WIRED recently demonstrated how to cheat at poker by hacking the Deckmate 2 card shufflers used in casinos. The mob was allegedly using the same trick to fleece victims for millions. Security researcher Joseph Tartaro demonstrates how he can insert a hacking device into a USB on the back of the shuffler that alters its code, then transmits the deck's order via Bluetooth to a phone app. The Deckmate 2 automatic card shufflers used in casinos, cardhouses, and high-end private poker games around the world are designed to shuffle a deck in seconds with perfect, computer-generated randomness, vastly speeding up play. They're also, amazingly, sold with a camera inside that can observe every card in the deck before it's dealt--a fact that's become very convenient for poker-cheating hackers and, allegedly, members of the Cosa Nostra mafia.

President Gitanas Nausėda called Russian aircraft incursion into Lithuanian territory a blatant breach of international law as NATO jets responded quickly.

After serving a federal prison sentence for violating anti-money-laundering laws and US sanctions, former crypto exchange CEO Changpeng Zhao has been pardoned by US president Donald Trump. US president Donald Trump has pardoned Changpeng Zhao, founder of the world's largest crypto exchange, Binance. Zhao, widely known as CZ, pled guilty in November 2023 to violating anti-money-laundering laws and US sanctions. The plea formed part of a sweeping deal with the US Department of Justice, under which Binance was required to pay a record-breaking $4.3 billion penalty. Zhao ultimately spent four months in federal prison.

OpenAI Removed Safeguards Before Teen's Suicide, Amended Lawsuit Claims OpenAI relaxed safeguards that would have prevented ChatGPT from engaging in conversations about self-harm in the months leading up to the suicide of Adam Raine, an amended complaint filed by the family in the San Francisco County Superior Court on Wednesday alleges. The amendment changes the theory of the case from reckless indifference to intentional misconduct, according to the family's lawyers, which could raise the damages awarded to the family. The Raine family's lawyers will have to prove that OpenAI was aware of the risks posed by ChatGPT and disregarded them. The family has asked for a jury trial. In an interview with TIME, Jay Edelson, one of the Raine family's lawyers, says OpenAI relaxed safeguards in an "intentional decision" to "prioritize engagement."

Early frameworks like the EU's AI Act focused on highly visible applications - banning high-risk uses in health, employment and law enforcement to prevent societal harms. But countries now target the underlying building blocks of AI. China restricts models to combat deepfakes and inauthentic content. Citing national security risks, the US controls the exports of the most advanced chips and, under Biden, even model weights - the "secret sauce" that turns user queries into results. These AI regulations are hiding in dense administrative language - "Implementation of Additional Export Controls" or "Supercomputer and Semiconductor End Use" bury the ledes. But behind this complex language is a clear trend: regulation is moving from AI applications to its building blocks.

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The advent of Large Language Models has revolutionized tasks across domains, including the automation of legal document analysis, a critical component of modern contract management systems. This paper presents a comprehensive implementation of LLM-enhanced metadata extraction for contract review, focusing on the automatic detection and annotation of salient legal clauses. Leveraging both the publicly available Contract Understanding Atticus Dataset (CUAD) and proprietary contract datasets, our work demonstrates the integration of advanced LLM methodologies with practical applications. We identify three pivotal elements for optimizing metadata extraction: robust text conversion, strategic chunk selection, and advanced LLM-specific techniques, including Chain of Thought (CoT) prompting and structured tool calling. The results from our experiments highlight the substantial improvements in clause identification accuracy and efficiency. Our approach shows promise in reducing the time and cost associated with contract review while maintaining high accuracy in legal clause identification. The results suggest that carefully optimized LLM systems could serve as valuable tools for legal professionals, potentially increasing access to efficient contract review services for organizations of all sizes.

A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introduce a new smooth nonconvex surrogate to approximate the Heaviside functions involved in discontinuous unfairness measures. The surrogate is based on smoothing methods from the optimization literature, and is new for the fair supervised learning literature. The surrogate is a tight approximation which ensures the trained prediction models are fair, as opposed to other (e.g., convex) surrogates that can fail to lead to a fair prediction model in practice. (b) Rather than rely on regularizers (that lead to optimization problems that are difficult to solve) and corresponding regularization parameters (that can be expensive to tune), we propose a strategy that employs hard constraints so that specific tolerances for unfairness can be enforced without the complications associated with the use of regularization. (c) Our proposed strategy readily allows for constraints on multiple (potentially conflicting) unfairness measures at the same time. Multiple measures can be considered with a regularization approach, but at the cost of having even more difficult optimization problems to solve and further expense for tuning. By contrast, through hard constraints, our strategy leads to optimization models that can be solved tractably with minimal tuning.

We present Hubble, a suite of fully open-source large language models (LLMs) for the scientific study of LLM memorization. Hubble models come in standard and perturbed variants: standard models are pretrained on a large English corpus, and perturbed models are trained in the same way but with controlled insertion of text (e.g., book passages, biographies, and test sets) designed to emulate key memorization risks. Our core release includes 8 models -- standard and perturbed models with 1B or 8B parameters, pretrained on 100B or 500B tokens -- establishing that memorization risks are determined by the frequency of sensitive data relative to size of the training corpus (i.e., a password appearing once in a smaller corpus is memorized better than the same password in a larger corpus). Our release also includes 6 perturbed models with text inserted at different pretraining phases, showing that sensitive data without continued exposure can be forgotten. These findings suggest two best practices for addressing memorization risks: to dilute sensitive data by increasing the size of the training corpus, and to order sensitive data to appear earlier in training. Beyond these general empirical findings, Hubble enables a broad range of memorization research; for example, analyzing the biographies reveals how readily different types of private information are memorized. We also demonstrate that the randomized insertions in Hubble make it an ideal testbed for membership inference and machine unlearning, and invite the community to further explore, benchmark, and build upon our work.

The rapid escalation of computational requirements for training large-scale language models has reinforced structural asymmetries between high-capacity jurisdictions and countries in the Global South. This paper examines the technical and fiscal feasibility of sovereign-scale language model training in Brazil and Mexico under conditions of constrained hardware access, energy availability, and fiscal ceilings. Using a dual-axis design that varies accelerator generation (NVIDIA H100 vs. A100) and training duration (90 vs. 150 days), we estimate compute demand, energy consumption, capital expenditures, and regulatory compatibility for the training of a 10-trillion-token model. Our findings show that while all configurations remain below export-control and electrical infrastructure thresholds, fiscal viability is determined by hardware efficiency. H100-based scenarios achieve training feasibility at a total cost of 8-14 million USD, while A100 deployments require 19-32 million USD due to higher energy and hardware demand. We argue that extending training timelines should be treated as a policy lever to mitigate hardware constraints, enabling the production of usable, auditable, and locally aligned models without competing at the global frontier. This study contributes to the discourse on AI compute governance and technological sovereignty by highlighting context-sensitive strategies that allow middle-income countries to establish sustainable and strategically sufficient AI capabilities.