MAROKO133 Breaking ai: Banning Phones in Schools Is Drastically Changing the Behavior of K

📌 MAROKO133 Breaking ai: Banning Phones in Schools Is Drastically Changing the Beh

Over the past few years, a huge number of schools in the United States and around the world have banned cell phone use among their students.

It’s a divisive topic, and the effects are only starting to come into focus. Just look at New York State, where governor Kathy Hocul and lawmakers put a ban into the state budget last spring in an effort to give kids a break from distractions at school.

Gothamist spoke to students about their experience with the ban, and the number one takeaway didn’t have to do with anything to do with hot-button topics like social media addiction or cyberbullying. Instead, it was that kiboshing phones is forcing kids to actually talk to each other in meatspace again — and it’s making schools way noisier, for better or worse.

“Sometimes I would take naps in the lunchroom, but now I can’t because of the noise,” 15-year old Queens high school student Jimena Garcia told the site.  “But it’s fun.”

That’s a bold contrast, the Gothamist reported, from previous semesters where kids sat in the lunchroom silently on the phones, creating an environment where you could “hear a pin drop.”

“I do like how this phone ban is allowing students to just connect with each other, make new friendships,” Alyssa Ko, the 17-year old class president at Garcia’s school, told Gothamist. “Because some people use their phone to just hide away.”

With some exceptions for students with disabilities or those learning English and needing a translation app, the ban prohibits all internet-enabled devices throughout the entirety of the school day. As of now, at least 31 states and Washington DC have implemented some sort of restrictions on cell phone usage in schools.

Parents have pushed back state for reasons ranging from concern about getting in touch with their kids in an emergency to good old-fashioned helicopter parenting, but teachers have been largely supportive, saying that phones have become an all-encompassing distraction in educational settings.

When educators were surveyed by the New York State United Teachers, for instance, the results were promising. Eighty-nine percent of respondents said the new policies have improved the school environment, 76 percent said class participation has improved, and 77 percent reported more positive social interactions both within classrooms and through hallways. 

“Now when we get computers, I actually have to [do] deep research instead of going straight to AI,” another NYC student told Gothamist. 

Not all students appreciate the bans, of course. aren’t embraced or appreciated by all. Enakshi Barua, 14, is opposed on principle.

“I feel like the trust isn’t there between the students and teachers,” 14-year-old Enakshi Barua told Gothamist. “So I feel like that should be built instead of banning the phones.” 

Perhaps the sweetest of the changes: analog activities are back, like passing notes in class, writing cards to crushes, and taking Polaroid pictures.

“There are just a lot of memories that we make throughout high school that we want to capture,” Ko told Gothamist. “I actually have a lot of Polaroids on my wall.”

More on screen time: Blocking the Internet on People’s Phones for Two Weeks Led to Profound Changes in Mental Health and Attention Span

The post Banning Phones in Schools Is Drastically Changing the Behavior of Kids 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