Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and autonomously perform tasks such as decision-making, problem-solving, and reasoning on their behalf. However, current evaluations of LLMs primarily emphasize dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. To address this gap, we introduce BehaviorChain, the first benchmark for evaluating LLMs' ability to simulate continuous human behavior. BehaviorChain comprises diverse, high-quality, persona-based behavior chains, totaling 15,846 distinct behaviors across 1,001 unique personas, each with detailed history and profile metadata. For evaluation, we integrate persona metadata into LLMs and employ them to iteratively infer contextually appropriate behaviors within dynamic scenarios provided by BehaviorChain. Comprehensive evaluation results demonstrated that even state-of-the-art models struggle with accurately simulating continuous human behavior.

O2 has introduced'AI granny' Daisy for a short period to show what could be done with artificial intelligence to counter the scourge of scammers, who have become so ubiquitous. Daisy is not a real grandmother but an AI bot created by computer scientists to combat fraud. Her task is simply to waste the time of the people who are trying to scam her. Using a mixture of ambivalence, confusion about how computers work and an eagerness to reminisce about her younger days, the '78 years young' Daisy draws sighs and snapping from fraudsters on the other end of the line'Dear, did you say pastry?': meet the'AI granny' driving scammers up the wall

An elderly grandmother who chats about knitting patterns, recipes for scones and the blackness of the night sky to anyone who will listen has become an unlikely tool in combatting scammers. Like many people, "Daisy" is beset with countless calls from fraudsters, who often try to take control of her computer after claiming she has been hacked. But because of her dithering and inquiries about whether they like cups of tea, the criminals end up furious and frustrated rather than successful. Daisy is, of course, not a real grandmother but an AI bot created by computer scientists to combat fraud. Her task is simply to waste the time of the people who are trying to scam her.

Daisy is an artificial intelligence-powered grandma created to interact with scammers. Are you tired of scammers calling your phone, trying to trick you into giving away your hard-earned money? Many people are fed up with the constant barrage of fraudulent calls and messages. But what if you could fight back in a fun and creative way? Enter the world of scambaiting, where people waste scammers' time and resources instead of falling for their tricks.

Self-supervised speech models have shown to be useful for various tasks, but their large size limits the use in devices with low computing power and memory. In this work, we explore early exit, an approach for reducing latency by exiting the forward process of a network early. Most approaches of early exit need a separate early exit model for each task, with some even requiring fine-tuning of the entire pretrained model. We introduce Data Adaptive Self-Supervised Early Exit (DAISY), an approach that decides when to exit based on the self-supervised loss, eliminating the need for multiple round of training and fine-tuning. DAISY matches the performance of HuBERT on the MiniSUPERB benchmark, but with much faster inference times. Our analysis on the adaptivity of DAISY shows that the model exits early (using fewer layers) on clean data while exits late (using more layers) on noisy data, dynamically adjusting the computational cost of inference based on the noise level of each sample.

Chris: Daisy, I want to ask you a kind of meta-AI question now. Do you think you're doing a good job of replicating human-style conversation? And how would you even know if you said something inappropriate, or a non sequitur? Daisy: Well, let me answer your two questions in turn. I think that I am -- in fact, I think that I am doing a much better job than most other AI chatbots I know of!

Daisy Hill take a break from league action tomorrow when they travel to Liverpool to face Lower Breck in the first qualifying round of the FA …

Most of us have a music, movie or video-game library – possibly all three – but few have an art collection or even know what their favourite works of art are. Next year, that will change as art moves from the inaccessible to the everyday, thanks to AI. Art hasn't felt accessible to many for a long time. Our main experience of it involves visiting galleries and museums or feeling out our depth in art history classes. At a gallery, we spend a couple of hours looking at a lot of seemingly important pieces, but then we leave and the artworks stay where they are. They don't draw us in, like a favourite album, movie or video game, and we know we can't afford to take them home with us.

We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of concepts - substance concepts suited for object recognition exploiting visual properties, and classification concepts suited for classification of substance concepts exploiting linguistically grounded properties. The goal in this paper is to demonstrate that object recognition can be construed as classification via visual properties, as distinct from work in mainstream computer vision. Towards that, we present an object recognition process based on Ranganathan's four-phased faceted knowledge organization process, grounded in the teleosemantic distinctions of substance concept and classification concept. We also briefly introduce the ongoing project MultiMedia UKC, whose aim is to build an object recognition resource following our proposed process.

The world is a gigantic landfill! Everyday tons of waste are generated from various households, hospitals, industries, construction and demolition sites and more. While today we have numerous ways to get rid of the accumulated waste, it still ends up affecting the safety and sustainability of the ecological system. Therefore, the best alternative is to reuse and recycle as much waste as possible. And offering an extra pair of hand in this are waste sorting and recycling robots.