Watermarking has emerged as a promising technique to track AI-generated content and differentiate it from authentic human creations. While prior work extensively studies watermarking for autoregressive large language models (LLMs) and image diffusion models, none address discrete diffusion language models, which are becoming popular due to their high inference throughput. In this paper, we introduce the first watermarking method for discrete diffusion models by applying the distribution-preserving Gumbel-max trick at every diffusion step and seeding the randomness with the sequence index to enable reliable detection. We experimentally demonstrate that our scheme is reliably detectable on state-of-the-art diffusion language models and analytically prove that it is distortion-free with an exponentially decaying probability of false detection in the token sequence length.

Large Language Models (LLMs) demonstrate varying performance across languages and cultural contexts. This study introduces a novel, culturally-rich, multilingual dataset derived from video recordings of the Romanian game show "Who Wants to Be a Millionaire?" (Vrei să fii Milionar?). We employed an innovative process combining optical character recognition (OCR), automated text extraction, and manual verification to collect question-answer pairs, enriching them with metadata including question domain (e.g., biology, history), cultural relevance (Romanian-specific vs. international), and difficulty. Benchmarking state-of-the-art LLMs, including Romanian-adapted models, on this dataset revealed significant performance disparities: models consistently achieve higher accuracy (80-95%) on international questions compared to Romanian-specific cultural questions (50-75%). We further investigate these differences through experiments involving machine translation of Romanian questions into English and cross-lingual tests using a comparable dataset in French. Our findings underscore the impact of cultural context and data source on LLM performance and offer practical insights for building robust, culturally-aware multilingual NLP systems, especially in educational domains. The dataset is publicly available at Hugging Face.

Tech millionaire turned longevity pioneer Bryan Johnson devotes more than 6 hours a day to trialling different methods to turn back the clock. Can the rest of us learn anything from his radical approach? Bryan Johnson is finishing his 6.5-hour morning routine when I sign on to Zoom for my allotted 15-minute call with him (a constraint of what a member of his team describes as his "crazy" schedule). The tech millionaire turned longevity pioneer is standing in front of a cement wall in his California home, the coldness of which is relieved by green bursts of tropical houseplants. Wearing a helmet-like headset, a few wires trailing out and down past the screen, together with a black T-shirt bearing the words "Don't Die", the effect is somewhere between a luxury Balinese villa and a VR store designed by Apple.

The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. SecONNds integrates a novel fully Boolean Goldreich-Micali-Wigderson (GMW) protocol for secure comparison -- addressing Yao's millionaires' problem -- using preprocessed Beaver's bit triples generated from Silent Random Oblivious Transfer. Our novel protocol achieves an online speedup of 17$\times$ in nonlinear operations compared to state-of-the-art solutions while reducing communication overhead. To further enhance performance, SecONNds employs Number Theoretic Transform (NTT) preprocessing and leverages GPU acceleration for homomorphic encryption operations, resulting in speedups of 1.6$\times$ on CPU and 2.2$\times$ on GPU for linear operations. We also present SecONNds-P, a bit-exact variant that ensures verifiable full-precision results in secure computation, matching the results of plaintext computations. Evaluated on a 37-bit quantized SqueezeNet model, SecONNds achieves an end-to-end inference time of 2.8 s on GPU and 3.6 s on CPU, with a total communication of just 420 MiB. SecONNds' efficiency and reduced computational load make it well-suited for deploying privacy-sensitive applications in resource-constrained environments. SecONNds is open source and can be accessed from: https://github.com/shashankballa/SecONNds.

Utilitarian games such as dictator games to measure fairness have been studied in the social sciences for decades. These games have given us insight into not only how humans view fairness but also in what conditions the frequency of fairness, altruism and greed increase or decrease. While these games have traditionally been focused on humans, the rise of AI gives us the ability to study how these models play these games. AI is becoming a constant in human interaction and examining how these models portray fairness in game play can give us some insight into how AI makes decisions. Over 101 rounds of the dictator game, I conclude that AI has a strong sense of fairness that is dependant of it it deems the person it is playing with as trustworthy, framing has a strong effect on how much AI gives a recipient when designated the trustee, and there may be evidence that AI experiences inequality aversion just as humans.

One unlikely day during the empty-belly years of the Great Depression, an advertisement appeared in the smeared, smashed-ant font of the New York Times' classifieds: Five hundred college graduates, male, to perform secretarial work of a pleasing nature. Thousands of desperate, out-of-work bachelors of arts applied; five hundred were hired ("they were mainly plodders, good men, but not brilliant"). They went to work for a mysterious Elon Musk-like millionaire who was devising "a new plan of universal knowledge." In a remote manor in Pennsylvania, each man read three hundred books a year, after which the books were burned to heat the manor. At the end of five years, the men, having collectively read three-quarters of a million books, were each to receive fifty thousand dollars.

With AI beginning to transform just about everything, the potential $1.8 trillion AI boom could mint a few millionaires. Even analysts at Bank of America are excited, noting that AI stocks are on the brink of an "iPhone moment" and could boost the global economy by $15.7 trillion in seven years. "We are at a defining moment – like the internet in the '90s – where Artificial Intelligence (AI) is moving towards mass adoption, with large language models like ChatGPT finally enabling us to fully capitalize on the data revolution," the firm told Business Insider. "It took ChatGPT just 5 days to reach 1 million users, 1 billion cumulative visits in 3 months, and an adoption rate which is 3x Tik Tok and 10x Instagram's," they added. "The technology is developing exponentially."

Investors are in search of Machine Learning stocks to invest in, observing a rapid increase in the use of machine learning across various sectors, including technology, healthcare, automotive, retail, advertising, defense, and financial services, as it is one of the key factors driving growth in ML stocks to become a millionaire. According to a Business Insights industry analysis report, the global machine learning market was worth $15.4 billion in 2021 and is projected to grow to more than $21 billion in 2022. By the end of 2029, the machine learning stock market is projected to be worth $210 billion and growing at a compound annual growth rate of 38.8% between 2022 and 2029. So, it is important to know the top companies and Machine Learning stocks to invest in to become a millionaire. International Business Machines Corporation (NYSE: IBM) and the Saudi Data and Artificial Intelligence Authority established a strategic partnership on September 27 to deploy artificial intelligence for carbon capture throughout the Kingdom of Saudi Arabia.

As a humanist who writes about the impact of digital technology on our lives, I am often mistaken for a futurist. The people most interested in hiring me for my opinions about technology are usually less concerned with building tools that help people live better lives in the present than they are in identifying the Next Big Thing through which to dominate them in the future. I don't usually respond to their inquiries. Why help these guys ruin what's left of the internet, much less civilisation? Still, sometimes a combination of morbid curiosity and cold hard cash is enough to get me on a stage in front of the tech elite, where I try to talk some sense into them about how their businesses are affecting our lives out here in the real world. That's how I found myself accepting an invitation to address a group mysteriously described as "ultra-wealthy stakeholders", out in the middle of the desert. A limo was waiting for me at the airport.

Sophisticated entrepreneurs and parents are in the early stages of mass market growth. The Artificial Intelligence (AI) software market is expected to grow 21% this year, while the overall AI market is expected to grow at a CAGR of 40% by 2028. Investors will be hard pressed to find another market with the expected strength. The key players in this market are Nvidia (NVDA-2.10%) and IBM (IBM 0.10%), both of which are moving towards enabling more efficient and automated lifestyles with potential for improvement and life-saving potential. Both can provide the resources needed to build your long-term portfolio.