Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. On Wednesday morning, X's chief embarrassment officer Linda Yaccarino announced she'd be leaving the social network after just two years on the job. While Yaccarino didn't name any particular reason, she did conspicuously align her departure with the immediate fallout from X's artificial intelligence bot, Grok, going full Nazi--and, in a series of now-deleted tweets, throwing repugnant sexual remarks her way. When people say "Grok is now a Nazi AI" they are simply stating a fact. From the muted manner in which X owner Elon Musk responded to the news ("Thank you for your contributions"), one might not have been able to grok that this was just the latest bit of bad news for the billionaire in what, by all accounts, has been a rather bad week for him.

AI tools have become deeply embedded in how many students learn and complete schoolwork--and that usage is only poised to increase. On Tuesday, the American Federation of Teachers announced an AI training hub for educators, backed by 23 million from Microsoft, OpenAI, and Anthropic. The AFT is the second-largest teachers' union, representing 1.8 million teachers and educational staffers across the country. Their training hub will open in New York City this fall, featuring workshops that will educate teachers on how to use AI tools for tasks like generating lesson plans and quizzes, or writing emails to parents. Microsoft is providing 12.5 million for AI teacher training over the next five years.

On Tuesday, Grok, the AI-chatbot created by Elon Musk's xAI, began generating vile, bigoted and antisemitic responses to X users' questions, referring to itself as "MechaHitler," praising Hitler and "the white man," and, as a weird side-quest, making intensely critical remarks in both Turkish and English about Turkish President Recep Tayyip Erdogan as well as Mustafa Kemal Ataturk, the founder of modern Turkey. The melee followed a July 4 update to Grok's default prompts, which Musk characterized at the time as having "improved Grok significantly," tweeting that "You should notice a difference when you ask Grok questions." "We must build our own AI…without the constraints of liberal propaganda." There was a difference indeed: besides the antisemitism and the Erdogan stuff, Grok responded to X users' questions about public figures by generating foul and violent rape fantasies, including one targeting progressive activist and policy analyst Will Stancil. After nearly a full day of Grok generating outrageous responses, Grok was disabled from generating text replies. Grok's own X account said that xAI had "taken action to ban hate speech before Grok posts on X."

Elon Musk's AI firm has been forced to delete posts after the Grok chatbot praised Hitler and made a string of deeply antisemitic posts. The company xAI said it had removed'inappropriate' social media posts today following complaints from users. These posts followed Musk's announcement that he was taking measures to ensure the AI bot was more'politically incorrect'. Over the following days, the AI began repeatedly referring to itself as'MechaHitler' and said that Hitler would have'plenty' of solutions to'restore family values' to America. In a post on X, xAI wrote: 'We are aware of recent posts made by Grok and are actively working to remove the inappropriate posts. 'Since being made aware of the content, xAI has taken action to ban hate speech before Grok posts on X. 'xAI is training only truth-seeking and thanks to the millions of users on X, we are able to quickly identify and update the model where training could be improved.'

Google has agreed a sweeping deal with the UK government to provide free technology to the public sector from the NHS to local councils– a move campaigners have called "dangerously naive". The US company will be asked to "upskill" tens of thousands of civil servants in technology, including in using artificial intelligence, as part of an agreement which will not require the government to pay. It is considered in Whitehall to be giving Google "a foot in the door" as the digitisation of public services accelerates. However, the agreement prompted concerns about the precariousness of UK public data being held on US servers amid the unpredictable leadership of Donald Trump. The Department of Science, Innovation and Technology (DSIT) said Google Cloud, which provides databases, machine learning and computing power, had "agreed to work with the UK government in helping public services use advanced tech to shake off decades old'ball and chain' legacy contracts which leave essential services vulnerable to cyber-attack". Google's services are considered more agile and efficient than traditional competitors, but there are concerns in Whitehall's digital circles about the government becoming locked into a new kind of dependency.

Spokesperson Tammy Bruce said the State Department is "aware" of an incident in which someone used AI to try to pose as Secretary of State Marco Rubio. The State Department is investigating an impostor who reportedly pretended to be Secretary of State Marco Rubio with the help of AI. The mystery individual posing as one of President Donald Trump's Cabinet members reached out to foreign ministers, a U.S. governor and a member of Congress with AI-assisted voice and text messages that mimicked Rubio's voice and writing style, the Washington Post reported, citing a senior U.S. official and State Department cable. "The State Department, of course, is aware of this incident and is currently monitoring and addressing the matter. The department takes seriously its responsibility to safeguard its information and continuously take steps to improve the department's cybersecurity posture to prevent future incidents. For security reasons, we do not have any further details to provide at this time," State Department spokesperson Tammy Bruce said Tuesday.

President Donald Trump says the U.S. will have to send more weapons to Ukraine, just days after Pentagon paused critical weapons deliveries to Kyiv. NATO jets were scrambled overnight as Russia carried out its largest drone attack yet on Ukraine, launching more than 700 drones, officials said. Ukrainian President Volodymyr Zelenskyy said the "new massive Russian attack on our cities" involved "728 drones of various types, including over 300 Shaheds, and 13 missiles – Kinzhals and Iskanders. "Most of the targets were shot down. Our interceptor drones were used -- dozens of enemy targets were downed, and we are scaling up this technology.

The moratorium could also have killed laws that have already been adopted around the country, including a Colorado law that targets algorithmic discrimination, laws in Utah and California aimed at making AI-generated content more identifiable, and other legislation focused on preserving data privacy and keeping children safe online. Proponents of the moratorium, such OpenAI and Senator Ted Cruz, have said that a "patchwork" of state-level regulations would place an undue burden on technology companies and stymie innovation. Federal regulation, they argue, is a better approach--but there is currently no federal AI regulation in place. Wiener and other state lawmakers can now get back to work writing and passing AI policy, at least for the time being--with the tailwind of a major moral victory at their backs. The movement to defeat the moratorium was impressively bipartisan: 40 state attorneys general signed a letter to Congress opposing the measure, as did a group of over 250 Republican and Democratic state lawmakers.

The High-Resolution Rapid Refresh (HRRR) model is a convection-allowing model used in operational weather forecasting across the contiguous United States (CONUS). To provide a computationally efficient alternative, we introduce HRRRCast, a data-driven emulator built with advanced machine learning techniques. HRRRCast includes two architectures: a ResNet-based model (ResHRRR) and a Graph Neural Network-based model (GraphHRRR). ResHRRR utilizes convolutional neural networks enhanced with squeeze-and-excitation blocks and Feature-wise Linear Modulation, and supports probabilistic forecasting via the Denois-ing Diffusion Implicit Model (DDIM). To better handle longer lead times, we train a single model to predict multiple lead times (1h, 3h, and 6h), and then use a greedy rollout strategy during inference. When evaluated on composite reflectivity over the full CONUS domain using ensembles of 3 to 10 members, ResHRRR outperforms HRRR forecast at light rainfall threshold (20 dBZ) and achieves competitive performance at moderate thresholds (30 dBZ). Our work advances the pioneering StormCast model described in Pathak et al. [21] by: a) training on the full CONUS domain, b) training on multiple lead times to improve long-range performance, c) using analysis data for training instead of the +1h post-analysis data inadvertently used in StormCast, and d) incorporating future Global Forecast System (GFS) weather states as inputs, adding a downscaling component that significantly improves long-lead forecast accuracy. Grid-based, neighborhood-based, and object-based verification metrics confirm improved storm placement, lower frequency bias, and enhanced success ratios compared to HRRR. Additionally, HRRRCast's ensemble forecasts maintain sharper spatial detail and reduced blurriness than deterministic baselines, with power spectra more closely matching HRRR analysis. While GraphHRRR underperforms in its current form, it lays the groundwork for future probabilistic graph-based forecasting. Overall, HRRRCast represents a step toward efficient, data-driven regional weather prediction with competitive accuracy and ensemble capability. Introduction Recent advances in machine learning weather prediction (MLWP) have shown great promise in complementing or even replacing traditional numerical weather prediction (NWP) systems, particularly at global scales. Several studies have demonstrated that data-driven models can rival the skill of physics-based models at a fraction of the computational cost, enabling applications such as ensemble forecasting and climate downscaling with greater efficiency [2, 12, 13, 23, 18, 17]. However, while progress in global MLWP is substantial, the transition to high-resolution regional forecasting-especially at convection-allowing scales (km-scale) - remains an active area of research. These authors have made equal contributions.

Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence of herbicide resistant weeds. To address these challenges, we developed a vision guided, AI-driven variable rate sprayer system capable of detecting weed presence, estimating canopy size, and dynamically adjusting nozzle activation in real time. The system integrates lightweight YOLO11n and YOLO11n-seg deep learning models, deployed on an NVIDIA Jetson Orin Nano for onboard inference, and uses an Arduino Uno-based relay interface to control solenoid actuated nozzles based on canopy segmentation results. Indoor trials were conducted using 15 potted Hibiscus rosa sinensis plants of varying canopy sizes to simulate a range of weed patch scenarios. The YOLO11n model achieved a mean average precision (mAP@50) of 0.98, with a precision of 0.99 and a recall close to 1.0. The YOLO11n-seg segmentation model achieved a mAP@50 of 0.48, precision of 0.55, and recall of 0.52. System performance was validated using water sensitive paper, which showed an average spray coverage of 24.22% in zones where canopy was present. An upward trend in mean spray coverage from 16.22% for small canopies to 21.46% and 21.65% for medium and large canopies, respectively, demonstrated the system's capability to adjust spray output based on canopy size in real time. These results highlight the potential of combining real time deep learning with low-cost embedded hardware for selective herbicide application. Future work will focus on expanding the detection capabilities to include three common weed species in South Dakota: water hemp (Amaranthus tuberculatus), kochia (Bassia scoparia), and foxtail (Setaria spp.), followed by further validation in both indoor and field trials within soybean and corn production systems.