📌 TOPINDIATOURS Hot ai: TikTok deal sealed: Trump gives US investors 80% control,

President Donald Trump signed an executive order Thursday to move TikTok into American hands. The deal will give US companies control of the app’s algorithm and majority ownership, ending months of uncertainty over its future in the country.

Under the plan, American investors will hold about 80% of the US version of TikTok. ByteDance and Chinese investors will retain less than 20%.

A new seven-member board will oversee the company, with six Americans and one foreign director. Cybersecurity and national security experts will fill the board seats, the White House said.

Oracle will take charge of TikTok’s US operations and provide cloud services to store user data. The company and its co-founder Larry Ellison will also control the recommendation algorithm under a licensed agreement. White House officials stressed that ByteDance and Chinese authorities will not have access to American user data.

Other investors include private equity firm Silver Lake, media mogul Rupert Murdoch and his son Lachlan, and Dell CEO Michael Dell. Trump said the investors are “all very well-known people, very famous people actually, financially.”

Vice President JD Vance confirmed the new US-controlled algorithm will dictate what users see in their feeds. When asked about whether it could push political content, Trump said, “I always like MAGA-related.” He added, “If I could make it 100% MAGA, I would, but it’s not going to work out that way, unfortunately.” He later clarified, “No, everyone’s going to be treated fairly.”

Long battle over ban ends

TikTok had faced a looming ban after Congress passed a bipartisan law last year forcing ByteDance to divest.

Lawmakers cited national security concerns, including possible data sharing with Beijing and manipulation of public opinion. The Supreme Court upheld the law in January, clearing the way for enforcement.

The app briefly went offline hours before Trump’s second inauguration. But the president signed an executive order on his first day in office to delay the ban.

He continued extending the deadline, giving negotiators more time to strike a deal.

Earlier this month, he granted TikTok until December 16 to finalize terms with US investors and Chinese authorities.

Trump credited his relationship with Chinese President Xi Jinping for helping close the agreement.

“I have great respect for President Xi, and I very much appreciate that he approved the deal, because to get it done properly, we really needed the support of China and the approval of China,” Trump said during the signing.

He also described a recent phone call with Xi as “a very good one.” He said the two leaders plan to meet at the APEC summit.

TikTok remains one of the most widely used apps in the US, with about 180 million users.

Trump himself joined the platform last year to reach younger voters. He has credited it with helping him win the 2024 election.

The president also said the deal protects small businesses that rely on TikTok for advertising. “Small businesses have become very successful because of TikTok, and we didn’t think of that,” he said.

The US government will collect a multibillion-dollar fee from the investor group. Trump described it as a “tremendous fee-plus,” and added, “I don’t want to throw that out the window.”

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Breaking ai: ByteDance Introduces Astra: A Dual-Model Architecture

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