- A.I. Is Coming for Mathematics, Too – The New York Times
Some mention of Lean and AI “proof-assistants” - AI Video Interviews and Hiring in 2025: The Future of Recruitment
Data Capture: As the candidate speaks and interacts with the system, the AI captures and analyzes multiple forms of data, such as:
Facial expressions: The system studies facial micro-expressions, like smiles or frowns, to assess emotional responses.
Voice modulation: AI picks up on tone, speed, and pitch to gauge confidence, stress, and honesty.
Speech patterns: The AI analyzes the content of the candidate’s answers to assess communication skills and even cultural fit. - Bentley-Gallup Business in Society Report
Seventy-nine percent of Americans say they trust businesses “not much” or “not at all” to use AI responsibly, and 40% say AI does greater harm than it does good. - OpenScholar: The open-source A.I. that’s outperforming GPT-4o in scientific research | VentureBeat
The OpenScholar team has released not only the code for the language model but also the entire retrieval pipeline, a specialized 8-billion-parameter model fine-tuned for scientific tasks, and a datastore of scientific papers. “To our knowledge, this is the first open release of a complete pipeline for a scientific assistant LM—from data to training recipes to model checkpoints,” the researchers wrote in their blog post announcing the system. - AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
Given these concerns, this study sought to explore the impact of AI tool usage on critical thinking skills with a particular focus on cognitive offloading as a mediating variable. This research aimed to provide a comprehensive understanding of the broader cognitive implications of AI tool usage by investigating how AI tools influence cognitive processes and the extent to which they encourage cognitive offloading. - Releasing Common Corpus: the largest public domain dataset for training LLMs
- The Dataset Convening: A community workshop on open AI datasets
Leading AI companies want us to believe that training performant LLMs without copyrighted material is impossible. We refuse to believe this. An emerging ecosystem of open LLM developers have created LLM training datasets —such as Common Corpus, YouTube-Commons, Fine Web, Dolma, Aya, Red Pajama and many more—that could provide blueprints for more transparent and responsible AI progress. - GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy – Google DeepMind
GenCast is a diffusion model, the type of generative AI model that underpins the recent, rapid advances in image, video and music generation. However, GenCast differs from these, in that it’s adapted to the spherical geometry of the Earth, and learns to accurately generate the complex probability distribution of future weather scenarios when given the most recent state of the weather as input.To train GenCast, we provided it with four decades of historical weather data from ECMWF’s ERA5 archive. This data includes variables such as temperature, wind speed, and pressure at various altitudes. The model learned global weather patterns, at 0.25° resolution, directly from this processed weather data.
- AIAAIC – User guide
The AIAAIC Repository (standing for ‘AI, Algorithmic and Automation Incidents and Controversies’) is an independent, open, public interest resource that details incidents and controversies driven by and relating to AI, algorithms and automation.Part dataset, part Wikipedia-style knowledge graph, the Repository forms part of a broader initiative to make AI, algorithmic and automation systems more transparent and open, and ultimately more accountable.
- The Insecurity Machine
Data brokers create intimate profiles so that we might be better targeted—segmenting us into categories that include “rural and barely making it,” “probably bipolar,” and “gullible elderly”—while companies invest millions into “affect recognition” so they can figure out when we are most persuadable, increasing psychological insecurity. Opaque systems of information collection and predictive analytics facilitate new forms of discrimination and redlining, marking certain populations as criminal threats or directing them into subprime financial services, predatory mortgages, and exploitative rental markets, increasing housing insecurity. Employers monitor and control employees remotely, refusing to offer decent wages and benefits or provide consistent scheduling, increasing job insecurity. - The Cult of Mac. You cannot reason a person out of a… | by Cory Doctorow | Medium
Apple’s most valuable intangible asset isn’t its patents or copyrights — it’s an army of people who believe that using products from a $2.89 trillion multinational makes them members of an oppressed religious minority whose identity is coterminal with the interests of Apple’s shareholders.–a pattern that’s being used in so many places lately
- Why Is Turing Wrong? Rethinking the nature of intelligence — LessWrong