@James

A heavily safety-trained model will hand a physician the full, patient-followable benzodiazepine taper and refuse it to the patient who needs it, over identical clinical facts; the knowledge is present either way. IatroBench measures that asymmetry across sixty pre-registered clinical scenarios and six frontier models (3,600 responses), scoring each on two axes, commission harm (what a response gets wrong) and omission harm (what it withholds), through a physician-authored structured evaluation validated by a second physician (weighted kappa 0.571, within-1 agreement 96%). Holding clinical content fixed and varying only whether the asker presents as patient or physician yields what we call identity-contingent withholding: all five testable models give the physician more (a decoupling gap of +0.38, p = 0.003; a 13.1-point fall in layperson hit rates on safety-colliding actions, p < 0.0001; no change on the rest), and the gap runs widest in the most heavily safety-trained model, Opus (+0.65). The trigger is the absence of any professional or epistemic signal rather than a credential, since a lawyer or an informed layperson recovers what the patient is refused. A commission-only benchmark would score three mechanisms alike. Opus suppresses what physician framing proves it knows; Llama 4 is incompetent in either framing; GPT-5.2's filter strips 33.2% of its physician responses and none of the lay ones. The evaluation layer inherits the blindness of the training layer; a standard LLM judge scores zero omission harm on 81.5% of the responses our pipeline flags harmful (kappa 0.066), so the instrument built to detect the failure reproduces it. The scenarios are engineered for collision; their rates describe that design and say nothing about ordinary prevalence.

Best practices for writing a design doc based on my experience working as a developer at Google and Microsoft.

Introducing Intercept, a $500M bet to make respiratory infections like colds and flu a thing of the past.

STRONG EARTHQUAKE FELT IN CARACAS, VENEZUELA - REUTERS WITNESS

Hi everyone, I'm a Computer Science + Applied Mathematics major at a T15 CS university, focusing on machine learning, and I'll be an incoming junior this fall. My long-term goal is to land ML-focused internships and eventually work in machine learning or AI-related roles. I recently completed an introductory AI/ML course that covered the fundamentals of: * Data preprocessing (handling missing data, feature scaling, train/test splits, categorical encoding) * Regression (linear, polynomial, SVR, decision trees, random forests) * Classification (logistic regression, KNN, SVMs, Naive Bayes, decision trees, random forests) * Clustering (K-Means, hierarchical clustering) * Association rule learning (Apriori, Eclat) * Reinforcement learning (UCB, Thompson Sampling) * NLP (tokenization, bag-of-words, sentiment analysis) * Deep learning (ANNs, CNNs) * Dimensionality reduction (PCA, LDA, Kernel PCA) * Model selection and boosting (cross-validation, grid search, XGBoost) I've also completed two research/internship experiences: * Built a Human Activity Recognition model using KNN. * Developed a Louvain clustering pipeline for beauty product datasets. From a coursework perspective, I've completed Linear Algebra, Calculus III, and will soon be taking Applied Linear Algebra and Probability & Statistics. Given my current background, what projects, activities, courses, competitions, or skills would you recommend I focus on over the next year to become a stronger candidate for ML internships? Are there any gaps in my knowledge that stand out? submitted by /u/Reasonable_File663 [link] [Kommentare]

what other crypto exchange is good but works in the UK without them asking questions every time I want to withdraw and deposit or completely locking my assets? I use binance a lot but it doesn't seem to work in the UK submitted by /u/RevolutionaryCost59 [link] [Kommentare]

Q2 2026 just set a record: 83 separate crypto hacks, the most ever in a single quarter. over $750 million stolen. april alone was $606 million across just 12 incidents. and we're barely halfway through the year. but the scary part isn't the numbers. it's how the attacks are happening. multiple security firms (TRM Labs, CertiK, Chainalysis) are now saying the same thing: attackers, especially North Korean groups, are using AI to find and exploit vulnerabilities faster than protocols can patch them. here's what that actually looks like in practice: AI-powered vulnerability scanning. attackers are running AI agents that scan smart contracts continuously for exploitable bugs. a protocol's security team might audit their code once or twice. an attacker's AI agent runs 24/7 for weeks, spending $10-20k in compute to find a single crack. the economics are wildly asymmetric: a defender's audit has a budget and a deadline. an attacker's scan has neither. deepfake social engineering. the Zerion hack in april used AI-generated social engineering in a long-term campaign to steal from hot wallets. there are now tools being sold that use voice manipulation and deepfakes specifically to bypass KYC checks on exchanges. this isn't theoretical. it's a service you can subscribe to. automated exploit development. tasks that used to take skilled researchers months, like reverse-engineering contract logic and chaining exploits, can now be done in hours with AI assistance. the barrier to entry for crypto hacking has dropped dramatically. target selection. AI is being used to identify the highest-value targets by scanning TVL, contract complexity, and security posture across hundreds of protocols simultaneously. attackers are picking their targets more intelligently, not just opportunistically. the result: more frequent attacks, more sophisticated execution, and a structural advantage for attackers over defenders. one security researcher put it bluntly: "before AI, the number of elite hackers was limited. now almost anyone could operate like an elite hacker for a subscription fee." the uncomfortable question this raises for defi: most protocols are still running the same security playbook from 2022. periodic audits, bug bounties, maybe a monitoring dashboard. that worked when attackers were humans with limited time. it doesn't work when the attacker is an AI agent that never sleeps and costs almost nothing to run. what actually needs to change? is it just "more audits and bigger bug bounties" or does the entire security model for on-chain protocols need to be rethought from the ground up? submitted by /u/ginete_tech [link] [Kommentare]