We hypothesize that these gradients AI systems frequently exhibit and amplify social biases, contain valuable information for identifying and modifying including gender bias, leading to harmful consequences gender-specific features. Our method aims to learn a in critical areas. This study introduces a novel encoderdecoder feature neuron that encodes gender information from the approach that leverages model gradients to input, i.e., model gradients. Unlike existing approaches learn a single monosemantic feature neuron encoding for extracting monosemantic features (e.g., Bricken et al. gender information. We show that our method can (2023)), our approach enables the learning of a feature neuron be used to debias transformer-based language models, with a desired, interpretable meaning, such as gender.

KIME, the robotic bartender, is a marvel of modern engineering. There is a revolutionary change underway when it comes to making our food and drinks. The advent of robotics, once limited to the fields of industrial manufacturing and the beloved Roomba, is now making a monumental leap into our kitchens and dining experiences. This shift is not just a fleeting trend; it's an evolution reshaping the very fabric of the food and beverage industry. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER KIME is a humanoid bartending kiosk that stands as a testament to the possibilities of robotic technology in food service.

The development of artificial agents for social interaction pushes to enrich robots with social skills and knowledge about (local) social norms. One possibility is to distinguish the expressive and the functional orders during a human-robot interaction. The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction-interaction in varying multi-party social situations-and beyond individual-based user personalization, thereby enlarging the current conception of "culturally-adaptive". The core idea is to provide the artificial agent with the capability to handle different kinds of interactional disruptions, and associated recovery strategies, in microsociology. The result is obtained by classifying functional and social disruptions, and by investigating the requirements a robot's architecture should satisfy to exploit such knowledge. The paper also highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity, leaving aside the further complexity of full-fledged human-human interactions. Without going into technical aspects, End-to-end Data-driven Architectures and Modular Architectures are discussed to evaluate the degree to which they can exploit this new set of social and cultural knowledge. Finally, a list of general requirements for such agents is proposed.

AI in the food industry, according to Tait, will also assist with automating reservations and customer service questions, which would ease workloads for human employees and restaurant owners. While the tech will also help restaurants "better manage stock and not over order" by analyzing "what products are selling well and detect and respond to consumer demand for a specific product."

This paper addresses the question of how bar robots can contribute to the well-being of guests. It first develops the basics of service robots and social robots. It gives a brief overview of which gastronomy robots are on the market. It then presents examples of bar robots and describes two models used in Switzerland. A research project at the School of Business FHNW collected empirical data on them, which is used for this article. The authors then discuss how the robots could be improved to increase the well-being of customers and guests and better address their individual wishes and requirements. Artificial intelligence can play an important role in this. Finally, ethical and social problems in the use of bar robots are discussed and possible solutions are suggested to counter these.

Artificial intelligence has created an alcoholic beverage that tastes like the melted candy found at the bottom of your grandma's handbag - and it got a three out of 10 in taste. This drink, called Grandma's Revenge, is just one recipe generated by the technology, which was asked to make cocktails based on names given by TikTok users. Grandma's Revenge features brandy, sherry and port, while another called Burning Inferno is made with vodka and Tabasco sauce. TikTok account Mob recently shared videos showing him following the instructions to make each drink based on what the AI churned out. TikTok account Mob shared two videos showing cocktails generated by AI.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. Manny actually did remember him. He'd been working at Happy Rent-a-Car for a while, and yes, after five years the tourists did all start to blend together. But he also prided himself on having a weirdly good memory, which meant that when the American investigators and their police liaisons asked, he could say with confidence: Oh, yeah. The man was clearly waiting for someone and seemed tired and fidgety after his flight. Manny watched him wander out to the bar on the curb, where they blasted American music and sold bad, expensive tacos and strong, sugary drinks. But he came back pretty quickly and said something like: What a scene. I came here to get away from that. Jimmy Buffett?--but he guessed the man meant his fellow Americans. This was a type that Manny encountered often, the ones who asked him where he liked to eat, in this really pointed way. No, they'd say, when he offered them a dinner recommendation. Where do you like to go? These were the tourists who spent their whole vacation looking for some better, more "authentic" Baja that they believed was hidden from them, a bedrock of reality they could reach if they only dug past the glass-bottom boat tours and resort buffets. Manny actually liked these tourists the least because he knew that even if he sent them to his favorite restaurant, they'd still feel disappointed. They would sacrifice their actual experience on the altar of their expectations. They were the ones who would have the worst time, because they were always looking for some other, better place concealed by the one that they could see. Manny, resigned, told the man where he liked to eat and watched him as he carefully wrote these suggestions down. Manny, in spite of himself, felt a little bad for him. The man asked where he liked to surf. Manny said he liked to go up north a bit. Cerritos could be fun, but it was way too crowded.

ARLINGTON, Va.--(BUSINESS WIRE)--As labor shortages and rising costs continue to cut into profit margins, restaurants are looking for ways to improve their business model--and automation is increasingly looking like a viable solution. From conversational AI drive-thrus to full-service fry cook robots, the possibilities are endless, but not all roles can be replaced. Capterra's 2022 Restaurant Automation Survey found that more than half of restaurant leaders say front-of-house restaurant workers can easily be automated with today's technology, but some disciplines remain currently irreplaceable. Most restaurants (76%) are currently using automation in three or more areas of operation and agree that certain roles are more automatable than others. Front-of-house (FOH) restaurant roles may be on the chopping block in the future, as most leaders believe that hosts, baristas, and drive-through operators can easily be replaced, and in some cases improved, by automation technology.

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with distributional meaning representations, thereby introducing the notion of semantic similarity into formal semantics, or to define distributional systems that aim to incorporate formal notions such as entailment and compositionality. However, given the fundamentally different 'representational currency' underlying formal and distributional approaches - models of the world versus linguistic co-occurrence - their unification has proven extremely difficult. Here, we define a Distributional Formal Semantics that integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are also inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of "information" (measured in terms of Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity.

Cruises have been cancelled since last march, resulting in over 100,000 Americans losing their jobs per a November report. Florida has been especially hard hit with the state being home to the three busiest cruise ports in the world. Robots may be able to pour drinks, but can they listen to your troubles? The future of bartending is coming to MSC Cruises' new flagship ship when the MSC Virtuosa launches later this year. The cruise line recently revealed details of its "immersive, futuristic" bar and entertainment experience – including a "humanoid" bartender.