to data
• Reuters article - OpenAI, Meta in talks with Reliance for AI partnerships, The Information reports
• 2024 AI Index report
• NDTV article - Ghibli-Style Images To Memes: White House Embraces Alt-Right Online Culture
• Elk post on DOGE and AI

DeepSeek R1 has taken the world by storm, causing a stock market crash and prompting further calls for export controls within the US. Since this story is still very much in development, with follow-up investigations and calls for governance being released almost daily, we thought it best to hold of for a little while longer to be able to tell the whole story. Nonetheless, it's a big story, so we provide a brief overview of all that's out there so far.

• (00:00) - Recording date

• DeepSeek website
• DeepSeek paper
• Reuters article - What is DeepSeek and why is it disrupting the AI sector?

Fallout coverage

• The Verge article - OpenAI has evidence that its models helped train China’s DeepSeek
• The Signal article - Nvidia loses nearly $600 billion in DeepSeek crash
• CNN article - US lawmakers want to ban DeepSeek from government devices
• Fortune article - Meta is reportedly scrambling ‘war rooms’ of engineers to figure out how DeepSeek’s AI is beating everyone else at a fraction of the price
• Dario Amodei's blogpost - On DeepSeek and Export Controls
• SemiAnalysis article - DeepSeek Debates
• Ars Technica article - Microsoft now hosts AI model accused of copying OpenAI data
• Wiz Blogpost - Wiz Research Uncovers Exposed DeepSeek Database Leaking Sensitive Information, Including Chat History

Investigations into "reasoning"

• Blogpost - There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study
• Preprint - s1: Simple test-time scaling
• Preprint - LIMO: Less is More for Reasoning
• Blogpost - Reasoning Reflections
• Preprint - Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH

Chris Canal, co-founder of EquiStamp, joins muckrAIkers as our first ever podcast guest! In this ~3.5 hour interview, we discuss intelligence vs. competencies, the importance of test-time compute, moving goalposts, the orthogonality thesis, and much more.

A seasoned software developer, Chris started EquiStamp as a way to improve our current understanding of model failure modes and capabilities in late 2023. Now a key contractor for METR, EquiStamp evaluates the next generation of LLMs from frontier model developers like OpenAI and Anthropic.

EquiStamp is hiring, so if you're a software developer interested in a fully remote opportunity with flexible working hours, join the EquiStamp Discord server and message Chris directly; oh, and let him know muckrAIkers sent you!

• (00:00) - Recording date

Links

• EquiStamp
• Chris's Twitter
• METR Paper - RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
• All Trades article - Learning from History: Preventing AGI Existential Risks through Policy by Chris Canal
• Better Systems article - The Omega Protocol: Another Manhattan Project

Superintelligence & Commentary

• Wikipedia article - Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
• Reflective Altruism article - Against the singularity hypothesis (Part 5: Bostrom on the singularity)
• Into AI Safety Interview - Scaling Democracy w/ Dr. Igor Krawczuk

Referenced Sources

• Book - Man-made Catastrophes and Risk Information Concealment: Case Studies of Major Disasters and Human Fallibility
• Artificial Intelligence Paper - Reward is Enough
• Wikipedia article - Capital and Ideology by Thomas Piketty
• Wikipedia article - Pantheon

LeCun on AGI

• "Won't Happen" - Time article - Meta’s AI Chief Yann LeCun on AGI, Open-Source, and AI Risk
• "But if it does, it'll be my research agenda latent state models, which I happen to research" - Meta Platforms Blogpost - I-JEPA: The first AI model based on Yann LeCun’s vision for more human-like AI

Other Sources

• Stanford CS Senior Project - Timing Attacks on Prompt Caching in Language Model APIs
• TechCrunch article - AI researcher François Chollet founds a new AI lab focused on AGI
• White House Fact Sheet - Ensuring U.S. Security and Economic Strength in the Age of Artificial Intelligence
• New York Post article - Bay Area lawyer drops Meta as client over CEO Mark Zuckerberg’s ‘toxic masculinity and Neo-Nazi madness’
• OpenEdition Academic Review of Thomas Piketty
• Neural Processing Letters Paper - A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks
• BFI Working Paper - Do Financial Concerns Make Workers Less Productive?
• No Mercy/No Malice article - How to Survive the Next Four Years by Scott Galloway

What happens when you bring over 15,000 machine learning nerds to one city? If your guess didn't include racism, sabotage and scandal, belated epiphanies, a spicy SoLaR panel, and many fantastic research papers, you wouldn't have captured my experience. In this episode we discuss the drama and takeaways from NeurIPS 2024.

Posters available at time of episode preparation can be found on the episode webpage.

EPISODE RECORDED 2024.12.22

• (00:00) - Recording date

Note: many workshop papers had not yet been published to arXiv as of preparing this episode, the OpenReview submission page is provided in these cases. 

• NeurIPS statement on inclusivity
• CTOL Digital Solutions article - NeurIPS 2024 Sparks Controversy: MIT Professor's Remarks Ignite "Racism" Backlash Amid Chinese Researchers’ Triumphs
• (1/2) NeurIPS Best Paper - Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
• Visual Autoregressive Model report this link now provides a 404 error
  • Don't worry, here it is on archive.is
• Reuters article - ByteDance seeks $1.1 mln damages from intern in AI breach case, report says
• CTOL Digital Solutions article - NeurIPS Award Winner Entangled in ByteDance's AI Sabotage Accusations: The Two Tales of an AI Genius
• Reddit post on Ilya's talk
• SoLaR workshop page

Referenced Sources

• Harvard Data Science Review article - Data Science at the Singularity
• Paper - Reward Reports for Reinforcement Learning
• Paper - It's Not What Machines Can Learn, It's What We Cannot Teach
• Paper - NeurIPS Reproducibility Program
• Paper - A Metric Learning Reality Check

Improving Datasets, Benchmarks, and Measurements

• Tutorial video + slides - Experimental Design and Analysis for AI Researchers (I think you need to have attended NeurIPS to access the recording, but I couldn't find a different version)
• Paper - BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
• Paper - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
• Paper - A Systematic Review of NeurIPS Dataset Management Practices
• Paper - The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track
• Paper - Benchmark Repositories for Better Benchmarking
• Paper - Croissant: A Metadata Format for ML-Ready Datasets
• Paper - Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
• Paper - Evaluating Generative AI Systems is a Social Science Measurement Challenge
• Paper - Report Cards: Qualitative Evaluation of LLMs

Governance Related

• Paper - Towards Data Governance of Frontier AI Models
• Paper - Ways Forward for Global AI Benefit Sharing
• Paper - How do we warn downstream model providers of upstream risks?
  • Unified Model Records tool
• Paper - Policy Dreamer: Diverse Public Policy Creation via Elicitation and Simulation of Human Preferences
• Paper - Monitoring Human Dependence on AI Systems with Reliance Drills
• Paper - On the Ethical Considerations of Generative Agents
• Paper - GPAI Evaluation Standards Taskforce: Towards Effective AI Governance
• Paper - Levels of Autonomy: Liability in the age of AI Agents

Certified Bangers + Useful Tools

• Paper - Model Collapse Demystified: The Case of Regression
• Paper - Preference Learning Algorithms Do Not Learn Preference Rankings
• LLM Dataset Inference paper + repo
• dattri paper + repo
• DeTikZify paper + repo

Fun Benchmarks/Datasets

• Paloma paper + dataset
• RedPajama paper + dataset
• Assemblage webpage
• WikiDBs webpage
• WhodunitBench repo
• ApeBench paper + repo
• WorkArena++ paper

Other Sources

• Paper - The Mirage of Artificial Intelligence Terms of Use Restrictions