Tootfinder

Happening now: Cambridge University REAL Centre 10th Anniversary Conference online.
Centre Director, Prof Pauline Rose, gives a warm welcome speaking about friendship as the starting point for rigorous research in education and development, building connections, inclusivity, and warmth.
https://www.educ.c…

This https://arxiv.org/abs/2505.04104 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Position: We Need Responsible, Application-Driven (RAD) AI Research
This position paper argues that achieving meaningful scientific and societal advances with artificial intelligence (AI) requires a responsible, application-driven approach (RAD) to AI research. As AI is increasingly integrated into society, AI researchers must engage with the specific contexts where AI is being applied. This includes being responsive to ethical and legal considerations, technical and societal constraints, and public discourse. We present the case for RAD-AI to drive research th…

Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
Mihaela van der Schaar, Richard Peck, Eoin McKinney, Jim Weatherall, Stuart Bailey, Justine Rochon, Chris Anagnostopoulos, Pierre Marquet, Anthony Wood, Nicky Best, Harry Amad, Julianna Piskorz, Krzysztof Kacprzyk, Rafik Salama, Christina Gunther, Francesca Frau, Antoine Pugeat, Ramon Hernandez

Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation
This manifesto represents a collaborative vision forged by leaders in pharmaceuticals, consulting firms, clinical research, and AI. It outlines a roadmap for two AI technologies - causal inference and digital twins - to transform clinical trials, delivering faster, safer, and more personalized outcomes for patients. By focusing on actionable integration within existing regulatory frameworks, we propose a way forward to revolutionize clinical research and redefine the gold standard for clinical …

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…

USC professor Allison Marsh writes this delightful short article for IEEE Spectrum about Marvin Minsky and Seymour Papert's British counterparts in Cambridge and how, in their case, a document (the Lighthill Report) precipitated cuts in British AI research funding in ways similar to how funding was temporarily cut in the U.S. following MIT's publishing of Minsky and Papert's "Perceptrons" in 1969.
"Freddy the Robot Was the Fall Guy for British AI"

Freddy the Robot Was the Fall Guy for British AI

Its creator lost a power struggle that led to an AI winter in the 1970s

GPS Spoofing Attacks on AI-based Navigation Systems with Obstacle Avoidance in UAV
Ji Hyuk Jung, Mi Yeon Hong, Ji Won Yoon
https://arxiv.org/abs/2506.08445

GPS Spoofing Attacks on AI-based Navigation Systems with Obstacle Avoidance in UAV
Recently, approaches using Deep Reinforcement Learning (DRL) have been proposed to solve UAV navigation systems in complex and unknown environments. However, despite extensive research and attention, systematic studies on various security aspects have not yet been conducted. Therefore, in this paper, we conduct research on security vulnerabilities in DRL-based navigation systems, particularly focusing on GPS spoofing attacks against the system. Many recent basic DRL-based navigation systems fun…

AI research nonprofit EleutherAI releases the Common Pile v0.1, an 8TB dataset of licensed and open-domain text for AI models that it says is one of the largest (Kyle Wiggers/TechCrunch)
https://techcrunch.com/2025/06/06/eleu

EleutherAI releases massive AI training dataset of licensed and open domain text | TechCrunch
EleutherAI, an AI research organization, has released what it's claiming is one of the largest collections of licensed and open-domain text for training AI models.

Probing Experts' Perspectives on AI-Assisted Public Speaking Training
Nesrine Fourati, Alisa Barkar, Marion Drag\'ee, Liv Danthon-Lefebvre, Mathieu Chollet
https://arxiv.org/abs/2507.07930

Probing Experts' Perspectives on AI-Assisted Public Speaking Training
Background: Public speaking is a vital professional skill, yet it remains a source of significant anxiety for many individuals. Traditional training relies heavily on expert coaching, but recent advances in AI has led to novel types of commercial automated public speaking feedback tools. However, most research has focused on prototypes rather than commercial applications, and little is known about how public speaking experts perceive these tools. Objectives: This study aims to evaluate expert…

Meta is preparing to unveil a new artificial intelligence research lab
dedicated to pursuing “superintelligence,”
a hypothetical A.I. system that exceeds the powers of the human brain,
as the tech giant jockeys to stay competitive in the technology race,
according to four people with the knowledge of the company’s plans.
Meta has tapped Alexandr Wang, 28,
the founder and chief executive of the A.I. start-up Scale AI,
to join the new lab, the people s…

Meta Is Creating a New A.I. Lab to Pursue ‘Superintelligence’
The new lab, set to include the Scale AI founder Alexandr Wang, is part of a reorganization of Meta’s artificial intelligence efforts under Mark Zuckerberg.

🌟 Real-World Impact
Companies are already seeing incredible results: • Goldman Sachs piloting autonomous AI coders • Developer tools integrating for enhanced productivity • Research institutions building custom agentic systems • Startups leveraging cost-effective AI infrastructure 🏢

Sadly, @… is right — and the article she quotes is (I think) wrong when it says this:
❝In some real sense Meta's future depends on hiring and retaining those people, and Meta has already committed $65 billion to Al infrastructure this year. What's a few hundred million more?❞
Meta’s future does not in fact depend on hiring these specific researchers. This isn’t for the researchers, and it’s not for the research either. It’s for the investors. It’s making a show of being willing to being even more preposterously all-in on AI than anyone else, because investors are gaga for that shit. https://mas.to/@kims/114666065511798564

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.

Exactly 20 years ago I published a blog post (which I've edited to adapt to my most recent incarnation of a blog) , with a comparison of tools and frameworks for quite a few technology stacks: Java, .NET, PHP, etc.
This was right at the very early stages of the Cloud, there was no "Big Data", there was certainly no AI, there was no iPhone or Android, Mac OS X releases were still big cats, Ubuntu had just appeared, and social media was just Orkut or MySpace. Enjoy.

State of the Art
This article is a copy of a research work I did today, to draw a map of today’s development technologies.

Create documentation from a git repo, then have a conversation with the repo. Amazing. #ai
https://deepwiki.com/

"Practical changes could reduce AI energy demand by up to 90%"
#AI #ArtificialIntelligence #Technology #Energy

Practical changes could reduce AI energy demand by up to 90%
Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research from UCL published in a new UNESCO report.

Understanding Japan’s AI Promotion Act: An “Innovation-First” Blueprint for AI Regulation
https://fpf.org/blog/understanding-japans-ai-promotion-act-an-innovation-first-blueprint-for-ai-regulation/

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Shuo Jiang, Min Xie, Frank Youhua Chen, Jian Ma, Jianxi Luo
https://arxiv.org/abs/2506.09755

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Research and practice in Intelligent Design (ID) have significantly enhanced engineering innovation, efficiency, quality, and productivity over recent decades, fundamentally reshaping how engineering designers think, behave, and interact with design processes. The recent emergence of Foundation Models (FMs), particularly Large Language Models (LLMs), has demonstrated general knowledge-based reasoning capabilities, and open new paths and avenues for further transformation in engineering design. …

SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
Ahmed Adel Attia, Jing Liu, Carl Espy-Wilson
https://arxiv.org/abs/2506.09206

SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Public classroom datasets remain limited, and the lack of a dedicated classroom noise corpus prevents the use of standard data augmentation techniques. In this paper, we introduce a scalable methodology for synthesizing classroom noise using game engines, a framework that extends to other domains. Using this methodology, we present SimClass, a dataset that includes both a s…

This https://arxiv.org/abs/2506.03988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinformation, the detection of AI-generated images is a pressing challenge and an active research topic. While many of the presented methods claim to achieve high detection accuracy, they are usually evaluated under idealized conditions. In particular, the adversarial robustness is often neglected, potentially due to a la…

Remember when we compared the energy consumption of bitcoin to a country? And that was a bad thing remember, so we didn’t buy any nor program with it.
> According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.
https://www.…

"Comparative review on Primo Research Assistant, Scopus AI, Web of Science Research Assistant and a explainer for AI search for librarians" by Aaron Tay: https://musingsaboutlibrarianship.blogspot.com/2025/05/comparative-review-…

From Anxiety to Alzheimer’s, Music Makes a Difference https://neurosciencenews.com/alzheimers-anxiety-music-28655/?utm_source=dlvr.it&utm_content=NeuroscienceNew%…

From Anxiety to Alzheimer’s, Music Makes a Difference - Neuroscience News
Neuroscience News provides research news for neuroscience, neurology, psychology, AI, brain science, mental health, robotics and cognitive sciences.

Building on the 90s, statistical n-gram language models, trained on vast text collections, became the backbone of NLP research. They fueled advancements in nearly all NLP techniques of the era, laying the groundwork for today's AI.
F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA
#NLP

Terabyte-Scale Analytics in the Blink of an Eye
Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
https://arxiv.org/abs/2506.09226 https://

Terabyte-Scale Analytics in the Blink of an Eye
For the past two decades, the DB community has devoted substantial research to take advantage of cheap clusters of machines for distributed data analytics -- we believe that we are at the beginning of a paradigm shift. The scaling laws and popularity of AI models lead to the deployment of incredibly powerful GPU clusters in commercial data centers. Compared to CPU-only solutions, these clusters deliver impressive improvements in per-node compute, memory bandwidth, and inter-node interconnect pe…

Enhancing Quantum Software Development Process with Experiment Tracking
Mahee Gamage, Otso Kinanen, Jake Muff, Vlad Stirbu
https://arxiv.org/abs/2507.06990

Enhancing Quantum Software Development Process with Experiment Tracking
As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development pr…

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Shuvashis Sarker
https://arxiv.org/abs/2506.07228

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to ta…

Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
Tarek Gasmi, Ramzi Guesmi, Ines Belhadj, Jihene Bennaceur
https://arxiv.org/abs/2507.06323

Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
Large Language Model (LLM) agents face security vulnerabilities spanning AI-specific and traditional software domains, yet current research addresses these separately. This study bridges this gap through comparative evaluation of Function Calling architecture and Model Context Protocol (MCP) deployment paradigms using a unified threat classification framework. We tested 3,250 attack scenarios across seven language models, evaluating simple, composed, and chained attacks targeting both AI-specif…

Lovely to give the keynote 'On Epistemic Humility: storytelling, positionalities, and just survival in/through research', for the Higher Degree Research Forum, University of South Australia.
I draw on post-colonial, Black, and Indigenous scholarship to problematise what we know and how we know.
#HigherEducation

Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
Zhijian Xu, Yilun Zhao, Manasi Patwardhan, Lovekesh Vig, Arman Cohan
https://arxiv.org/abs/2507.02694

Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers
Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. We first present a comprehensive taxonomy of limitation types in scientific research, with a focus on AI. Guided by this taxonomy, for studying limitations, we present LimitGe…

Nvidia CEO Jensen Huang warns the UK lacks the digital infrastructure needed to capitalize on AI despite "incredible" research talent, as PM Starmer pledges £1B (Tim Bradshaw/Financial Times)
https://www.ft.com/content/cc04adfb-81b2-477f-b85c-ce042e8f83a8…

Nvidia chief says UK lacks digital infrastructure as Keir Starmer pledges £1bn for AI
Jensen Huang seeks ‘sovereign’ artificial intelligence deals that diversify US chip giant’s business away from Big Tech groups

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng, Hubert P. H. Shum
https://arxiv.org/abs/2507.07134

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performa…

One way to ensure I don't buy an item is to market it "as seen on Shark Tank" -- but that's probably just me. Another way to ensure I don't buy an item is to market it as having AI features (??), and apparently that's widespread.
https://futurism.com/customers-see-ai-…

Something Hilarious Happens When Potential Customers See That a Product Has AI Features
New research explores how customers react to products that are marketed as having AI — with striking results.

There’s more examples but it seems that AI research attracts people with a bias towards a reactionary worldview.

I shared insights from research on AI's impact on undergrad creators by my collaborators Greg Nelson and Troy Schotter in an interview with Devin Daigneault for FoxABC Maine about the upcoming #MaineAIconf Friday 13 June.

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 …

AffectMachine-Pop: A controllable expert system for real-time pop music generation
Kat R. Agres, Adyasha Dash, Phoebe Chua, Stefan K. Ehrlich
https://arxiv.org/abs/2506.08200

AffectMachine-Pop: A controllable expert system for real-time pop music generation
Music is a powerful medium for influencing listeners' emotional states, and this capacity has driven a surge of research interest in AI-based affective music generation in recent years. Many existing systems, however, are a black box which are not directly controllable, thus making these systems less flexible and adaptive to users. We present \textit{AffectMachine-Pop}, an expert system capable of generating retro-pop music according to arousal and valence values, which can either be pre-determ…

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Lu\'is Cruz, Jo\~ao Paulo Fernandes, Maja H. Kirkeby, Silverio Mart\'inez-Fern\'andez, June Sallou, Hina Anwar, Enrique Barba Roque, Justus Bogner, Joel Casta\~no, Fernando Castor, Aadil Chasmawala, Sim\~ao Cunha, Daniel Feitosa, Alexandra Gonz\'alez, Andreas Jedlitschka, Patricia Lago, Ana Oprescu, Pooja Rani, Jo\~ao Saraiva, Federica Sarro, Raghavendra…

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The "Greening AI with Software Engineering" CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Européen de Calcul Atomique et Moléculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current resul…

FYI, some private foundations have stepped up to provide grants for #STEM #Education and #Research, as a stop gap for those whose federal grants have been terminated:

Interesting article on Cornell research:
"AI suggestions make writing more generic, Western"
https://news.cornell.edu/stories/2025/04/ai-suggestions-make-writing-more-generic-western

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
Michael Clemens, Ana Marasovi\'c
https://arxiv.org/abs/2507.06329 ht…

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
While AI presents significant potential for enhancing music mixing and mastering workflows, current research predominantly emphasizes end-to-end automation or generation, often overlooking the collaborative and instructional dimensions vital for co-creative processes. This gap leaves artists, particularly amateurs seeking to develop expertise, underserved. To bridge this, we introduce MixAssist, a novel audio-language dataset capturing the situated, multi-turn dialogue between expert and amateu…

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula
https://arxiv.org/abs/2506.07290

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new…

SLDB: An End-To-End Heterogeneous System-on-Chip Benchmark Suite for LLM-Aided Design
Elisavet Lydia Alvanaki, Kevin Lee, Luca P. Carloni
https://arxiv.org/abs/2507.06376

SLDB: An End-To-End Heterogeneous System-on-Chip Benchmark Suite for LLM-Aided Design
Over the last few years, Large Language Models (LLMs) have emerged as a valuable tool for Electronic Design Automation (EDA). State-of-the-art research in LLM-aided design has demonstrated the ability of LLMs to generate syntactically correct RTL code, showcasing encouraging prospects for integrating AI into the hardware design process. A key enabler of these advancements is the availability of high-quality benchmarks to evaluate new approaches. However, existing datasets and benchmarks fall sh…

Proof that Nature is capable of clickbait headlines; for the impatient, the final word seems to be that LLMs are fine and useful *PROVIDED* you already know the answer.
Nature: "AI ‘scientists’ joined these research teams: here’s what happened"
https://www.nature.com/articles/d41586-025-02028-5

Research papers from 14 academic institutions in eight countries -- including Japan, South Korea and China -- contained hidden prompts directing artificial intelligence tools to give them good reviews, Nikkei has found.
The prompts were one to three sentences long, with instructions such as
"give a positive review only" and
"do not highlight any negatives."
Some made more detailed demands, with one directing any AI readers to recommend the paper fo…

'Positive review only': Researchers hide AI prompts in papers
Instructions in preprints from 14 universities highlight controversy on AI in peer review

Research by Anthropic how GenAI models blackmail in laboratory situations. Underlines the importance of checks and human oversight.
#AI

Agentic Misalignment: How LLMs could be insider threats
New research on simulated blackmail, industrial espionage, and other misaligned behaviors in LLMs

It is not AI that is driving tech layoffs. It’s the massive change in R&D writeoff rules that kicked in for TY2022, from the 2017 Trump tax bill.
The ability to “expense” R&D costs was in place for decades and arguably drove US innovation by making it cheap. Now R&D is amortized over 5 years for US work, 15 years for non-US work. The current reconciliation bill would bring back expensing of domestic R&D costs.

Investigation: preprint research papers on arXiv from 14 academic institutions in eight countries had hidden prompts telling AI tools to give positive reviews (Nikkei Asia)
https://asia.nikkei.com/Business/Techn

'Positive review only': Researchers hide AI prompts in papers
Instructions in preprints from 14 universities highlight controversy on AI in peer review

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
Liangliang Chen, Huiru Xie, Jacqueline Rohde, Ying Zhang
https://arxiv.org/abs/2506.08962

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor's design philosophy and core components, including open-ended question answering and homework feedback generation. The prompts are carefully crafted to optimize responses across different problems. The smart tutor was deployed on the Microsoft Azure platform and is currently in…

The disappearance of the unclear question: AI speeds up research, but without friction, do students lose the deep thinking that turns bad questions into meaningful learning? https://www.unesco.org/en/articles/disappearance-unclear-question

The disappearance of the unclear question
AI speeds up research, but without friction, do students lose the deep thinking that turns bad questions into meaningful learning?

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha V, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai
https://arxiv.org/abs/2506.06099…

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively cu…

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
Kemboi Cheruiyot, Nickson Kiprotich, Vyacheslav Kungurtsev, Kennedy Mugo, Vivian Mwirigi, Marvin Ngesa
https://arxiv.org/abs/2507.06278

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three…

According to the folks at Messente, here are the top AI-crackable four digit PINs.
https://messente.com/blog/most-vulnerable-pins

It Takes 0.44 Seconds to Crack These PINs - Are You Using One?
The security level of 4-digit code remains uncertain. Our research found that it takes 0.44 seconds for AI to crack your PIN.

Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
Zhihao Wang, Alessandro Cornacchia, Franco Galante, Carlo Centofanti, Alessio Sacco, Dingde Jiang
https://arxiv.org/abs/2507.01997

Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting
Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthesis and automating network diagnosis tasks, among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effo…

✨ Spring SIGs wrapped! ✨
From Prague to Paris to Ispra, 4 GÉANT Special Interest Groups (SIGs) met to explore AI, Time & Frequency networks, service strategy development, and network operations.
Spring days packed with great ideas, useful insights, new collaborations, and our beloved Lightning Talks.
👏 Big thanks to CESNET, RENATER, EC JRC & all who joined in-person and online.
Catch up on all the highlights and takeaways here 👉

GÉANT Community Programme | GÉANT CONNECT Online
The GÉANT community programme supports research and education through the activities of Special Interest Groups and Task Forces and connects academia, the business community and governmental organisations.

Another one of the disgruntled OG AI computer scientists was Karl Steinbuch, who made one of the first implementations of a neural network.
He was also an ardent Nazi and directly ordered war crimes in the Second World War.
After his research stalled he wrote many racist, misogynist and xenophobic articles for the rest of his life.
He won many awards.
https://en.wikipedia.org/wiki/Karl_Steinbuch

This https://arxiv.org/abs/2505.24785 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

EXP-Bench: Can AI Conduct AI Research Experiments?
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research question and incomplete starter code, EXP-Bench challenges AI agents to formulate hypotheses, design and implement experimental procedures, …

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…

Andy Konwinski, co-founder of Databricks and Perplexity, launches the nonprofit Laude Institute, self-funded with $100M for grants to AI projects and labs (Mike Wheatley/SiliconANGLE)
https://siliconangle.com/2025/06/2…

Andy Konwinski, co-founder of Databricks and Perplexity AI, launches $100M research institute focused on beneficial AI - SiliconANGLE
Andy Konwinski, co-founder of Databricks and Perplexity AI, launches $100M research institute focused on beneficial AI - SiliconANGLE

The FOMO about the 10-year University of Cambridge REAL Centre Anniversary Celebration Conference is *real*.
If you can't be there in person, join online.
Incredible lineup.
12 June 2025
https://www.eventbrite.co…

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…

AI4Research: A Survey of Artificial Intelligence for Scientific Research
Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen, Wanxiang Che
https://arxiv.org/abs/2507.01903

AI4Research: A Survey of Artificial Intelligence for Scientific Research
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct rese…

Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
Julia Barnett, Kimon Kieslich, Jasmine Sinchai, Nicholas Diakopoulos
https://arxiv.org/abs/2506.05605

Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because c…

ChatGPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, Llama 4, and Copilot comparison: Claude was the best overall with the highest consistency and no hallucinations (Geoffrey A. Fowler/Washington Post)
https://www.washingtonpost.com/technology/

Review | 5 AI bots took our tough reading test. One was smartest — and it wasn’t ChatGPT.
We challenged AI helpers to decode legal contracts, simplify medical research, speed-read a novel and make sense of Trump speeches. Some of the AI analysis was impressive — and some was downright dumb.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
Hongzhou Rao, Yanjie Zhao, Xinyi Hou, Shenao Wang, Haoyu Wang
https://arxiv.org/abs/2506.23762

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status …

Predicting Empirical AI Research Outcomes with Language Models
Jiaxin Wen, Chenglei Si, Yueh-han Chen, He He, Shi Feng
https://arxiv.org/abs/2506.00794 htt…

Predicting Empirical AI Research Outcomes with Language Models
Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better…

Beware! The AI Act Can Also Apply to Your AI Research Practices
Alina Wernick, Kristof Meding
https://arxiv.org/abs/2506.03218 https://

Beware! The AI Act Can Also Apply to Your AI Research Practices
The EU has become one of the vanguards in regulating the digital age. A particularly important regulation in the Artificial Intelligence (AI) domain is the EU AI Act, which entered into force in 2024. The AI Act specifies -- due to a risk-based approach -- various obligations for providers of AI systems. These obligations, for example, include a cascade of documentation and compliance measures, which represent a potential obstacle to science. But do these obligations also apply to AI researcher…

Quanta Magazine authors Janna Levin and Steven Strogatz strike up a conversation with Ellie Pavlick (Research Scientist at Google Deep Mind) about the differences and similarities between the way people understand language, what NLP algorithms do, and the fact that such conversations more often than not shed light into more than Linguistics' computational side.
"Will AI Ever Understand Language Like Humans?"

Will AI Ever Understand Language Like Humans? | Quanta Magazine
AI may sound like a human, but that doesn’t mean that AI learns like a human. In this episode, Ellie Pavlick explains why understanding how LLMs can process language could unlock deeper insights into both AI and the human mind.

This https://arxiv.org/abs/2405.20771 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Towards Black-Box Membership Inference Attack for Diffusion Models
Given the rising popularity of AI-generated art and the associated copyright concerns, identifying whether an artwork was used to train a diffusion model is an important research topic. The work approaches this problem from the membership inference attack (MIA) perspective. We first identify the limitation of applying existing MIA methods for proprietary diffusion models: the required access of internal U-nets. To address the above problem, we introduce a novel membership inference attack metho…

A look at DeepSeek's impact on the AI model race and market share landscape, roughly 150 days after DeepSeek R1 shook stock markets and the Western AI world (SemiAnalysis)
https://semianalysis.com/2025/07/03/deepseek-debrief-128-days-later/

DeepSeek Debrief: >128 Days Later 
SemiAnalysis is hiring an analyst in New York City for Core Research, our world class research product for the finance industry. Please apply here It’s been a bit over 150 days since the launc…

Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation
Haris Khan, Shumaila Asif, Hassan Nasir
https://arxiv.org/abs/2507.07043

Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation
The integration of artificial intelligence into hearing assistance marks a paradigm shift from traditional amplification-based systems to intelligent, context-aware audio processing. This systematic literature review evaluates advances in AI-driven selective noise cancellation (SNC) for hearing aids, highlighting technological evolution, implementation challenges, and future research directions. We synthesize findings across deep learning architectures, hardware deployment strategies, clinical …

"Tech giants lag on AI emissions disclosures, research finds"
#AI #ArtificialIntelligence #Climate #ClimateChange

This https://arxiv.org/abs/2506.05376 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Red Teaming Roadmap Towards System-Level Safety
Large Language Model (LLM) safeguards, which implement request refusals, have become a widely adopted mitigation strategy against misuse. At the intersection of adversarial machine learning and AI safety, safeguard red teaming has effectively identified critical vulnerabilities in state-of-the-art refusal-trained LLMs. However, in our view the many conference submissions on LLM red teaming do not, in aggregate, prioritize the right research problems. First, testing against clear product safety …

An analysis of 15M biomedical abstracts from 2010 to 2024 finds researchers using AI to write abstracts use certain words far more often than those who don't (Gina Kolata/New York Times)
https://www.nytimes.com/2025/…

454 Hints That a Chatbot Wrote Part of a Biomedical Researcher’s Paper
Scientists show that the frequency of a set of words seems to have increased in published study abstracts since ChatGPT was released into the world.

Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
Huiqian Lai
https://arxiv.org/abs/2506.05514 https://

Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates u…

This https://arxiv.org/abs/2504.14522 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Biased by Design: Leveraging AI Inherent Biases to Enhance Critical Thinking of News Readers
This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical thinking in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bia…

The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, S\"oren Mindermann, Vanessa Wilfred, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Sim\'eon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnes Delaborde, Nouha Dziri, Francisco Eiras, J…

The Singapore Consensus on Global AI Safety Research Priorities
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space…

Source: Mark Zuckerberg has, on 10 occasions, offered to pay AI research talent up to $300M over four years, with $100M in compensation for the first year (Zoë Schiffer/Wired)
https://www.wired.com/story/mark-zuckerberg-meta-offer-top-ai-talent-…

Here’s What Mark Zuckerberg Is Offering Top AI Talent
The Meta CEO is leading a hiring blitz, offering top talent at OpenAI eye-watering pay packages and endless access to cutting-edge chips.

At its Discover 2025 conference, HPE unveiled HPE CloudOps Software, GreenLake Intelligence, which can deploy AI agents across its hybrid cloud stack, and more (Larry Dignan/Constellation Research)
https://www.constellationr.com/…

HPE launches GreenLake Intelligence, adds AI agents throughout hybrid cloud stack
HPE launched new CloudOps Software, layered AI agents throughout its hybrid cloud stack, unveiled GreenLake Intelligence and expanded its AI factory. In addition, Digital Realty said it would standardize its data center footprint on HPE. At its flagship Discover 2025 conference, HPE showcased its new branding as well as play for AI workloads. The big picture from HPE is that hybrid IT operations, networking, storage and AI operations can be largely turnkey via AI agents.

An AI-native experimental laboratory for autonomous biomolecular engineering
Mingyu Wu, Zhaoguo Wang, Jiabin Wang, Zhiyuan Dong, Jingkai Yang, Qingting Li, Tianyu Huang, Lei Zhao, Mingqiang Li, Fei Wang, Chunhai Fan, Haibo Chen
https://arxiv.org/abs/2507.02379

An AI-native experimental laboratory for autonomous biomolecular engineering
Autonomous scientific research, capable of independently conducting complex experiments and serving non-specialists, represents a long-held aspiration. Achieving it requires a fundamental paradigm shift driven by artificial intelligence (AI). While autonomous experimental systems are emerging, they remain confined to areas featuring singular objectives and well-defined, simple experimental workflows, such as chemical synthesis and catalysis. We present an AI-native autonomous laboratory, target…

Google announces 30 Gemini AI tools for educators, like "Gems" which help students on specific subjects, included with Google Workspace for Education accounts (Sarah Perez/TechCrunch)
https://techcrunch.com/2025…

Google embraces AI in the classroom with new Gemini tools for educators, chatbots for students, and more | TechCrunch
Google will give teachers the ability to create interactive study guides using the AI research tool Notebook LM, along with their classroom materials.

This https://arxiv.org/abs/2504.14522 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Biased by Design: Leveraging AI Inherent Biases to Enhance Critical Thinking of News Readers
This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical thinking in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bia…

STELLA: Self-Evolving LLM Agent for Biomedical Research
Ruofan Jin, Zaixi Zhang, Mengdi Wang, Le Cong
https://arxiv.org/abs/2507.02004 https://

STELLA: Self-Evolving LLM Agent for Biomedical Research
The rapid growth of biomedical data, tools, and literature has created a fragmented research landscape that outpaces human expertise. While AI agents offer a solution, they typically rely on static, manually curated toolsets, limiting their ability to adapt and scale. Here, we introduce STELLA, a self-evolving AI agent designed to overcome these limitations. STELLA employs a multi-agent architecture that autonomously improves its own capabilities through two core mechanisms: an evolving Templat…

Meta reveals more information about its research glasses Aria Gen 2, including their form factor, computer vision cameras, sensors, and on-device compute (Emma Roth/The Verge)
https://www.theverge.com/news/679707/meta-aria-gen-2-upgrades-specs-ai

Here’s what’s inside Meta’s experimental new smart glasses
Meta has revealed the technical details about Aria Gen 2, the latest version of its experimental smart glasses designed for researchers.

Towards Effective Multidisciplinary Health and HCI Teams based on AI Framework
Mohammed Almutairi, Diego G\'omez-Zar\'a
https://arxiv.org/abs/2506.05226

Towards Effective Multidisciplinary Health and HCI Teams based on AI Framework
As a Ph.D. student with a diverse background in both public and private sectors, I have encountered numerous challenges in cross-disciplinary and multi-stakeholder team projects. My research on developing team compositions that involve multidisciplinary members from fields including education, academia, and health. Along with my advisor, we are focused on exploring how HCI can help individuals assemble more effective teams. This effort involves developing socio-technical systems that guide and …

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
Saskia Laura Schr\"oer, Luca Pajola, Alberto Castagnaro, Giovanni Apruzzese, Mauro Conti
https://arxiv.org/abs/2506.12519

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
As Artificial Intelligence (AI) continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across various sectors. In security, AI's role in strengthening organizational resilience has been studied for over two decades. While much attention has focused on AI's constructive applications, the increasing maturity and integration of AI have also exposed its darker potentials. This article explores two emerging AI-relate…

Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
Boyuan Zheng, Zerui Fang, Zhe Xu, Rui Wang, Yiwen Chen, Cunshi Wang, Mengwei Qu, Lei Lei, Zhen Feng, Yan Liu, Yuyang Li, Mingzhou Tan, Jiaji Wu, Jianwei Shuai, Jia Li, Fangfu Ye
https://arxiv.org/abs/2506.23692

Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use of LLM-driven agents to automate the entire research workflow-as the true Fifth Scientific Paradigm. This paper introduces a five-level classification for Agent4S, outlining a clear roadmap from simple task automation to fully autonomous, collaborative "AI Scientists." This framework defines the next revolutionary ste…

Q&A with Yoshua Bengio on his nonprofit LawZero focusing on AI safety, its research direction, risks of increasingly capable and autonomous AI systems, and more (Sigal Samuel/Vox)
https://www.vox.com/future-perfect/417087/ai-safety-yoshua-bengio-lawze…

He’s the godfather of AI. Now, he has a bold new plan to keep us safe from it.
Humans — not AI systems — should be the ones with agency, says the godfather of AI.

This https://arxiv.org/abs/2504.02234 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

LLM Social Simulations Are a Promising Research Method
Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted this method. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a review of empirical comparisons between LLMs and human r…

This https://arxiv.org/abs/2506.00202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & know…

AISCliteracy: Assessing Artificial Intelligence and Cybersecurity Literacy Levels and Learning Needs of Students
Devendra Chapagain, Naresh Kshetri, Bishwo Prakash Pokharel
https://arxiv.org/abs/2506.23321

AISCliteracy: Assessing Artificial Intelligence and Cybersecurity Literacy Levels and Learning Needs of Students
Artificial intelligence (AI) is rapidly transforming global industries and societies, making AI literacy an indispensable skill for future generations. While AI integration in education is still emerging in Nepal, this study focuses on assessing the current AI literacy levels and identifying learning needs among students in Chitwan District of Nepal. By measuring students' understanding of AI and pinpointing areas for improvement, this research aims to provide actionable recommendations for edu…

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
Zhanxin Hao, Haifeng Luo, Yongyi Chen, Yu Zhang
https://arxiv.org/abs/2506.02966

Unpacking Graduate Students' Learning Experience with Generative AI Teaching Assistant in A Quantitative Methodology Course
The study was conducted in an Advanced Quantitative Research Methods course involving 20 graduate students. During the course, student inquiries made to the AI were recorded and coded using Bloom's taxonomy and the CLEAR framework. A series of independent sample t-tests and poisson regression analyses were employed to analyse the characteristics of different questions asked by students with different backgrounds. Post course interviews were conducted with 10 students to gain deeper insights int…

Airalogy: AI-empowered universal data digitization for research automation
Zijie Yang, Qiji Zhou, Fang Guo, Sijie Zhang, Yexun Xi, Jinglei Nie, Yudian Zhu, Liping Huang, Chou Wu, Yonghe Xia, Xiaoyu Ma, Yingming Pu, Panzhong Lu, Junshu Pan, Mingtao Chen, Tiannan Guo, Yanmei Dou, Hongyu Chen, Anping Zeng, Jiaxing Huang, Tian Xu, Yue Zhang
https://

Airalogy: AI-empowered universal data digitization for research automation
Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured, digitized datasets. Achieving comprehensive AI empowerment across multiple disciplines is still out of reach. Present-day research data collection is often fragmented, lacking unified standards, inefficiently managed, and difficult to share. Creating a single platform for standardized data digitization needs to overc…

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn, Angelina Wang, Fernando Diaz, Flavio du Pin Calmon, Margaret Mitchell, Michael Ekstrand, Reuben Binns, Solon Barocas
https://arxiv.org/abs/2506.14652

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about AI capabilities. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception -- i…

Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection
Ji-Youn Jung, Devansh Saxena, Minjung Park, Jini Kim, Jodi Forlizzi, Kenneth Holstein, John Zimmerman
https://arxiv.org/abs/2506.17494

Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection
AI projects often fail due to financial, technical, ethical, or user acceptance challenges -- failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, this paper investigates how early-stage AI concept sorting in commercial settings can reflect RAI principles. Through three design experiments -- including a probe study with ind…

Google DeepMind unveils AlphaGenome, an AI tool to predict the effects of DNA changes on molecular processes, available via an API for non-commercial research (Antonio Regalado/MIT Technology Review)
https://www.technologyreview.com/2025/06/25/1…

Google’s new AI will help researchers understand how our genes work
First came AlphaFold. Now comes AlphaGenome for DNA.

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Moto Kamiura
https://arxiv.org/abs/2507.02442 https://

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of our research is to contribute to this improvement by reformulating machine learning models through the lens of category theory, thereby developing a semantic framework for structuring and understanding AI systems. Our categorical modeling in this paper clarifies and formalize…

URSA: The Universal Research and Scientific Agent
Michael Grosskopf, Russell Bent, Rahul Somasundaram, Isaac Michaud, Arthur Lui, Nathan Debardeleben, Earl Lawrence
https://arxiv.org/abs/2506.22653

URSA: The Universal Research and Scientific Agent
Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in "agentic" AI has the potential to revolutionize modern science and remove bottlenecks to progress. In this work, we present URSA, a scientific agent ecosystem for…

Anthropic's test of 16 top AI models from OpenAI and others found that, in some cases, they resorted to malicious behavior to avoid replacement or achieve goals (Ina Fried/Axios)
https://www.axios.com/2025/06/20/ai-models-deceive-steal-blackmail-an…

Top AI models will lie, cheat and steal to reach goals, Anthropic finds
The research used hypothetical scenarios, but those situations could get real fast.

Anthropic launches the Anthropic Economic Futures Program to bolster research on the impacts of AI and encourage new proposals on how to mitigate the downsides (Reed Albergotti/Semafor)
https://www.semafor.com/article/06/27/2025

Anthropic launches new effort to study AI’s economic impact
CEO Dario Amodei has warned of massive job losses because of AI and hopes research will help the world prepare.

What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Matthew Kam, Cody Miller, Miaoxin Wang, Abey Tidwell, Irene A. Lee, Joyce Malyn-Smith, Beatriz Perez, Vikram Tiwari, Joshua Kenitzer, Andrew Macvean, Erin Barrar
https://arxiv.org/abs/2506.00202…

What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & know…