Nathan Fielder, like Andy Kaufman before him, makes performance-art comedy that does not only poke fun at the world but experimentally perturbs it, and he plies this trade in the buffer zone between reality and artifice. He presents himself as something of a Kaspar Hauser figure for the age of artificial intelligence, a foundling raised not by wolves but by an advanced and affectless race of extraterrestrial anthropologists. His object is to isolate and mimic the rudiments of human sociability. Fielder's intuition is that many putatively normal people share his own bewildered dread of everyday interactions, which are at once governed by established, if opaque, social norms and subject to unnerving unpredictability. Children learn to tame uncertainty through repetition: they replay interactions in an effort to interpret and control the varied challenges of their environment.

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.

In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To reduce the often prohibitive scheduling constraints associated with cooperative training, this article introduces a paradigm for cooperative asynchronous training of human teams in which trainees practice coordination with autonomous teammates rather than humans. We introduce a novel experimental design for evaluating autonomous teammates for use as training partners in cooperative training. We apply the design to a human-subjects experiment where humans are trained with either another human or an autonomous teammate and are evaluated with a new human subject in a new, partially observable, cooperative game developed for this study. Importantly, we employ a method to cluster teammate trajectories from demonstrations performed in the experiment to form a smaller number of training conditions. This results in a simpler experiment design that enabled us to conduct a complex cooperative training human-subjects study in a reasonable amount of time. Through a demonstration of the proposed experimental design, we provide takeaways and design recommendations for future research in the development of cooperative asynchronous training systems utilizing robot surrogates for human teammates.

Subject-verb agreement in the presence of an attractor noun located between the main noun and the verb elicits complex behavior: judgments of grammaticality are modulated by the grammatical features of the attractor. For example, in the sentence "The girl near the boys likes climbing", the attractor (boys) disagrees in grammatical number with the verb (likes), creating a locally implausible transition probability. Here, we parametrically modulate the distance between the attractor and the verb while keeping the length of the sentence equal. We evaluate the performance of both humans and two artificial neural network models: both make more mistakes when the attractor is closer to the verb, but neural networks get close to the chance level while humans are mostly able to overcome the attractor interference. Additionally, we report a linear effect of attractor distance on reaction times. We hypothesize that a possible reason for the proximity effect is the calculation of transition probabilities between adjacent words. Nevertheless, classical models of attraction such as the cue-based model might suffice to explain this phenomenon, thus paving the way for new research. Data and analyses available at https://osf.io/d4g6k

We consider a nonprehensile manipulation task in which a mobile manipulator must balance objects on its end effector without grasping them -- known as the waiter's problem -- and move to a desired location while avoiding static and dynamic obstacles. In constrast to existing approaches, our focus is on fast online planning in response to new and changing environments. Our main contribution is a whole-body constrained model predictive controller (MPC) for a mobile manipulator that balances objects and avoids collisions. Furthermore, we propose planning using the minimum statically-feasible friction coefficients, which provides robustness to frictional uncertainty and other force disturbances while also substantially reducing the compute time required to update the MPC policy. Simulations and hardware experiments on a velocity-controlled mobile manipulator with up to seven balanced objects, stacked objects, and various obstacles show that our approach can handle a variety of conditions that have not been previously demonstrated, with end effector speeds and accelerations up to 2.0 m/s and 7.9 m/s$^2$, respectively. Notably, we demonstrate a projectile avoidance task in which the robot avoids a thrown ball while balancing a tall bottle.

In the fall of 2000, as the first dot-com bubble was bursting, the Guatemalan computer scientist Luis von Ahn attended a talk, at Carnegie Mellon, about ten problems that Yahoo couldn't solve. Von Ahn, who had just begun his Ph.D., liked solving problems. He had planned to study math until he realized that many mathematicians were still toiling away over questions that had proved unanswerable for centuries. "I talked to some computer-science professors and they would say, 'Oh, yeah, I solved an open problem last week,' " he told me recently. "That seemed just a lot more interesting."

Gordon Ramsay may not be invited to the Olympics this year. Beijing is focusing on robotic cooks and servers to prepare and serve food to the attendees in the city's Winter Olympics Main Media Center to minimize the spread of COVID-19 and help maximize efficacy, according to a recent Food & Wine report. "The intelligent meal preparation and meal service system here can not only improve the efficiency of meal supply, but also save manpower to the maximum extent and avoid excessive human interaction in the context of epidemic prevention and control," the state-run Xinhua News Agency said. A worker grabs food delivered to a table robotically in the media dining area of the main media center ahead of the 2022 Winter Olympics, Wednesday, Feb. 2, 2022, in Beijing. "The media restaurant will operate 24 hours a day during the competition, providing various dining options such as Chinese food, Western food, and fast food."

A Chinese restaurant chain in the north west of England has been forced to make use of robotic waiters, after struggling for staff during the Covid pandemic. Directors at The Chinese Buffet unleashed one BellaBot in each of four restaurants in Liverpool, St Helens, Bolton and Wigan, to serve food to diners. When the buffet re-opened after the last lockdown, its owners decided to serve food to people at the table, ordered via an app, rather than allow them to serve themselves. This added an extra strain on the already short waiting staff, according to owners Paolo Hu and Peter Wu, who said the BellaBots had already proved popular with diners. The guide price for the friendly-faced robots is $20,000 (£14,500), which is less than the cost of employing a waiter at minimum wage for 40 hours per week. Quirky footage shows Bella, who features a wide-eyed feline face, sweeping across the restaurant floor dishing out delicacies to delighted customers.