MAROKO133 Breaking ai: People Are Starting to Get Divorced Because of Affairs With AI Terb

📌 MAROKO133 Hot ai: People Are Starting to Get Divorced Because of Affairs With AI

Say what you will about AI chatbots, but there’s no question they’re now affecting the real world — including imploding your marriage.

We’ve talked pretty extensively about all the alarming ways that AI is disrupting people’s personal lives, including serving as a delusion-prone person’s own little cult leader, a friend or romantic companion for lonely teens and adults, and as a chaotic therapist that’s blowing up people’s marriages.

But can a spouse “cheat” on you with an AI companion? That’s the big question hanging over marriage law right now, Wired reports, as more and more disgruntled partners are citing their significant other’s AI paramour as grounds for a divorce.

“The law is still developing alongside these experiences,” divorce attorney Rebecca Palmer told the magazine. “But some people think of it as a true relationship, and sometimes better than one with a person.”

Palmer’s firm has worked with clients who have gotten or are seeking a divorce because of their partner cheating on them with AI, including an ongoing case in which the accused spouse blew money on — and, astoundingly, shared private information like bank accounts and social security numbers with — a chatbot.

The conundrum is giving judges a headache, as they already “struggle with what to do about affairs with humans,” Palmer added.

Raising the stakes — and also complicating the path to reaching a broader consensus on this emerging issue — in some states, cheating on a spouse is literally illegal. In Michigan, Wisconsin, and Oklahoma, adultery is a felony charge punishable with up to five years in prison or a fine up to $10,000, Wired noted.

Should a partner’s AI obsession, for example, be justification for losing the kids? In custody battles, “it is conceivable and likely” that judges would call a parent’s judgment into question if “they’re having intimate discussions with a chatbot,” Palmer told Wired, which also “brings into question how they are spending time with their child.”

Elizabeth Yang, a family law attorney in California, predicts that we’ll see a boom in divorce filings as more people fall for their AI lovers, similar to how there was an uptick in divorces during the COVID pandemic, she said.

“As [AI] continues improving, becoming more realistic, compassionate, and empathetic, more and more people in unhappy marriages who are lonely are going to be going to seek love with a bot,” Yang told Wired.

Yang’s prediction appears to be on the money. Wired notes that in the UK, a partner’s emotional attachment to an AI chatbot has already become a more common factor in a divorce, according to data from Divorce-Online.

It’s hard to say which way the winds will blow. But some legislators are already trying to draw a line in the sand. Ohio, for example, is attempting to ban human-AI marriages by affirming that AIs are “nonsentient entities” that do not have personhood.

More on AI: Perplexity CEO Warns That AI Girlfriends Can Melt Your Brain

The post People Are Starting to Get Divorced Because of Affairs With AI appeared first on Futurism.

🔗 Sumber: futurism.com

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

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