Security experts have urged people to be cautious with the viral agentic AI tool, known for being highly capable but also wildly unpredictable. Last month, Jason Grad issued a late-night warning to the 20 employees at his tech startup. "You've likely seen Clawdbot trending on X/LinkedIn. While cool, it is currently unvetted and high-risk for our environment, he wrote in a Slack message with a red siren emoji. "Please keep Clawdbot off all company hardware and away from work-linked accounts." Grad isn't the only tech executive who has raised concerns to staff about the experimental agentic AI tool, which was briefly known as MoltBot and is now named OpenClaw. A Meta executive says he recently told his team to keep OpenClaw off their regular work laptops or risk losing their jobs. The executive told reporters he believes the software is unpredictable and could lead to a privacy breach if used in otherwise secure environments. He spoke on the condition of anonymity to speak frankly.

RBF kernel to increase pretraining data diversity. Architectural details In all experiments, we use the same ExPT architecture. This section details how we constructed new objectives from the original D'Kitty and Ant that we In Ant-Energy, the reward at each time step is: R =1+ Survival reward Control cost Contact cost, (6) which means we incentivize the robot to conserve energy instead of running fast. D'Kitty tasks In D'Kitty, the goal is to design a morphology that allows the D'Kitty robot to reach We found the approximate oracle provided by Design-Bench not accurate enough to provide a reliable comparison of optimization methods on this task. C.1 Effects of GP hyperparameters We empirically examine the impact of two GP hyperparameters, the variance and the length scale ` Specifically, we evaluate the performance of ExPT on D'Kitty We average the performance across 3 seeds.

Different inner loss and outer loss other than what we used can be instantiated in the subclass.

Chronic Kidney Disease (CKD) constitutes a major global medical burden, marked by the gradual deterioration of renal function, which results in the impaired clearance of metabolic waste and disturbances in systemic fluid homeostasis. Owing to its substantial contribution to worldwide morbidity and mortality, the development of reliable and efficient diagnostic approaches is critically important to facilitate early detection and prompt clinical management. This study presents a deep convolutional neural network (CNN) for early CKD detection from CT kidney images, complemented by class balancing using Synthetic Minority Over-sampling Technique (SMOTE) and interpretability via Gradient-weighted Class Activation Mapping (Grad-CAM). The model was trained and evaluated on the CT KIDNEY DATASET, which contains 12,446 CT images, including 3,709 cyst, 5,077 normal, 1,377 stone, and 2,283 tumor cases. The proposed deep CNN achieved a remarkable classification performance, attaining 100% accuracy in the early detection of chronic kidney disease (CKD). This significant advancement demonstrates strong potential for addressing critical clinical diagnostic challenges and enhancing early medical intervention strategies.

Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the industry. Recent advances in conditional generative models, such as transformers and diffusers, have enabled direct trajectory generation tailored to advertiser preferences, offering a promising alternative to traditional Markov Decision Process-based methods. However, these generative methods face significant challenges, such as the distribution shift between offline and online environments, limited exploration of the action space, and the necessity to meet constraints like marginal Cost-per-Mille (CPM) and Return on Investment (ROI). To tackle these challenges, we propose GRAD (Generative Reward-driven Ad-bidding with Mixture-of-Experts), a scalable foundation model for auto-bidding that combines an Action-Mixture-of-Experts module for diverse bidding action exploration with the Value Estimator of Causal Transformer for constraint-aware optimization. Extensive offline and online experiments demonstrate that GRAD significantly enhances platform revenue, highlighting its effectiveness in addressing the evolving and diverse requirements of modern advertisers. Furthermore, GRAD has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 2.18% increase in Gross Merchandise Value (GMV) and 10.68% increase in ROI.

We draw the data points by random sampling from the full data according to their gradient values.

Neural Information Processing Systems http://nips.cc/

"What we observe is not nature itself, but nature exposed to our method of questioning.

In this work, we introduce a scalable Bayesian causal discovery framework based on a combination of stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and V ariational Inference (VI) that overcomes these limitations.