This article presents Teledrive, a telepresence robotic system with embodied AI features that empowers an operator to navigate the telerobot in any unknown remote place with minimal human intervention. We conceive Teledrive in the context of democratizing remote care-giving for elderly citizens as well as for isolated patients, affected by contagious diseases. In particular, this paper focuses on the problem of navigating to a rough target area (like bedroom or kitchen) rather than pre-specified point destinations. This ushers in a unique AreaGoal based navigation feature, which has not been explored in depth in the contemporary solutions. Further, we describe an edge computing-based software system built on a WebRTC-based communication framework to realize the aforementioned scheme through an easy-to-use speech-based human-robot interaction. Moreover, to enhance the ease of operation for the remote caregiver, we incorporate a person following feature, whereby a robot follows a person on the move in its premises as directed by the operator. Moreover, the system presented is loosely coupled with specific robot hardware, unlike the existing solutions. We have evaluated the efficacy of the proposed system through baseline experiments, user study, and real-life deployment.

Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecSys struggles under cold scenarios with sparse user-item interactions, recent strategies have focused on leveraging modality information of user/items (e.g., text or images) based on pre-trained modality encoders and Large Language Models (LLMs). Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge. In this work, we propose an efficient All-round LLM-based Recommender system, called A-LLMRec, that excels not only in the cold scenario but also in the warm scenario. Our main idea is to enable an LLM to directly leverage the collaborative knowledge contained in a pre-trained state-of-the-art CF-RecSys so that the emergent ability of the LLM as well as the high-quality user/item embeddings that are already trained by the state-of-the-art CF-RecSys can be jointly exploited. This approach yields two advantages: (1) model-agnostic, allowing for integration with various existing CF-RecSys, and (2) efficiency, eliminating the extensive fine-tuning typically required for LLM-based recommenders. Our extensive experiments on various real-world datasets demonstrate the superiority of A-LLMRec in various scenarios, including cold/warm, few-shot, cold user, and cross-domain scenarios. Beyond the recommendation task, we also show the potential of A-LLMRec in generating natural language outputs based on the understanding of the collaborative knowledge by performing a favorite genre prediction task. Our code is available at https://github.com/ghdtjr/A-LLMRec .

Human annotation is a time-consuming task that requires a significant amount of effort. To address this issue, interactive data annotation utilizes an annotation model to provide suggestions for humans to approve or correct. However, annotation models trained with limited labeled data are prone to generating incorrect suggestions, leading to extra human correction effort. To tackle this challenge, we propose Araida, an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections. Araida involves an error-aware integration strategy that dynamically coordinates an annotation model and a k-nearest neighbors (KNN) model, giving more importance to KNN's predictions when predictions from the annotation model are deemed inaccurate. Empirical studies demonstrate that Araida is adaptable to different annotation tasks and models. On average, it reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.

Large Language Models (LLMs) have achieved remarkable success across various industries due to their exceptional generative capabilities. However, for safe and effective real-world deployments, ensuring honesty and helpfulness is critical. This paper addresses the question: Can we prioritize the helpfulness of LLMs while preserving their honesty? To begin with, we establish exhaustive principles aimed at guaranteeing the honesty of LLM. Additionally, we introduce a novel dataset, referred to as HoneSet, comprising 930 queries spanning six categories meticulously crafted to assess an LLM's capacity for maintaining honesty. Subsequently, we present two approaches to augmenting honesty and helpfulness in LLMs: a training-free enhancement and a fine-tuning-based improvement. The training-free approach, which is based on curiosity-driven prompting, empowers LLMs to articulate internal confusion and uncertainty regarding queries, thereby optimizing their responses. Conversely, the fine-tuning-based method employs a two-stage process inspired by curriculum learning: initially instructing LLMs to discern between honest and dishonest responses, then refining their training to enhance helpfulness. Experiments conducted on nine prominent LLMs demonstrate a significant improvement in alignment with honesty across all models through the implementation of our proposed enhancements. Particularly noteworthy is the 65.3% enhancement observed in Llama3-8b and the remarkable 124.7% improvement in Mistral-7b, as measured by the H$^{2}$ (honest and helpful) assessment. We believe that our work can pave the way for developing more trustworthy LLMs for real-world applications.

Reading fluency assessment is a critical component of literacy programmes, serving to guide and monitor early education interventions. Given the resource intensive nature of the exercise when conducted by teachers, the development of automatic tools that can operate on audio recordings of oral reading is attractive as an objective and highly scalable solution. Multiple complex aspects such as accuracy, rate and expressiveness underlie human judgements of reading fluency. In this work, we investigate end-to-end modeling on a training dataset of children's audio recordings of story texts labeled by human experts. The pre-trained wav2vec2.0 model is adopted due its potential to alleviate the challenges from the limited amount of labeled data. We report the performance of a number of system variations on the relevant measures, and also probe the learned embeddings for lexical and acoustic-prosodic features known to be important to the perception of reading fluency.

The Tribeca Film Festival will debut five short films made by AI, as detailed by The Hollywood Reporter. The shorts will use OpenAI's Sora model, which transforms text inputs into create video clips. This is the first time this type of technology will take center stage at the long-running film festival. "Tribeca is rooted in the foundational belief that storytelling inspires change. Humans need stories to thrive and make sense of our wonderful and broken world," said co-founder and CEO of Tribeca Enterprises Jane Rosenthal.

It took Billy Basso seven years to make Animal Well, the dense, dark Metroidvania game that crashed onto Steam's top-seller chart earlier this month amid a flurry of player hype. The game is a labyrinth exercise where players wander a world inhabited by sometimes friendly, sometimes not-friendly creatures as a small, very able blob. It's emblematic of what's possible with indie games--a breed that could be on the brink of extinction. Animal Well is light on instruction. Part of the game is figuring out how to play. It all but requires players to interact in Discord or Reddit communities when their puzzle-solving dead-ends.

Over the long weekend, Netflix co-CEO Ted Sarandos got a bit of a roasting for telling the New York Times' Lulu Garcia-Navarro that Barbie and Oppenheimer, whose combined global box office was 2.4 billion, "would have enjoyed just as big an audience on Netflix." It's easy to chuckle at Sarandos' comments, as it was when Zack Snyder told Joe Rogan that his movie Rebel Moon--Part One: A Child of Fire pulled in more viewers than Greta Gerwig's theatrical smash. But as Sarandos' interview was being mocked around the internet, movie theaters were experiencing their worst Memorial Day weekend in decades, led, just barely, by an underwhelming start for Furiosa: A Mad Max Saga. Little more than a week after the prequel to the beloved Mad Max: Fury Road debuted to awestruck reviews at Cannes, the film edged out Garfield to win the weekend with a four-day haul of 32 million at the domestic box office, which was a far less robust showing than industry experts had predicted, and well short of its predecessor's 45 million opening. Meanwhile, according to Netflix's figures, more than 28 million viewers worldwide celebrated the holiday by firing up Atlas, in which Jennifer Lopez is a scientist who defends Earth from annihilation by a terrorist artificial intelligence played by Simu Liu. Common sense, and possibly even Ted Sarandos, will tell you that people don't watch Netflix's content the way they watch a movie like Barbie in a theater--or even the way they'll watch Barbie when it turns up on Netflix.

One of the key strategies of streaming services is to keep you in front of a screen for as long as possible. As soon as one episode of a show you're watching ends, the next one pops up automatically. But this approach has its limits. After all, when a series ends, Netflix will try to autoplay another series that it thinks you'll like, but it has a terrible success rate. Maybe the tone of the suggested show is wrong, or maybe it's too exhausting to be dumped into the sea of exposition that a new show brings.

In this episode, Cherlynn and Devindra discuss Copilot+ and the potential rise of Arm-based Windows systems, and we dive into the new Surface Pro and Surface Laptop.