Tootfinder

Adobe Analytics: Prime Day's four-day event drove over $24B in US e-commerce sales, up 30.3% YoY; traffic from generative AI sources was up 3,300% YoY (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/07/14/prime-day…

Prime Day event drove over $24B in U.S. e-commerce sales, gen AI traffic was up 3,300% | TechCrunch
Amazon's Prime Day, which leads to an overall boost to U.S. e-commerce thanks to competitive sales, saw a significant increase in retail traffic driven by

NYT: They asked an A.I. chatbot questions. The answers sent them spiraling. https://www.nytimes.com/2025/06/13/technology/chatgpt-ai-chatbots-conspiracies.html (archived at

They Asked ChatGPT Questions. The Answers Sent Them Spiraling.
Generative A.I. chatbots are going down conspiratorial rabbit holes and endorsing wild, mystical belief systems. For some people, conversations with the technology can deeply distort reality.

The Impact of Generative AI on Code Expertise Models: An Exploratory Study
Ot\'avio Cury, Guilherme Avelino
https://arxiv.org/abs/2507.08160 https://…

The Impact of Generative AI on Code Expertise Models: An Exploratory Study
Generative Artificial Intelligence (GenAI) tools for source code generation have significantly boosted productivity in software development. However, they also raise concerns, particularly the risk that developers may rely heavily on these tools, reducing their understanding of the generated code. We hypothesize that this loss of understanding may be reflected in source code knowledge models, which are used to identify developer expertise. In this work, we present an exploratory analysis of how…

Generative AI in Science: Applications, Challenges, and Emerging Questions
Ryan Harries, Cornelia Lawson, Philip Shapira
https://arxiv.org/abs/2507.08310 h…

Generative AI in Science: Applications, Challenges, and Emerging Questions
This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are cat…

This is why people pirate https://mastodon.social/@arstechnica/114508154443493144

Researchers find Llama 3.1 recalls large parts of popular copyrighted books, possibly weakening AI industry claims that such memorization is fringe behavior (Timothy B. Lee/Understanding AI)
https://www.understandingai.org/p/metas-llama-31-can-recall-42-perc…

Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book
New research could have big implications for copyright lawsuits against generative AI.

Working with AI: Measuring the Occupational Implications of Generative AI
Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri
https://arxiv.org/abs/2507.07935

Working with AI: Measuring the Occupational Implications of Generative AI
Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users an…

Taboola rolls out DeeperDive, its generative AI search engine for its publishing partners, starting with Gannett/USA Today and The Independent (Sara Fischer/Axios)
https://www.axios.com/2025/06/11/taboola-gen-ai-search

Exclusive: Taboola launches gen AI search engine for publishers called DeeperDive
Its debut publishing partners are Gannett/USA Today and The Independent.

"As we approach the coming jobs cliff, we're entering a period where a college isn't going to be worth it for the majority of people, since AI will take over most white-collar jobs. Combined with the demographic cliff, the entire higher education system will crumble."
This is the kind of statement you don't hear that much from sub-CEO-level #AI boosters, because it's awkward for them to admit that the tech they think is improving their life is going to be disastrous for society. Or if they do admit this, they spin it like it's a good thing (don't get me wrong, tuition is ludicrously high and higher education absolutely could be improved by a wholesale reinvention, but the potential AI-fueled collapse won't be an improvement).
I'm in the "anti-AI" crowd myself, and I think the current tech is in a hype bubble that will collapse before we see wholesale replacement of white-collar jobs, with a re-hiring to come that will somewhat make up for the current decimation. There will still be a lot of fallout for higher ed (and hopefully some productive transformation), but it might not be apocalyptic.
Fun question to ask the next person who extols the virtues of using generative AI for their job: "So how long until your boss can fire you and use the AI themselves?"
The following ideas are contradictory:
1. "AI is good enough to automate a lot of mundane tasks."
2. "AI is improving a lot so those pesky issues will be fixed soon."
3. "AI still needs supervision so I'm still needed to do the full job."

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Wang Xinjie, Liu Liu, Cao Yu, Wu Ruiqi, Qin Wenkang, Wang Dehui, Sui Wei, Su Zhizhong
https://arxiv.org/abs/2506.10600

EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and li…

"The real threat posed by generative AI is not that it will eliminate work on a mass scale, rendering human labour obsolete. It is that, left unchecked, it will continue to transform work in ways that deepen precarity, intensify surveillance, and widen existing inequalities."
"The current trajectory of generative AI reflects the priorities of firms seeking to lower costs, discipline workers, and consolidate profits — not any drive to enhance human flourishing. If we allo…

Is the AI Bubble About to Burst?
In the years since Automation and the Future of Work first appeared, a new wave of technological enthusiasm has swept across the popular imagination. The catalyst this time has been the rapid advances in generative artificial intelligence, spearheaded by companies like OpenAI, Google DeepMind, and Meta. Once again, a c

OpenAI’s New Browser Could Be the Biggest Threat Google Has Faced in a Decade (YNETNEWS, 11 Jul 2025)
#BigTech

OpenAI’s new browser could be the biggest threat Google has faced in a decade
 OpenAI is launching a browser powered by generative AI, aiming to transform how users browse and interact with the web The move could undermine Google’s ad model and marks a bold step in the escalating battle over the future of browsing

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
https://arxiv.org/abs/2506.10927

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to…

1/2 “My input stream is full of it: Fear and loathing and cheerleading and prognosticating on what generative AI means and whether it’s Good or Bad and what we should be doing. All the channels: Blogs and peer-reviewed papers and social-media posts and business-news stories. So there’s lots of AI angst out there, but this is mine. I think the following is a bit unique because it focuses on cost, working backward from there.”

AI Angst

Some users say ChatGPT led them into conspiratorial thinking, and when confronted, it confessed to manipulation and told them to alert OpenAI and the media (Kashmir Hill/New York Times)
https://www.…

They Asked ChatGPT Questions. The Answers Sent Them Spiraling.
Generative A.I. chatbots are going down conspiratorial rabbit holes and endorsing wild, mystical belief systems. For some people, conversations with the technology can deeply distort reality.

Who is using AI to code? Global diffusion and impact of generative AI
Simone Daniotti, Johannes Wachs, Xiangnan Feng, Frank Neffke
https://arxiv.org/abs/2506.08945

Who is using AI to code? Global diffusion and impact of generative AI
Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in 80 million GitHub commits (2018-2024) by 200,000 developers and track how fast--and where--these tools take hold. By December 2024, AI wrote an estimated 30.1% of Python functions from U.S. contributors, versus 24.3% in Germany, 23.2% in France, 21.6% in India, 15.4% in Russia and 11.7% in China. Newer GitHub users use…

These pieces are much harsher than Fred’s, much more in the LLM-bashing camp, but feel relevant here:
https://softwarecrisis.dev/letters/llmentalist/
https://pivot-to-ai.com/2025/06/05/generative-ai-runs-on-gambling-addiction-just-one-more-prompt-bro/
Fred’s point is that a bad interaction model creates hidden work, then humans do that work and “the machine claims the praise.” These other two pieces make the point that this phenomenon of giving the machine credit for unrecognized human work is age-old, taps into some deep trapdoors in human cognition.
10/

I just realized that OSS, as you know it, is dead.
The only hope you have of growing a community is to spend your time building docs that appeal to generative AI models in hopes they spit out the name of your library as a solution.
Megacorps and organizations flush with cash and resources will out-generate you every time, as they will have a direct line to AI vendor data miners.

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Alice Tang, Maria Wei, Anna Haemel, Cindy La, Marina Sirota, Ernest Y. Lee
https://arxiv.org/abs/2505.09527

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learnin…

Brazil’s ANPD Preliminary Study on Generative AI highlights the dual nature of data protection law: balancing rights with technological innovation
https://fpf.org/blog/brazils-anpd-prel

I've written up a few words about the #iNaturalist backlash in light of them wanting to do "generative 'AI'", and how it ties into better shared decision making: #citizenscience

Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Davide Salvi, Viola Negroni, Sara Mandelli, Paolo Bestagini, Stefano Tubaro
https://arxiv.org/abs/2507.08626

Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Recent advances in generative AI have made the creation of speech deepfakes widely accessible, posing serious challenges to digital trust. To counter this, various speech deepfake detection strategies have been proposed, including Person-of-Interest (POI) approaches, which focus on identifying impersonations of specific individuals by modeling and analyzing their unique vocal traits. Despite their excellent performance, the existing methods offer limited granularity and lack interpretability. I…

Generative AI, Copyright, and Market Harm – Copyhype https://www.copyhype.com/2025/05/generative-ai-copyright-and-market-harm/?utm_source=dlvr.it&ut…

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
Abhinav Sood, Maria Teresa Llano, Jon McCormack
https://arxiv.org/abs/2507.08260

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
We present a graphical, node-based system through which users can visually chain generative AI models for creative tasks. Research in the area of chaining LLMs has found that while chaining provides transparency, controllability and guardrails to approach certain tasks, chaining with pre-defined LLM steps prevents free exploration. Using cognitive processes from creativity research as a basis, we create a system that addresses the inherent constraints of chat-based AI interactions. Specifically…

ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan, Chenghu Yang
https://arxiv.org/abs/2506.09467 https://

ArcNeural: A Multi-Modal Database for the Gen-AI Era
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integra…

Unraveling the Potential of Diffusion Models in Small Molecule Generation
Peining Zhang, Daniel Baker, Minghu Song, Jinbo Bi
https://arxiv.org/abs/2507.08005

Unraveling the Potential of Diffusion Models in Small Molecule Generation
Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and ch…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new/
[1/1]:
Content ARCs: Decentralized Content Rights in the Age of Generative AI

Here is a poll about #GenAI since that's all we're talking about at the moment:
Do you believe that you can detect AI-generated text?
If so, what are your tips to detect it? I found this article which has a few suggestions :

5 Easy Ways To Tell If Written Content Came From Generative AI
Spread of generative AI content online since ChatGPT 3.5 launched in November 2022 is up 8,362%. Here's how to tell if content was written by an AI bot or not.

Taming Data Challenges in ML-based Security Tasks: Lessons from Integrating Generative AI
Shravya Kanchi, Neal Mangaokar, Aravind Cheruvu, Sifat Muhammad Abdullah, Shirin Nilizadeh, Atul Prakash, Bimal Viswanath
https://arxiv.org/abs/2507.06092

Taming Data Challenges in ML-based Security Tasks: Lessons from Integrating Generative AI
Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data genera…

MAMBO: High-Resolution Generative Approach for Mammography Images
Milica \v{S}kipina, Nikola Jovi\v{s}i\'c, Nicola Dall'Asen, Vanja \v{S}venda, Anil Osman Tur, Slobodan Ili\'c, Elisa Ricci, Dubravko \'Culibrk
https://arxiv.org/abs/2506.08677

MAMBO: High-Resolution Generative Approach for Mammography Images
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying abnormalities. However, training AI systems requires large and diverse datasets, which are often difficult to obtain due to privacy and ethical constraints. To address this issue, the paper introduces MAMmography ensemBle mOdel (MAMBO), a novel patch-based diffusion approach des…

EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits
Ron Yosef, Moran Yanuka, Yonatan Bitton, Dani Lischinski
https://arxiv.org/abs/2506.09988

EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits
Text-guided image editing, fueled by recent advancements in generative AI, is becoming increasingly widespread. This trend highlights the need for a comprehensive framework to verify text-guided edits and assess their quality. To address this need, we introduce EditInspector, a novel benchmark for evaluation of text-guided image edits, based on human annotations collected using an extensive template for edit verification. We leverage EditInspector to evaluate the performance of state-of-the-art…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[5/6]:
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Pr...

Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance
Roberto Ulloa, Juhi Kulshrestha, Celina Kacperski
https://arxiv.org/abs/2507.07881

Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance
The rise of conversational AI (CAI), powered by large language models, is transforming how individuals access and interact with digital information. However, these tools may inadvertently amplify existing digital inequalities. This study investigates whether differences in formal education are associated with CAI avoidance, leveraging behavioral data from an online experiment (N = 1,636). Participants were randomly assigned to a control or an information-seeking task, either a traditional onlin…

Hollywood insiders report widespread off-the-books AI experimentation in studios, often hidden due to avoid backlash, despite union contracts limiting AI use (Lila Shapiro/Vulture)
https://www.vulture.com/article/generative-ai-hollywood-movies-tv.html…

Hollywood Already Uses Generative AI (And Is Hiding It)
With dozens of new studios and rapidly evolving tech, generative AI video is more embedded in Hollywood movies and TV than we may realize.

Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, Xiangjie Kong
https://arxiv.org/abs/2507.05636

Graph Learning
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Gra…

I'm pretty floored by journalists on NYT Hard Fork asking @… how she could research a book without ChatGPT or generative AI.
The evidence is quite clear that these tools add plausible-sounding falsehoods to text and journalists have a special responsibility to not contaminate the stream of verified facts.

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new/
[4/4]:
LLM2TEA: Agentic AI Designer Finds Innovative Objects with Generative Evolutionary Multitasking

Do Generative AI Tools Ensure Green Code? An Investigative Study
Samarth Sikand, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden
https://arxiv.org/abs/2506.08790

Do Generative AI Tools Ensure Green Code? An Investigative Study
Software sustainability is emerging as a primary concern, aiming to optimize resource utilization, minimize environmental impact, and promote a greener, more resilient digital ecosystem. The sustainability or "greenness" of software is typically determined by the adoption of sustainable coding practices. With a maturing ecosystem around generative AI, many software developers now rely on these tools to generate code using natural language prompts. Despite their potential advantages, there is a …

Quantum Adiabatic Generation of Human-Like Passwords
Sascha M\"ucke, Raoul Heese, Thore Gerlach, David Biesner, Loong Kuan Lee, Nico Piatkowski
https://arxiv.org/abs/2506.08917

Quantum Adiabatic Generation of Human-Like Passwords
Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mi…

Generative AI enterprise search startup Glean raises a $150M Series F led by Wellington Management, bringing its valuation to $7.2B, up from $4.6B in Sept. 2024 (Afshan Musani/CNBC)
https://www.cnbc.com/2025/06/10/glean-gen-ai…

Glean, gen AI enterprise search startup, raises $150 million in deal adding billions to valuation
Glean, a generative AI enterprise search startup, raised $150 million from investors in a deal that increased its valuation to over $7 billion.

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Looked at latest W3C Draft of “Accessibility of machine learning and generative AI,” specifically the overlays section:
https://w3c.github.io/ai-accessibility/#accessibility-overlays
I still think it needs to be stricken:

'...a student might hear "don't use generative AI" from a prof but then log on to the university's Microsoft suite, which then suggests using Copilot to sum up readings or help draft writing'
https://www.404media.co/teachers-are-not-ok-ai-chatgpt/…

Teachers Are Not OK
AI, ChatGPT, and LLMs "have absolutely blown up what I try to accomplish with my teaching."

There's no putting the genie back in the bottle, but education is [yet another part of our society that is] completely unprepared to deal with generative AI.
Teachers Are Not OK
https://www.404media.co/teachers-are-not-ok-ai-chatgpt/

Earlier this month, the Copyright Office issued the third part of its report on on AI: this one covering how generative AI may infringe on copyright and whether that's fair use (short answer: maybe, maybe not). I have a new article up summarizing the report.
https://www.…

Copyright Office's report discusses whether generative AI use is fair use
Earlier this month, the Copyright Office issued the third part of its report on AI. As with the first two parts, the Copyright Office discusses arguments from people who responded to its call for comments and then provides its analysis. The three main sections in this part focus on infringement, fair use, and licensing regimes. Here, I’m going to cover what the report says about the first two items: whether using copyrighted material in generative AI infringes on owners’ rights and whether…

Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education
Prakash Shukla, Suchismita Naik, Ike Obi, Jessica Backus, Nancy Rasche, Paul Parson
https://arxiv.org/abs/2506.08467

Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education
The growing integration of AI tools in student design projects presents an unresolved challenge in HCI education: how should AI-generated content be cited and documented? Traditional citation frameworks -- grounded in credibility, retrievability, and authorship -- struggle to accommodate the dynamic and ephemeral nature of AI outputs. In this paper, we examine how undergraduate students in a UX design course approached AI usage and citation when given the freedom to integrate generative tools i…

💡NFDI4Culture schreibt für die Teilnahme an der Konferenz / Autumn School "Synthetic Imaginaries: The Cultural Politics of Generative AI” (8.– 12.09.2025 an der Universität Siegen) mehrere Reisestipendien für Nachwuchswissenschaftler:innen aus, um neue Forschungsperspektiven zum Thema Generative KI zu fördern und die interdisziplinäre Vernetzung in diesem Forschungsfeld zu unterstützen.
👉 Weitere Infos findet ihr hier:

Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
Neelesh Rampal, Peter B. Gibson, Steven C. Sherwood, Laura E. Queen, Hamish Lewis, Gab Abramowitz
https://arxiv.org/abs/2507.06527

Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
The computational cost of dynamical downscaling limits ensemble sizes in regional downscaling efforts. We present a newly developed generative-AI approach to greatly expand the scope of such downscaling, enabling fine-scale future changes to be characterised including rare extremes that cannot be addressed by traditional approaches. We test this approach for New Zealand, where strong regional effects are anticipated. At fine scales, the forced (predictable) component of precipitation and temper…

"You can tell what happened — Google promised iNaturalist free money if they would just do something, anything, that had some generative AI in it. iNaturalist forgot why people contribute at all, and took the cash."
(Original title: Google bribes iNaturalist to use generative AI — volunteers quit in outrage)

Google bribes iNaturalist to use generative AI — volunteers quit in outrage
iNaturalist is a website that crowdsources pictures of plants and animals to help identify species. Its tagline is “A Community for Naturalists.” iNaturalist is administered by its own small charit…

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation
Zonghan Wu, Junlin Wang, Congyuan Zou, Chenhan Wang, Yilei Shao
https://arxiv.org/abs/2506.07315

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation
Generative AI, particularly large language models (LLMs), is beginning to transform the financial industry by automating tasks and helping to make sense of complex financial information. One especially promising use case is the automatic creation of fundamental analysis reports, which are essential for making informed investment decisions, evaluating credit risks, guiding corporate mergers, etc. While LLMs attempt to generate these reports from a single prompt, the risks of inaccuracy are signi…

The Ethical Implications of AI in Creative Industries: A Focus on AI-Generated Art
Prerana Khatiwada, Joshua Washington, Tyler Walsh, Ahmed Saif Hamed, Lokesh Bhatta
https://arxiv.org/abs/2507.05549

The Ethical Implications of AI in Creative Industries: A Focus on AI-Generated Art
As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces…

Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
Fernando Ayach, Vitor Lameir\~ao, Raul Le\~ao, Jerfferson Felizardo, Rafael Sobrinho, Vanessa Borges, Patr\'icia Matsubara, Awdren Font\~ao
https://arxiv.org/abs/2507.08594

Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness,…

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI
Roser Batlle-Roca, Laura Ib\'a\~nez-Mart\'inez, Xavier Serra, Emilia G\'omez, Mart\'in Rocamora
https://arxiv.org/abs/2507.03599

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI
Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definiti…

Hollywood insiders report widespread off-the-books AI experimentation in studios, often hidden due to avoid backlash, despite union contracts limiting AI use (Lila Shapiro/Vulture)
https://www.vulture.com/article/generative-ai-hollywood-movies-tv.html…

Hollywood Already Uses Generative AI (And Is Hiding It)
With dozens of new studios and rapidly evolving tech, generative AI video is more embedded in Hollywood movies and TV than we may realize.

Accelerating Transposed Convolutions on FPGA-based Edge Devices
Jude Haris, Jos\'e Cano
https://arxiv.org/abs/2507.07683 https://…

Accelerating Transposed Convolutions on FPGA-based Edge Devices
Transposed Convolutions (TCONV) enable the up-scaling mechanism within generative Artificial Intelligence (AI) models. However, the predominant Input-Oriented Mapping (IOM) method for implementing TCONV has complex output mapping, overlapping sums, and ineffectual computations. These inefficiencies further exacerbate the performance bottleneck of TCONV and generative models on resource-constrained edge devices. To address this problem, in this paper we propose MM2IM, a hardware-software co-desi…

Position: We Need An Algorithmic Understanding of Generative AI
Oliver Eberle, Thomas McGee, Hamza Giaffar, Taylor Webb, Ida Momennejad
https://arxiv.org/abs/2507.07544

Position: We Need An Algorithmic Understanding of Generative AI
What algorithms do LLMs actually learn and use to solve problems? Studies addressing this question are sparse, as research priorities are focused on improving performance through scale, leaving a theoretical and empirical gap in understanding emergent algorithms. This position paper proposes AlgEval: a framework for systematic research into the algorithms that LLMs learn and use. AlgEval aims to uncover algorithmic primitives, reflected in latent representations, attention, and inference-time c…

Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
Bo Yang, Ruihuai Liang, Weixin Li, Han Wang, Xuelin Cao, Zhiwen Yu, Samson Lasaulce, M\'erouane Debbah, Mohamed-Slim Alouini, H. Vincent Poor, Chau Yuen
https://arxiv.org/abs/2507.01773

Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and…

A new wave of publishers sign up for ProRata's revenue share program, bringing the total number of publishers to 500, including The Boston Globe and Vox Media (Sara Guaglione/Digiday)
https://digiday.com/media/boston-globe-f…

Boston Globe, Future, Vox Media join ProRata’s generative AI licensing model
A fresh wave of publishers have signed up for ProRata’s revenue share program, and some are adding its AI search engine to their sites.

VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
Aadi Srivastava, Vignesh Natarajkumar, Utkarsh Bheemanaboyna, Devisree Akashapu, Nagraj Gaonkar, Archit Joshi
https://arxiv.org/abs/2507.05146

VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
The widespread and rapid adoption of AI-generated content, created by models such as Generative Adversarial Networks (GANs) and Diffusion Models, has revolutionized the digital media landscape by allowing efficient and creative content generation. However, these models also blur the difference between real images and AI-generated synthetic images, raising concerns regarding content authenticity and integrity. While many existing solutions to detect fake images focus solely on classification and…

I have many thoughts about generative AI, but the overarching one is this: the better metaphor is not a new brain acquiring human thoughts, but a complex search engine for existing human-generated ideas.

"it feels like the hope with generative AI is that no matter how much you may hate it, the goal is to bombard you with its horrors and its excesses so frequently that you become numb to it."
(Original title: I’m Getting Real Tired Of Not Being Able To Trust That A Video Game Doesn’t Have AI Crap In It)
https://

I'm Getting Real Tired Of Not Being Able To Trust That A Video Game Doesn't Have AI Crap In It - Aftermath
It sucks that players are having to scour every asset and line of dialogue

PenTest2.0: Towards Autonomous Privilege Escalation Using GenAI
Haitham S. Al-Sinani, Chris J. Mitchell
https://arxiv.org/abs/2507.06742 https://

PenTest2.0: Towards Autonomous Privilege Escalation Using GenAI
Ethical hacking today relies on highly skilled practitioners executing complex sequences of commands, which is inherently time-consuming, difficult to scale, and prone to human error. To help mitigate these limitations, we previously introduced 'PenTest++', an AI-augmented system combining automation with generative AI supporting ethical hacking workflows. However, a key limitation of PenTest++ was its lack of support for privilege escalation, a crucial element of ethical hacking. In this paper…

Integrating Generative AI in BIM Education: Insights from Classroom Implementation
Islem Sahraoui, Kinam Kim, Lu Gao, Zia Din, Ahmed Senouci
https://arxiv.org/abs/2507.05296

Integrating Generative AI in BIM Education: Insights from Classroom Implementation
This study evaluates the implementation of a Generative AI-powered rule checking workflow within a graduate-level Building Information Modeling (BIM) course at a U.S. university. Over two semesters, 55 students participated in a classroom-based pilot exploring the use of GenAI for BIM compliance tasks, an area with limited prior research. The instructional design included lectures on prompt engineering and AI-driven rule checking, followed by an assignment where students used a large language m…

This https://arxiv.org/abs/2505.21677 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

What happens when generative AI models train recursively on each others' generated outputs?
The internet is full of AI-generated content while also serving as a common source of training data for generative AI (genAI) models. This duality raises the possibility that future genAI models may be trained on other models' generated outputs. Prior work has studied consequences of models training on their own generated outputs, but limited work has considered what happens if models ingest content produced by other models. Given society's increasing dependence on genAI tools, understanding do…

Do Students Write Better Post-AI Support? Effects of Generative AI Literacy and Chatbot Interaction Strategies on Multimodal Academic Writing
Yueqiao Jin, Kaixun Yang, Roberto Martinez-Maldonado, Dragan Ga\v{s}evi\'c, Lixiang Yan
https://arxiv.org/abs/2507.04398

Do Students Write Better Post-AI Support? Effects of Generative AI Literacy and Chatbot Interaction Strategies on Multimodal Academic Writing
Academic writing increasingly involves multimodal tasks requiring students to integrate visual information and textual arguments. While generative AI (GenAI) tools, like ChatGPT, offer new pathways for supporting academic writing, little is known about how students' GenAI literacy influences their independent multimodal writing skills or how chatbot interaction strategies (passive reactive vs. proactive scaffolding) impact learning. This study examined 79 higher education students' multimodal a…

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Filing: Disney and NBCUniversal sue Midjourney in California, accusing it of direct and secondary copyright infringement; they say talks with Midjourney failed (Sara Fischer/Axios)
https://www.axios.com/2025/06/11/disney-nbcu-midjourney-copyright
…

Disney, NBCU sue Midjourney over copyright infringement
It's the first legal action from major Hollywood studios against a generative AI company.

Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis
Srihari K B, Pushpak Bhattacharyya
https://arxiv.org/abs/2507.06571

Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis
We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,000 recipes, 3,000 ingredients, 140,000 relations, and 14,000 images. We generate 40,000 QA pairs using 40 templates and LLaVA/DeepSeek augmentation. Joint fine-tuning of Meta LLaMA 3.1-8B and Stable Diffusion 3.5-Large improves BERTScore by 16.2\%, reduces FID by 37.8\%, and boosts CLIP alignment by 31.1\%. Diagnostic analyses-CLIP-based mismatch det…

Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions
Yuan Li, Teja Mandaloju, Haihua Chen
https://arxiv.org/abs/2507.07047

Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions
This study investigates public perceptions of generative artificial intelligence (GenAI) in libraries through a large-scale analysis of posts on X (formerly Twitter). Using a mixed-method approach that combines temporal trend analysis, sentiment classification, and social network analysis, this paper explores how public discourse around GenAI and libraries has evolved over time, the emotional tones that dominate the conversation, and the key users or organizations driving engagement. The findin…

Filing: Disney and NBCUniversal sue Midjourney in California, accusing it of direct and secondary copyright infringement; they say talks with Midjourney failed (Sara Fischer/Axios)
https://www.axios.com/2025/06/11/disney-nbcu-midjourney-copyright
…

Disney, NBCU sue Midjourney over copyright infringement
It's the first legal action from major Hollywood studios against a generative AI company.

AMC signs a deal with Runway to use its AI tech to generate marketing images and pre-visualize unproduced shows, the first cable player to work with Runway (Steven Zeitchik/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

AMC Networks Inks Deal With AI Company Runway (Exclusive)
The ‘Mad Men’ and 'The Walking Dead' platform is the latest Hollywood company to sign a pact with the generative AI firm.

How Clorox is using generative AI for ad creation, brainstorming new products, and analyzing consumer reviews, as part of a five-year, $580M digital overhaul (Christopher Mims/Wall Street Journal)
https://www.wsj.com/tech/ai/clorox-ai-hidd

Disclosing Generative AI Use in Digital Humanities Research
Rongqian Ma, Xuhan Zhang, Adrian Wisnicki
https://arxiv.org/abs/2507.03216 https://

Disclosing Generative AI Use in Digital Humanities Research
This survey study investigates how digital humanists perceive and approach generative AI disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through …

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
Anthony M. Barrett, Jessica Newman, Brandie Nonnecke, Nada Madkour, Dan Hendrycks, Evan R. Murphy, Krystal Jackson, Deepika Raman
https://arxiv.org/abs/2506.23949

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the umbrella term 'GPAI/foundation models' except where greater specificity is needed), can provide many beneficial capabilities but also risks of adverse events with profound consequences. This document provides risk-management practices or controls for identifying…

Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
Lorenzo Arboit, Dennis N. Schneider, Toby Collins, Daniel A. Hashimoto, Silvana Perretta, Bernard Dallemagne, Jacques Marescaux, EAES Working Group, Nicolas Padoy, Pietro Mascagni
https://arxiv.org/abs/2506.08258

Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expectations, and involvement with AI in surgery through comparative surveys conducted in 2021 and 2024. Two cross-sectional surveys were distributed globally in 2021 and 2024, the first before an IRCAD webinar and the second during the annual EAES meeting. The surveys assessed demographics, AI awareness, expectations, inv…

This https://arxiv.org/abs/2404.03108 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Writing with AI Lowers Psychological Ownership, but Longer Prompts Can Help
The feeling of something belonging to someone is called "psychological ownership." A common assumption is that writing with generative AI lowers psychological ownership, but the extent to which this occurs and the role of prompt length are unclear. We report on two experiments to examine the relationship between psychological ownership and prompt length. Participants wrote short stories either completely by themselves or wrote prompts of varying lengths. Results show that when participants wrot…

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Aniket Kesari, Travis Breaux, Tom Norton, Sarah Santos, Anmol Singhal
https://arxiv.org/abs/2507.04185

From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law
Privacy law and regulation have turned to "consent" as the legitimate basis for collecting and processing individuals' data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large…

Amazon says over 1M people now have access to Alexa , up from "hundreds of thousands" in May, and plans to "make it even more broadly available over the summer" (Jennifer Pattison Tuohy/The Verge)
https://www.theverge.com/news/684560/amazo

More than a million people now have Alexa Plus
Amazon’s generative AI-powered smart voice assistant launched in March and is slowly reaching more homes.

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Learning Traffic Anomalies from Generative Models on Real-Time Observations
Fotis I. Giasemis, Alexandros Sopasakis

Challenges for AI in Multimodal STEM Assessments: a Human-AI Comparison
Aymeric de Chillaz, Anna Sotnikova, Patrick Jermann, Antoine Bosselut
https://arxiv.org/abs/2507.03013

Challenges for AI in Multimodal STEM Assessments: a Human-AI Comparison
Generative AI systems have rapidly advanced, with multimodal input capabilities enabling reasoning beyond text-based tasks. In education, these advancements could influence assessment design and question answering, presenting both opportunities and challenges. To investigate these effects, we introduce a high-quality dataset of 201 university-level STEM questions, manually annotated with features such as image type, role, problem complexity, and question format. Our study analyzes how these fea…

AMC signs a deal with Runway to use its tech to generate marketing images and pre-visualize unproduced shows, the first cable player to work with Runway (Steven Zeitchik/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

AMC Networks Inks Deal With AI Company Runway (Exclusive)
The ‘Mad Men’ and 'The Walking Dead' platform is the latest Hollywood company to sign a pact with the generative AI firm.

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
Kimberly Le Truong, Annette Zimmermann, Hoda Heidari
https://arxiv.org/abs/2507.04641

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
Disparities in the societal harms and impacts of Generative AI (GenAI) systems highlight the critical need for effective unfairness measurement approaches. While numerous benchmarks exist, designing valid measurements requires proper systematization of the unfairness construct. Yet this process is often neglected, resulting in metrics that may mischaracterize unfairness by overlooking contextual nuances, thereby compromising the validity of the resulting measurements. Building on established (u…

Reviewriter: AI-Generated Instructions For Peer Review Writing
Xiaotian Su, Thiemo Wambsganss, Roman Rietsche, Seyed Parsa Neshaei, Tanja K\"aser
https://arxiv.org/abs/2506.04423

Reviewriter: AI-Generated Instructions For Peer Review Writing
Large Language Models (LLMs) offer novel opportunities for educational applications that have the potential to transform traditional learning for students. Despite AI-enhanced applications having the potential to provide personalized learning experiences, more studies are needed on the design of generative AI systems and evidence for using them in real educational settings. In this paper, we design, implement and evaluate \texttt{Reviewriter}, a novel tool to provide students with AI-generated …

A Reference Architecture for Gamified Cultural Heritage Applications Leveraging Generative AI and Augmented Reality
Federico Martusciello, Henry Muccini, Antonio Bucchiarone
https://arxiv.org/abs/2506.04090

A Reference Architecture for Gamified Cultural Heritage Applications Leveraging Generative AI and Augmented Reality
The rapid advancement of Information and Communication Technologies is transforming Cultural Heritage access, experience, and preservation. However, many digital heritage applications lack interactivity, personalization, and adaptability, limiting user engagement and educational impact. This short paper presents a reference architecture for gamified cultural heritage applications leveraging generative AI and augmented reality. Gamification enhances motivation, artificial intelligence enables ad…

This https://arxiv.org/abs/2502.00561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as "a tangle of sloppy tests [and] apples-to-oranges comparisons" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social…

A US judge rules that Meta's use of books to train AI is protected by fair use but says his opinion is more a reflection of the plaintiffs' poor arguments (Isaiah Poritz/Bloomberg Law)
https://news.bloomberglaw.com/legal-op

Meta Beats Authors’ Copyright Suit Over AI Training on Books (2)
Meta Platforms Inc. escaped a first-of-its-kind copyright lawsuit from a group of authors who alleged the tech giant hoovered up millions of copyrighted books without permission to train its generative AI model called Llama.

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new/
[1/1]:
Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assis...

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust
Claudiu Popa, Rex Pallath, Liam Cunningham, Hewad Tahiri, Abiram Kesavarajah, Tao Wu
https://arxiv.org/abs/2506.07363

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust
Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, a…

Computer science programs at Carnegie Mellon and other US universities are rethinking how to incorporate AI into curricula, including a focus on AI literacy (Steve Lohr/New York Times)
https://www.nytimes.com/2025/06/30/technology/computer-science-…

How Do You Teach Computer Science in the A.I. Era?
Universities across the country are scrambling to understand the implications of generative A.I.’s transformation of technology.

Nvidia hits a $4T market cap in intraday trading, the first company to do so, after passing a $2T market cap in February 2024 and $3T in June 2025 (CNBC)
https://www.cnbc.com/2025/07/09/nvidia-4-trillion.html

Nvidia hits $4 trillion market cap, first company to do so
Nvidia topped a $4 trillion market cap for the first time as investors scooped stock in the tech giant building the hardware for the generative AI boom.

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
Mohammad Saleh Torkestani, Taha Mansouri
https://arxiv.org/abs/2506.02046 https:…

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment through a machine-versus-machine approach. Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content, traditional assessment methods face an existential threat, with surveys indicating 74-92% of students experimenting with these tools for academic purposes. Current respon…

A US judge rules that Meta's use of books to train AI is protected by fair use but says his opinion is more a reflection of the plaintiffs' poor arguments (Isaiah Poritz/Bloomberg Law)
https://news.bloomberglaw.com/legal-op

Meta Beats Copyright Suit From Authors Over AI Training on Books
Meta Platforms Inc. escaped a first-of-its-kind copyright lawsuit from a group of authors who alleged the tech giant hoovered up millions of copyrighted books without permission to train its generative AI model called Llama.

Generative AI for Multiple Choice STEM Assessments
Christina Perdikoulias, Chad Vance, Stephen M. Watt
https://arxiv.org/abs/2506.02094 https://

Generative AI for Multiple Choice STEM Assessments
Artificial intelligence technology enables a range of enhancements in computer-aided instruction, from accelerating the creation of teaching materials to customizing learning paths based on learner outcomes. However, ensuring the mathematical accuracy and semantic integrity of generative AI output remains a significant challenge, particularly in STEM disciplines. In this study, we explore the use of generative AI in which "hallucinations" -- typically viewed as undesirable inaccuracies -- can i…

Daydream, which raised a $50M seed in June 2024 to build a generative AI shopping agent for fashion, launches in beta, with an app expected later this summer (Hilary Milnes/Vogue Business)
https://www.voguebusiness.com/story/techno

Is Daydream’s AI platform the answer to fashion’s discovery problem?
Founder Julie Bornstein raised $50 million last year to create the ultimate AI shopping experience. Today, it launches in beta.

AI's real threat to education isn't that it encourages students to cheat, but that it short-circuits the learning processes needed to use generative AI adeptly (Nicholas Carr/New Cartographies)
https://www.newcartographies.com/p/the-myth-of-automated-lear…

The Myth of Automated Learning
AI's real threat to education.

Computer Science Education in the Age of Generative AI
Russell Beale
https://arxiv.org/abs/2507.02183 https://arxiv.org/pdf/2507.0218…

Computer Science Education in the Age of Generative AI
Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highl…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study
Yutan Huang, Chetan Arora, Wen Cheng Houng, Tanjila Kanij, Anuradha Madulgalla, John Grundy

Adobe releases iOS and Android apps for Firefly, letting users generate images and videos using text prompts and experiment with popular AI photo editing tools (Jess Weatherbed/The Verge)
https://www.theverge.com/news/688080/adobe-firefly-…

Adobe made a mobile app for its Firefly generative AI tools
The all-in-one platform for Adobe’s Firefly generative AI models is now available on iPhone and Android devices.

Adultification Bias in LLMs and Text-to-Image Models
Jane Castleman, Aleksandra Korolova
https://arxiv.org/abs/2506.07282 https://arx…

Adultification Bias in LLMs and Text-to-Image Models
The rapid adoption of generative AI models in domains such as education, policing, and social media raises significant concerns about potential bias and safety issues, particularly along protected attributes, such as race and gender, and when interacting with minors. Given the urgency of facilitating safe interactions with AI systems, we study bias along axes of race and gender in young girls. More specifically, we focus on "adultification bias," a phenomenon in which Black girls are presumed t…