Tesla avoids California sales ban by removing'autopilot' from marketing Tesla will avoid a 30-day suspension of its dealer and manufacturer licenses in California, its biggest market, after the US electric vehicle maker stopped using the term "autopilot" in the marketing of its vehicles in the state. Tesla now uses the term "supervised" in references to its full self-driving technology and has stopped using "autopilot" entirely in its marketing in the state. State regulators said Tuesday that Tesla had stopped misleading drivers about the safety of its cars, and so the state will not suspend its state sales license for 30 days, as had been threatened. The decision by the California department of motor vehicles comes after CEO Elon Musk's electric vehicle company was found by an administrative law judge last year to have misled drivers about the ability of Tesla cars to drive themselves in its use of the terms "autopilot" and "full self-driving". In 2022, the DMV had accused Tesla of misleading consumers by using "autopilot" and "full self-driving" for its advanced driver-assistance features.

Please see the most updated version here . Was there a specific task in mind? Was there a specific gap that needed to be filled? The MOTI VE dataset was created to promote the development of new drug-target interaction (DTI) prediction models based on both, existing relationships between compounds and their protein targets, and the similarity of JUMP Cell Painting morphological features of perturbed cells [2].The MOTI VE dataset was created with the DTI task in mind, and addresses a lack of graph-based biological datasets with empirical node features. Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? This dataset was created by the Carpenter-Singh Lab in the Imaging Platform at the Broad Institute of MIT and Harvard, Cambridge, Massachusetts. What support was needed to make this dataset? If there is an associated grant, provide the name of the grantor and the grant name and number, or if it was supported by a company or government agency, give those details.) The authors gratefully acknowledge an internship from the Massachusetts Life Sciences Center (to ES).

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

Scout AI is using technology borrowed from the AI industry to power lethal weapons--and recently demonstrated its explosive potential. Like many Silicon Valley companies today, Scout AI is training large AI models and agents to automate chores. The big difference is that instead of writing code, answering emails, or buying stuff online, Scout AI's agents are designed to seek and destroy things in the physical world with exploding drones. In a recent demonstration, held at an undisclosed military base in central California, Scout AI's technology was put in charge of a self-driving off-road vehicle and a pair of lethal drones. The agents used these systems to find a truck hiding in the area, and then blew it to bits using an explosive charge.

We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes .

Large Language Models (LLMs) have long held sway in the realm s of artificial intelligence research. Numerous efficient techniques, inc luding weight pruning, quantization, and distillation, have been embraced to comp ress LLMs, targeting memory reduction and inference acceleration, which unders core the redundancy in LLMs. However, most model compression techniques concen trate on weight optimization, overlooking the exploration of optimal arch itectures. Besides, traditional architecture search methods, limited by the eleva ted complexity with extensive parameters, struggle to demonstrate their effecti veness on LLMs. In this paper, we propose a training-free architecture search fram ework to identify optimal subnets that preserve the fundamental strengths of the o riginal LLMs while achieving inference acceleration. Furthermore, after gen erating subnets that inherit specific weights from the original LLMs, we introduce a reformation algorithm that utilizes the omitted weights to rectify the inher ited weights with a small amount of calibration data. Compared with SOT A training-fr ee structured pruning works that can generate smaller networks, our method dem onstrates superior performance across standard benchmarks. Furthermore, our generated subnets can directly reduce the usage of GPU memory and achieve infer ence acceleration.

When turned on, "GitHub Copilot checks code completion suggestions with their surrounding