MAROKO133 Breaking ai: War Profiteers Furious After Polymarket Refuses to Pay Out on Venez

📌 MAROKO133 Breaking ai: War Profiteers Furious After Polymarket Refuses to Pay Ou

Nearly a week after US strikes bombarded Venezuela during the kidnapping of its president Nicolás Maduro, betting platform Polymarket is refusing to pay out users who gambled on the invasion of Venezeula.

Polymarket is a controversial prediction market where users can gamble on the outcome of real-world events, like who will win a certain election, or which side will control a certain village in the Ukraine-Russo war by a pre-specified date.

In this particular wager, Polymarket was supposed to pay out if “the United States commences a military offensive intended to establish control over any portion of Venezuela,” a determination to be made by “a consensus of credible sources,” the New York Times reported. To those who wagered on such an outcome, the events of January 3rd were proof positive that an invasion had taken place.

Instead, Polymarket came out with a statement arguing the opposite.

“This market refers to US military operations intended to establish control,” the note declares. “President Trump’s statement that they will ‘run’ Venezuela while referencing ongoing talks with the Venezuelan government does not alone qualify the snatch-and-extract mission to capture Maduro as an invasion.”

Those looking to cash in on the brutal attacks — which have killed at least 100 people so far — weren’t too impressed with the platform’s semantical wit.

“Pretty sure this should be yes after Trump said approximately 20 times in his press conference that the US now controls Venezuela,” groused a bettor with $1,876 wagered on a US invasion of Venezuela by March 31st. “We are going to run the country,” another user exclaimed.

“They think there will be no punishment for all this fraud. They are openly changing the rules and manipulating the market,” one user who bet $123 on the outcome complained. “A major investigation file is being prepared regarding Polymarket by the US Department of Justice. Don’t worry.”

Betting market participants relitigating results they don’t like is quickly becoming a pattern in the sector, with similar debates playing out over Time magazine’s “Person of the Year” pick and Elon Musk’s overly ambitious promises about Tesla’s self-driving taxi program.

Still, the latest incident heaps drama on Venezuela’s role in betting markets; it comes amidst some pretty compelling allegations that an insider with advanced knowledge of the US attack on Venezuela scored over $400,000 betting similar lines. (The alleged insider’s bets won all their bets.)

In this case however, every bettor who thought they were crowdsourcing the truth instead got a time honored lesson: no matter what you’re betting over, the house always wins.

More on Venezuela: Trump’s Air Strikes Targeted a Scientific Research Institute, Venezuela Says

The post War Profiteers Furious After Polymarket Refuses to Pay Out on Venezuelan Invasion Bets appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Eksklusif ai: ByteDance Introduces Astra: A Dual-Model Architecture fo

The increasing integration of robots across various sectors, from industrial manufacturing to daily life, highlights a growing need for advanced navigation systems. However, contemporary robot navigation systems face significant challenges in diverse and complex indoor environments, exposing the limitations of traditional approaches. Addressing the fundamental questions of “Where am I?”, “Where am I going?”, and “How do I get there?”, ByteDance has developed Astra, an innovative dual-model architecture designed to overcome these traditional navigation bottlenecks and enable general-purpose mobile robots.

Traditional navigation systems typically consist of multiple, smaller, and often rule-based modules to handle the core challenges of target localization, self-localization, and path planning. Target localization involves understanding natural language or image cues to pinpoint a destination on a map. Self-localization requires a robot to determine its precise position within a map, especially challenging in repetitive environments like warehouses where traditional methods often rely on artificial landmarks (e.g., QR codes). Path planning further divides into global planning for rough route generation and local planning for real-time obstacle avoidance and reaching intermediate waypoints.

While foundation models have shown promise in integrating smaller models to tackle broader tasks, the optimal number of models and their effective integration for comprehensive navigation remained an open question.

ByteDance’s Astra, detailed in their paper “Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning” (website: https://astra-mobility.github.io/), addresses these limitations. Following the System 1/System 2 paradigm, Astra features two primary sub-models: Astra-Global and Astra-Local. Astra-Global handles low-frequency tasks like target and self-localization, while Astra-Local manages high-frequency tasks such as local path planning and odometry estimation. This architecture promises to revolutionize how robots navigate complex indoor spaces.

Astra-Global: The Intelligent Brain for Global Localization

Astra-Global serves as the intelligent core of the Astra architecture, responsible for critical low-frequency tasks: self-localization and target localization. It functions as a Multimodal Large Language Model (MLLM), adept at processing both visual and linguistic inputs to achieve precise global positioning within a map. Its strength lies in utilizing a hybrid topological-semantic graph as contextual input, allowing the model to accurately locate positions based on query images or text prompts.

The construction of this robust localization system begins with offline mapping. The research team developed an offline method to build a hybrid topological-semantic graph G=(V,E,L):

• V (Nodes): Keyframes, obtained by temporal downsampling of input video and SfM-estimated 6-Degrees-of-Freedom (DoF) camera poses, act as nodes encoding camera poses and landmark references.
• E (Edges): Undirected edges establish connectivity based on relative node poses, crucial for global path planning.
• L (Landmarks): Semantic landmark information is extracted by Astra-Global from visual data at each node, enriching the map’s semantic understanding. These landmarks store semantic attributes and are connected to multiple nodes via co-visibility relationships.

In practical localization, Astra-Global’s self-localization and target localization capabilities leverage a coarse-to-fine two-stage process for visual-language localization. The coarse stage analyzes input images and localization prompts, detects landmarks, establishes correspondence with a pre-built landmark map, and filters candidates based on visual consistency. The fine stage then uses the query image and coarse output to sample reference map nodes from the offline map, comparing their visual and positional information to directly output the predicted pose.

For language-based target localization, the model interprets natural language instructions, identifies relevant landmarks using their functional descriptions within the map, and then leverages landmark-to-node association mechanisms to locate relevant nodes, retrieving target images and 6-DoF poses.

To empower Astra-Global with robust localization abilities, the team employed a meticulous training methodology. Using Qwen2.5-VL as the backbone, they combined Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). SFT involved diverse datasets for various tasks, including coarse and fine localization, co-visibility detection, and motion trend estimation. In the GRPO phase, a rule-based reward function (including format, landmark extraction, map matching, and extra landmark rewards) was used to train for visual-language localization. Experiments showed GRPO significantly improved Astra-Global’s zero-shot generalization, achieving 99.9% localization accuracy in unseen home environments, surpassing SFT-only methods.

Astra-Local: The Intelligent Assistant for Local Planning

Astra-Local acts as the intelligent assistant for Astra’s high-frequency tasks, a multi-task network capable of efficiently generating local paths and accurately estimating odometry from sensor data. Its architecture comprises three core components: a 4D spatio-temporal encoder, a planning head, and an odometry head.

The 4D spatio-temporal encoder replaces traditional mobile stack perception and prediction modules. It begins with a 3D spatial encoder that processes N omnidirectional images through a Vision Transformer (ViT) and Lift-Splat-Shoot to convert 2D image features into 3D voxel features. This 3D encoder is trained using self-supervised learning via 3D volumetric differentiable neural rendering. The 4D spatio-temporal encoder then builds upon the 3D encoder, taking past voxel features and future timestamps as input to predict future voxel features through ResNet and DiT modules, providing current and future environmental representations for planning and odometry.

The planning head, based on pre-trained 4D features, robot speed, and task information, generates executable trajectories using Transformer-based flow matching. To prevent collisions, the planning head incorporates a masked ESDF loss (Euclidean Signed Distance Field). This loss calculates the ESDF of a 3D occupancy map and applies a 2D ground truth trajectory mask, significantly reducing collision rates. Experiments demonstrate its superior performance in collision rate and overall score on out-of-distribution (OOD) datasets compared to other methods.

The odometry head predicts the robot’s relative pose using current and past 4D features and additional sensor data (e.g., IMU, wheel data). It trains a Transformer model to fuse information from different sensors. Each sensor modality is processed by a specific tokenizer, combined with modality embeddings and temporal positional embeddi…

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🔗 Sumber: syncedreview.com