MAROKO133 Update ai: Details Emerge About OpenAI’s “Adult Mode” Edisi Jam 15:16

📌 MAROKO133 Update ai: Details Emerge About OpenAI’s “Adult Mode” Wajib Baca

In October, ChatGPT maker OpenAI announced it would be opening the floodgates for “mature apps.”

“Now that we have been able to mitigate the serious mental health issues and have new tools, we are going to be able to safely relax the restrictions in most cases,” he tweeted at the time.

“As part of our ‘treat adult users like adults’ principle, we will allow even more, like erotica for verified adults,” Altman added.

Five months later, an “adult mode” chatbot that’s willing to breach topics that have been off-limits on ChatGPT so far — a move characterized by critics as a way to boost revenue in light of some disastrous financials — remains nowhere to be seen.

And as the Wall Street Journal reports, the subject is still sending a shiver down the spines of company advisors, who are wary of the many potential dangers of letting OpenAI’s already-hooked customers engage in intimately-charged conversations.

In fact, many staffers and executives were reportedly blindsided by Altman’s promise in the first place, making an imminent launch out of the question.

Despite plenty of concerns and internal debates over the risks, from users growing too emotionally attached to compulsive use, OpenAI is reportedly still forging ahead. (The company did admit earlier this month that adult mode’s launch would be delayed as other products were being prioritized.)

Plenty of glaring security issues remain, with inside sources telling the WSJ that its new age-prediction system has been misclassifying minors as adults 12 percent of the time. While that may not sound like much, multiplied by ChatGPT’s enormous user base, millions of underage children could be accessing inappropriate chats.

In an effort to keep nonconsensual sexual images off the platform — something competitor Elon Musk’s xAI has been unsuccessfully grappling with — OpenAI is playing it relatively safe by restricting spicy conversations to just text.

It’s also trying to control the narrative by painting its new feature as a way to generate something you’d find in romance novels. A spokeswoman told the WSJ that its erotica chats were more akin to “smut rather than pornography.”

The spokeswoman also assured that users will be encouraged to seek relationships in the real world.

But given the shaky track record of implementing effective guardrails and moderating explicit content — while Altman claims “serious mental health issues” are no longer a problem for OpenAI, a wealth of data suggests otherwise — it remains to be seen how OpenAI’s “adult mode” will fare.

If xAI’s Grok is anything to go by, the risks are considerable. Users have been using the chatbot to unclothe images of real people, resulting in a wave of nonconsensual pornographic images flooding the largely unmoderated social media site.

Its ongoing struggles with child sex abuse material (CSAM) culminated in a lawsuit on behalf of three teens, including two minors, which was filed in the Northern District of California today. The plaintiffs accuse xAI of fostering an environment that allowed for the spread of CSAM.

There have also been countless instances of users forming intense relationships with AI chatbots. Underage users are particularly vulnerable, often developing strong bonds without their parents’ knowledge.

In extreme cases, the phenomenon has been linked to a string of tragic teen suicides, culminating in several high-profile lawsuits aimed at OpenAI and its competitors.

In short, OpenAI is painfully aware of the risks in rolling out its “adult mode” feature.

According to the WSJ, though, it’s looking to launch in a month or so.

“We still believe in the principle of treating adults like adults,” the company told the newspaper, “but getting the experience right will take more time.”

More on OpenAI and smut: OpenAI Says It Will Move to Allow Smut

The post Details Emerge About OpenAI’s “Adult Mode” 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