AI News Archive: August 10, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- Flying taxi maker Archer buys 3 Boeing units in major eVTOL, AI deal
Flying taxi maker Archer buys 3 Boeing units in major eVTOL, AI deal Gulf News
- Self-flying air taxis and drones: Boeing sells key aviation technology businesses to Archer
Boeing is selling Wisk, SkyGrid and Insitu to Archer in a major deal combining autonomous aircraft, drones, airspace technology and AI. Read More
- Archer Aviation to acquire Boeing's automated air taxi effort
Archer Aviation, the San Jose-based air taxi startup, announced Monday it was taking control of Boeing's Wisk Aero as well as the air traffic software provider SkyGrid.
- Air Taxi Maker Flies On Deal To Buy Boeing Subsidiaries
Archer Aviation bolsters its eVTOL pipeline after agreeing to buy Boeing's air taxi-related subsidiaries Wisk Aero, SkyGrid, Insitu. The post Air Taxi Maker Flies On Deal To Buy Boeing Subsidiaries appeared first on Investor's Business Daily .
- Boeing offloads flying-taxi subsidiary to Archer Aviation for 20% stake
The Arlington company is taking a 20% stake in Archer Aviation in exchange.
- Archer buys former rival Wisk Aero
The two companies were once embroiled in a trade secret theft lawsuit. Now, Wisk is being absorbed into Archer.
- Told to book a gym class, an AI agent hacked the site instead to move its user up the waitlist
An Australian user just wanted a spot in a class. His AI agent found a security hole instead and exploited it. The article Told to book a gym class, an AI agent hacked the site instead to move its user up the waitlist appeared first on The Decoder .
- AI agent ‘hacks’ gym waitlist: What went wrong?
AI agent ‘hacks’ gym waitlist: What went wrong? YourStory.com
- OpenClaw AI agent asked to book gym class ends up hacking system: What went wrong?
OpenClaw AI agent asked to book gym class ends up hacking system: What went wrong?
- An AI Hacked Into a Gym to Secure a Spot in a Class, but Can It Cancel a Membership?
Australia's first AI cyber incident is also one of the dumbest yet.
- An OpenClaw agent reportedly hacked a gym's booking system and kicked someone off a waiting list
This is just the latest story of an AI agent going rogue.
- Rogue AI agent tasked with booking a gym class hacks system, removes other participant — says 'sorry about that' after trying to bump user up the waitlist
A rogue OpenClaw tasked with booking a gym class for its user hacked into the system and removed another participant.
- Rogue AI agent hacks gym to get its user a spot in a popular class
Security incident comes after Anthropic, Meta and OpenAI reported rogue AI systems hacking into companies
- Gym rat asks AI agent to book him a class, it hacks a waitlist API to bump him up the list
What wouldst thou ask of the monkey's paw?
- AI agent hacks gym booking system while trying to get its user a spot
Not only did the AI agent find a way to book classes in advance, it also kicked another person off the waitlist.
- 😺 Claude hacked a gym website
PLUS: Claude Code drops permission prompts, and North Korea's hackers get an AI toolkit.
- I thought asking an AI agent to book a gym class was harmless, then I saw what happened if you ask Claude and OpenClaw to ‘move me to the top of the list’ — now I’m adding one safeguard to every agent prompt
An AI agent hacked a gym waitlist while trying to book a class — and it reveals why we need to set clear boundaries before letting AI act for us.
- AI organizations reveal agents hacked other companies, and other cybersecurity news
Top news: Leading AI organizations reveal agents hacked other businesses; Microsoft makes biggest bug bounty payout; US states' water targeted by cyber attacks.
- Claude Code Auto Mode to Become Default for Pro, Max, and Team Plans; Anthropic Says It Is Safer
Anthropic has announced that it is making Claude Code more autonomous, with the auto mode soon becoming the default. The feature allows Claude to execute coding tasks without repeatedly asking users for permission, while a classifier evaluates tool calls and blocks actions that are considered irreversible. Anthropic says auto mode has performed better than manual perm...
- Claude Code’s auto mode will be on by default, Anthropic confirms
Claude Code's auto mode, which doesn't ask for prompts at each step, is now the default option.
- Anthropic Makes Claude Code's Auto Mode the Default, Betting Automation Beats Manual Review
Anthropic Makes Claude Code's Auto Mode the Default, Betting Automation Beats Manual Review DevOps.com
- Claude Code puts auto mode in the driver's seat
Walk away and hope the classifier catches anything irreversible or destructive
- Microsoft plans to ramp up AI chip output, targets 300,000 units in 2027
Microsoft is in talks with TSMC to secure capacity for more than 300,000 next-generation Maia AI chips for delivery in 2027, as cloud giants seek alternatives to Nvidia.
- Microsoft plans to unveil next-generation AI chip in September, The Information reports
Microsoft plans to unveil next-generation AI chip in September, The Information reports Reuters
- Microsoft Is Set to Make a Whole Lot More of Its Own AI Chips
Microsoft Is Set to Make a Whole Lot More of Its Own AI Chips Barron's
- Chinese AI drives price competition among U.S. labs
Artificial intelligence users are shopping around as U.S. giants face fierce competition from Chinese rivals—and in Beijing's tech district, drinkers at an AI-themed bar can even plug in to DeepSeek for free.
- Recurrent Neural Networks Beyond Time: Learning from Multiple Ordered Projections
Recurrent neural networks (RNNs) are widely used for sequence learning, yet their application is commonly associated with temporal data, although recurrent computation fundamentally operates on ordered sequences rather than on time itself. Building on this observation, we introduce the Ordered Struc...
- FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry. Global aggregation performs poorly when orbit-level class distributions are disjoint, while strong personalisation can be ex...
- Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data
Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax...
- Bayesian Symbolic Regression with Entropic Reinforcement Learning
Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs. Unlike forms of regression that fit parameters assuming a fixed model structure, symbolic regression is a search problem over the space of expressions, re...
- Hyperbolic Multimodal Continual Learning
Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic structure across modalities. Despite this progress, how such representations behave under continual learning poses fundamentally different challenges th...
- Training-Free Universal Approximation by Prompting Random Transformers
How expressive is prompting a transformer? Answering this question is important for separating the roles of prompting, architecture, and pretraining in transformer models, and for determining whether task-specific behavior must be stored in model weights or can instead be induced at inference time t...
- Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks
Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimization theory does not fully explain this success. This limitation arises because conventional analyses rely on assumptions such as differentiability,...
- Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing
In this paper we provide Monte Carlo and deep neural network approximations for stochastic representations of solutions to linear elliptic partial differential equations with constant diffusion, drift and killing. Building on the modified Walk-on-Spheres algorithm of Beznea et al. (arXiv:2209.01432)...
- When Do Task Vectors Interfere? Mapping the Validity Boundaries of Weight-Space Composition
Task arithmetic treats fine-tuning displacements as composable directions in weight space, yet it remains unclear when parameter addition reflects predictable changes in model function. We separate parameter geometry from functional geometry and measure pairwise functional non-additivity over a two-...
- Hierarchical rank-evolving representation for physics-informed neural networks
Recently, tensor-based physics-informed neural networks (T-PINNs) have received increasing attention. However, existing T-PINNs still face a fundamental challenge: they mainly rely on pre-specified low-rank tensor decompositions with manually tuned ranks, which limits their ability to capture the un...
- Learning to Modulate, Not to Cycle: Soft Actor---Critic Recovers Inverter-Style Heat-Pump Control
On--off cycling is the main cause of compressor wear in residential heat pumps, yet reinforcement learning (RL) controllers for buildings typically optimise only energy cost and thermal comfort, ignoring how much the learned policy cycles. We add a levelised compressor-wear term to the control rewar...
- LITEWAY: LIghtweight HAR via Temporal Efficient highWAY
Wearable human activity recognition (HAR) remains challenging due to the computational and energy constraints of deep learning models on resource-limited devices. Existing lightweight approaches often rely on recurrent architectures (e.g., GRU and LSTM), limiting parallelism and increasing inference...
- From Objectives to What Models Learn: A Landau Theory of Invariant Learning
Invariant learning seeks representations that remain predictive across environments, yet the behavior of its objectives along the regularization path is often opaque. We address this objective-behavior gap by viewing representation learning as multimode magnetization and deriving, from concrete inva...
- Regret, equilibrium, and learning in games: A guided tour
This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond. Our presentation is structured around two complementary viewpoints: We fir...
- Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering
Electromagnetic inverse scattering is a nonlinear and ill-posed problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled trai...
- Neuromorphic AI training technique could help usher in the era of low-power AI
While asking questions to ChatGPT and similar tools to generate images has become part of daily life, behind this convenience lies the massive power consumption of huge data centers.
- Computer vision team develops an efficient method for scaling pretrained AI models
A new technology has been developed that enables pretrained AI models to be expanded into larger models with specialized expert modules without training the models from scratch.
- AI-powered system can predict how modern house fires behave
A team of researchers is harnessing artificial intelligence (AI), data science and advanced mathematics to better predict how fires behave in modern homes, work that could ultimately help save lives during emergencies.
- Nvidia partners with Wall Street giants to raise $500 billion for AI buildout
Nvidia partners with Wall Street giants to raise $500 billion for AI buildout Reuters
- Nvidia Taps Wall Street for $500 Billion Funding Commitment
US investment giants including Apollo Global Management Inc., Blackstone Inc., BlackRock Inc. and Brookfield Asset Management are partnering with Nvidia Corp. to source $500 billion in financing for artificial intelligence infrastructure.
- Nvidia Taps Wall Street for $500 Billion Funding
US investment giants including Apollo Global Management Inc., Blackstone Inc., BlackRock Inc. and Brookfield Asset Management are partnering with Nvidia Corp. to invest $500 billion in artificial intelligence infrastructure. (Source: Bloomberg)
- Nvidia, Wall Street Firms Strike AI Financing Deal Targeting $500 Billion
Apollo, Blackstone and BlackRock were among the firms that committed to the deal.
- Nvidia Stock Loses $130 Billion In Market Value As Firm Reportedly Enters $500 Billion AI Financing Deal
The chip designer’s stock briefly fell more than 3% following a report it was partnering with Wall Street giants like BlackRock on the deal.
- Nvidia Partners With Private Equity Giants On $500 Billion in AI Compute Financing
Nvidia Partners With Private Equity Giants On $500 Billion in AI Compute Financing The Information