MAROKO133 Hot ai: US Military Tested Havana Syndrome Weapon on Large Mammals, Whistleblowe

📌 MAROKO133 Update ai: US Military Tested Havana Syndrome Weapon on Large Mammals,

Sprawling revelations about so-called Havana Syndrome show no signs of going away.

Rumors of the alleged neurological condition — and the mythological spy-weapon that might cause it — have begun re-circulating in recent months, stirred up by early 2026 reporting that the US government paid as much as eight figures to procure a mysterious device linked to the ailment.

Now, ex-government whistleblowers are adding fuel to Havana Syndrome’s smoldering embers. In a baffling interview on CBS’ 60 Minutes, former CIA officers and conveniently anonymous government officials chimed in on the conversation, demanding answers and blasting the government for initiating what they call a massive coverup.

According to CBS‘ sources, a Havana Syndrome weapon has been in the hands of a US military lab for over a year. The weapon — small enough to be held by a person, but powerful enough to blast through windows and drywall to hit a target hundreds of feet away — had allegedly even been tested on rats and sheep. Those tests, CBS insists, show injuries “consistent with those seen in humans.” (An interesting framing, given that symptoms of Havana Syndrome aren’t even consistent among humans.)

One of the former agents who agreed to give his name was Marc Polymeropoulos, who according to CBS, spent 26 years “involved in every covert action program in the Middle East.”

In the segment, Polymeropoulos criticized a previous Intelligence Community Assessment from 2023, which found it was “very unlikely” the neurological symptoms were caused by a foreign adversary, as well as the government’s refusal to take Havana Syndrome seriously.

“I did some very interesting things for the US government, always with the idea that they would have my back if I got jammed up,” the CIA officer said. “I just needed to get medical care when I came back, and they wouldn’t even do that. So this moral injury, this sense of betrayal is so acute with me. That’s something that I can never forgive them for.”

The scientific community remains divided. No peer-reviewed study has confirmed the existence of such a weapon in the hands of US adversaries, though a government researcher in Norway reportedly assembled something close, earning himself a visit from the US State Department.

And while anything is possible with regard to Havana Syndrome, there’s a much simpler explanation for the 60 Minutes piece: CBS‘ new editor-in-chief Bari Weiss.

Though Weiss styles herself as a radical centrist, her unprecedented takeover of one of the US’s biggest newsrooms has drawn effusive praise from Donald Trump. For her part, Weiss has already proven to be a happy stenographer for the White House, pulling a bombshell segment from 60 Minutes that painted the administration in a bad light.

It may just be a coincidence that the “Havana Syndrome gun” materialized right when Trump began itching for a fight with foreign adversaries like Venezuela, Iran, and Cuba. And it could just be a stroke of luck that this fear mongering dispatch happens to trace back to Bari Weiss’s CBS.

One thing it’s not, however, is independently verified — and until it is, you’ll have to excuse our raised eyebrows.

More on government weapons: Pentagon Refuses to Say If AI Was Used to Select Elementary School as Bombing Target

The post US Military Tested Havana Syndrome Weapon on Large Mammals, Whistleblowers Says appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Update ai: ByteDance Introduces Astra: A Dual-Model Architecture for A

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