Meta chief Mark Zuckerberg reveals the Muse'charm' device during the Meta Connect event in Menlo Park, California, this week. Meta chief Mark Zuckerberg reveals the Muse'charm' device during the Meta Connect event in Menlo Park, California, this week. We earn a commission if you buy something through an affiliate link. The tech giant has launched a cute'little guy' that can answer emails, book travel or do your weekly food shop After the recent backlash against Meta's so-called "pervert" smart glasses, this week Mark Zuckerberg launched a much cuter alternative: the Muse "charm" device. With a screen that shows a cutesy little avatar, it has drawn comparisons with Tamagotchis.

Michio Okada first conceptualised "weak robots", which have limited capabilities themselves, and are framed as objects or "social others" which people are invited to assist and take care of. In Okada's work, such robots are used to invite pro-social behaviour from people, such as encouraging them to pick up trash to assist a trash can robot (Okada (2022)). We conceptualise human-robot interaction (HRI) as a stage where weak robots-- designed to be "cute" and vulnerable--play the role of incidental actors that subvert the person engaging with them. Caudwell and Lacey (2020) argue that cuteness as a design choice for robots can encourage users to trust and form relationships with those robots, which introduces ambivalent power dynamics through the production of intimacy . In fact, cuteness can also be seen as a deceptive or "dark" pattern, due to the utilisation of cuteness to prompt affective responses which can be used to collect emotional data, as well as some degree of reduction of user agency (Lacey and Caudwell (2019)). The ability and affordances of cute and weak robots to influence user behaviour merits the discussion of their ethicality, which we do in this paper through design fiction. Unlike traditional HRI research, often confined to laboratory settings, our focus is on spontaneous, real-world interactions that transform everyday environments into sites of performative potential. We argue that the theatricality of these encounters is central to understanding their impact: the presence of a weak and/or cute robot, such as the trash can robot, developed by Okada and the Interaction and Communication Design Lab of the T oyohashi University of T echnology, acts as a disruptive interloper that introduces an observer's effect and, thus, affects the human interlocutors. First, we examine the concept of weak robots through the lens of performativity theory as well as concepts of machine (dys)function.

As social robots become more common, many have adopted cute aesthetics aiming to enhance user comfort and acceptance. However, the effect of this aesthetic choice on human feedback in reinforcement learning scenarios remains unclear. Previous research has shown that humans tend to give more positive than negative feedback, which can cause failure to reach optimal robot behavior. We hypothesize that this positive bias may be exacerbated by the robot's level of perceived cuteness. To investigate, we conducted a user study where participants critique a robot's trajectories while it performs a task. We then analyzed the impact of the robot's aesthetic cuteness on the type of participant feedback. Our results suggest that there is a shift in the ratio of positive to negative feedback when perceived cuteness changes. In light of this, we experiment with a stochastic version of TAMER which adapts based on the user's level of positive feedback bias to mitigate these effects.

The Japanese notion of "kawaii" or expressions of cuteness, vulnerability, and/or charm is a global cultural export. Work has explored kawaii-ness as a design feature and factor of user experience in the visual appearance, nonverbal behaviour, and sound of robots and virtual characters. In this initial work, we consider whether voices can be kawaii by exploring the vocal qualities of voice assistant speech, i.e., kawaii vocalics. Drawing from an age-inclusive model of kawaii, we ran a user perceptions study on the kawaii-ness of younger- and older-sounding Japanese computer voices. We found that kawaii-ness intersected with perceptions of gender and age, i.e., gender ambiguous and girlish, as well as VA features, i.e., fluency and artificiality. We propose an initial model of kawaii vocalics to be validated through the identification and study of vocal qualities, cognitive appraisals, behavioural responses, and affective reports.

Machine learning can sometimes seem confusing, with algorithm names and model types seemingly proliferating without end. But we know for a fact that anyone can understand and employ machine learning, no matter their skill level. With major advancements like our latest release (RapidMiner Go), it's easier than ever for beginners to start leveraging machine learning as a powerful tool to drive business impact. That's why we wanted to take a step back and draw up some explainers about the core concepts in machine learning for newcomers. In that spirit, we'll be looking at two of the most common categories of machine learning in this post: supervised and unsupervised machine learning.

Google Assistant and Alexa may pretend to have "personality," but they really don't. Telling a joke when asked does not make any of them a great raconteur. This is fine for two reasons. First, it's not what they're for, and second, giving an artificial creation personality is very, very difficult. Gatebox, the company behind the eponymous product, is succeeding where others have either failed, or aren't even trying.

Train a neural network on flawed data and you'll have one that makes lots of mistakes. Most neural networks learn to distinguish between things by sampling different groups. This is supervised learning, and it only works if someone labels the data first so that the network knows what it's looking at. But how can you find the "right" data to train your AI, and confirm its quality? Well, what you feed your machine might surprise you.

Following several delays, a new range of social domestic robots is expected to enter the market at the end of this year. They are no ordinary bots. Designed to provide companionship and care, they recognise faces and voices of close family and friends, play games, tell jokes and continue to learn from each interaction. They also have one feature in common. They are intended to be extraordinarily cute and appealing.

Baseball games have slowed down because, as players' salaries have increased, the value of each pitch and each swing has increased, as well. When a pitcher gets thirty starts in a season, a hundred pitches each, bucking for a twelve-million-dollar contract, each pitch is worth four thousand dollars. "Avengers: Age of Ultron" cost about two hundred and fifty million dollars to make, and Joss Whedon, its writer and director, appears to have taken his time, too--especially with the script. Whedon has done something similar to what he did in "The Avengers"--namely, to make a film that's in tune with the political zeitgeist as he perceives it. There, it was a post-9/11 revenge fantasy set against a backdrop of unpopular foreign wars.

Matt Bencke is CEO of Spare5. Previously, he worked at Getty Images, Microsoft and Boeing. Most artificial intelligence models are built and trained by humans, and therefore have the potential to learn, perpetuate and massively scale the human trainers' biases. This is the word of warning put forth in two illuminating articles published earlier this year by Jack Clark at Bloomberg and Kate Crawford at The New York Times. Tl;dr: The AI field lacks diversity -- even more spectacularly than most of our software industry.