AI chat toy company Bondu left its web console almost entirely unprotected. Researchers who accessed it found nearly all the conversations children had with the company's stuffed animals. Earlier this month, Joseph Thacker's neighbor mentioned to him that she'd preordered a couple of stuffed dinosaur toys for her children. She'd chosen the toys, called Bondus, because they offered an AI chat feature that lets children talk to the toy like a kind of machine-learning-enabled imaginary friend. But she knew Thacker, a security researcher, had done work on AI risks for kids, and she was curious about his thoughts.

Amazon has launched a web portal for Alexa+ at Alexa.com, providing early access users with a ChatGPT-like interface featuring suggested prompts and easy text copying capabilities. PCWorld reports the portal allows file uploads for document analysis and offers improved usability compared to previous voice-only interactions with Amazon's AI assistant. Alexa+ remains free during early access, with future plans to offer it free to Prime members while charging non-Prime users a monthly fee. It's been nearly a year since Amazon first launched the new, AI-enhanced Alexa+, but until now, a key feature has been missing: the ability to access and chat with Alexa+ on the web. Now it appears Amazon has fulfilled that promise, with an Alexa+ web portal finally going live--for at least some Alexa+ early access users, anyway--at Alexa.com.

Abstract-- Assigning tasks to robots often involves supplying the robot with an overarching goal, such as through natural language, and then relying on the robot to uncover and execute a plan to achieve that goal. In many settings common to human-robot interaction, however, the world is only partially observable to the robot, requiring that it create plans under uncertainty. Although many probabilistic planning algorithms exist for this purpose, these algorithms can be inefficient if executed with the robot's limited computational resources, or may require more steps than expected to achieve the goal. We thereby created a new, lightweight, probabilistic planning algorithm, Plan-Orchestrated Tree Assembly for Lookahead (POrTAL), that combines the strengths of two baseline planning algorithms, FF-Replan and POMCP . In a series of case studies, we demonstrate POrTAL's ability to quickly arrive at solutions that outperform these baselines in terms of number of steps. We additionally demonstrate how POrTAL performs under varying temporal constraints. The ability of modern robots to respond to arbitrary user requests has advanced considerably in recent years. This advancement is in large part due to robots' ability to autonomously plan their own actions. When receiving a goal such as "bring me a cup of coffee," for example, a robot can calculate the minimum number of steps required to achieve this goal: obtain the coffee grinds, proceeding to the coffee maker, load the grinds, and so on. In many scenarios common to human-robot interaction, however, this planning must be performed under considerable uncertainty.

Cooperative video games, where multiple participants must coordinate by communicating and reasoning under uncertainty in complex environments, yield a rich source of language data. We collect the Portal Dialogue Corpus: a corpus of 11.5 hours of spoken human dialogue in the co-op mode of the popular Portal 2 virtual puzzle game, comprising 24.5K total utterances. We analyze player language and behavior, identifying a number of linguistic phenomena that rarely appear in most existing chitchat or task-oriented dialogue corpora, including complex spatial reference, clarification and repair, and ad-hoc convention formation. To support future analyses of language use in complex, situated, collaborative problem-solving scenarios, we publicly release the corpus, which comprises player videos, audio, transcripts, game state data, and both manual and automatic annotations of language data.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A view of cleared lots and sparse construction after the Palisades fire in the Sunset Mesa neighborhood of eastern Malibu on Oct. 21. This is read by an automated voice. Please report any issues or inconsistencies here . Fire survivors can now access a portal with vetted residential templates designed to speed rebuilding and reduce costs, with homes potentially ready by 2026.

New consoles used to come out every five years - so where's the PlayStation 6? You used to be able to count the number of years between game consoles on one hand. The original Sony PlayStation came out in the UK in September 1995. Five years later, the PS2 was released and brought with it significant changes. It was a similar story for other consoles but, of late, things seem to have slowed down - which might explain why, as the PS5 hits its fifth anniversary, a potential PS6 is nowhere in sight.

Understanding user behavior on the web is increasingly critical for optimizing user experience (UX). This study introduces Augmented Web Usage Mining (AWUM), a methodology designed to enhance web usage mining and improve UX by enriching the interaction data provided by CAWAL (Combined Application Log and Web Analytics), a framework for advanced web analytics. Over 1.2 million session records collected in one month (~8.5GB of data) were processed and transformed into enriched datasets. AWUM analyzes session structures, page requests, service interactions, and exit methods. Results show that 87.16% of sessions involved multiple pages, contributing 98.05% of total pageviews; 40% of users accessed various services and 50% opted for secure exits. Association rule mining revealed patterns of frequently accessed services, highlighting CAWAL's precision and efficiency over conventional methods. AWUM offers a comprehensive understanding of user behavior and strong potential for large-scale UX optimization.

Animist worldviews treat beings, plants, landscapes, and even tools as persons endowed with spirit, an orientation that has long shaped human-nonhuman relations through ritual and moral practice. While modern industrial societies have often imagined technology as mute and mechanical, recent advances in artificial intelligence (AI), especially large language models (LLMs), invite people to anthropomorphize and attribute inner life to devices. This paper introduces A(I)nimism, an interactive installation exploring how large language objects (LLOs) can mediate animistic relationships with everyday things. Housed within a physical 'portal', the system uses GPT-4 Vision, voice input, and memory-based agents to create evolving object-personas. Encounters unfold through light, sound, and touch in a ritual-like process of request, conversation, and transformation that is designed to evoke empathy, wonder, and reflection. We situate the project within anthropological perspectives, speculative design, and spiritual HCI. AI's opacity, we argue, invites animistic interpretation, allowing LLOs to re-enchant the mundane and spark new questions of agency, responsibility, and design.

This paper introduces TrueGradeAI, an AI-driven digital examination framework that directly addresses the shortcomings of traditional paper assessments, namely excessive paper usage, logistical complexity, grading delays, and evaluator bias. The system preserves natural handwriting by capturing stylus input on secure tablets and applying transformer-based optical character recognition for transcription. Evaluation is performed through a retrieval-augmented pipeline that integrates faculty solutions, cache layers, and external references, enabling a large language model to assign scores with explicit, evidence-linked reasoning. By combining handwriting preservation with scalable and transparent evaluation, the framework reduces environmental costs, accelerates feedback cycles, and progressively builds a reusable knowledge base, while explicitly working to mitigate grading bias and ensure fairness in assessment. Despite advances in digital infrastructure, most institutions continue to rely on paper-based examinations as their primary mode of assessment.