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Rational speakers are supposed to know what they know and what they do not know, and to generate expressions matching the strength of evidence. In contrast, it is still a challenge for current large language models to generate corresponding utterances based on the assessment of facts and confidence in an uncertain real-world environment. While it has recently become popular to estimate and calibrate confidence of LLMs with verbalized uncertainty, what is lacking is a careful examination of the linguistic knowledge of uncertainty encoded in the latent space of LLMs. In this paper, we draw on typological frameworks of epistemic expressions to evaluate LLMs' knowledge of epistemic modality, using controlled stories. Our experiments show that the performance of LLMs in generating epistemic expressions is limited and not robust, and hence the expressions of uncertainty generated by LLMs are not always reliable. To build uncertainty-aware LLMs, it is necessary to enrich semantic knowledge of epistemic modality in LLMs.

Semantic 3D mapping, the process of fusing depth and image segmentation information between multiple views to build 3D maps annotated with object classes in real-time, is a recent topic of interest. This paper highlights the fusion overconfidence problem, in which conventional mapping methods assign high confidence to the entire map even when they are incorrect, leading to miscalibrated outputs. Several methods to improve uncertainty calibration at different stages in the fusion pipeline are presented and compared on the ScanNet dataset. We show that the most widely used Bayesian fusion strategy is among the worst calibrated, and propose a learned pipeline that combines fusion and calibration, GLFS, which achieves simultaneously higher accuracy and 3D map calibration while retaining real-time capability. We further illustrate the importance of map calibration on a downstream task by showing that incorporating proper semantic fusion on a modular ObjectNav agent improves its success rates. Our code will be provided on Github for reproducibility upon acceptance.

Context changes expectations about upcoming words - following a story involving an anthropomorphic peanut, comprehenders expect the sentence the peanut was in love more than the peanut was salted, as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This updating of expectations has been explained using Situation Models - mental representations of a described event. However, recent work showing that N400 amplitude is predictable from distributional information alone raises the question whether situation models are necessary for these contextual effects. We model the results of Nieuwland and van Berkum (2006) using six computational language models and three sets of word vectors, none of which have explicit situation models or semantic grounding. We find that a subset of these can fully model the effect found by Nieuwland and van Berkum (2006). Thus, at least some processing effects normally explained through situation models may not in fact require explicit situation models.

Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our method differs from previous prediction-based navigation methods, such as frontier potential prediction or egocentric map completion, by directly predicting unseen targets while leveraging the global context from all previously explored areas. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data. Once trained, the model can be incorporated into a modular pipeline for ObjectNav without the need for any reinforcement learning. We validate the effectiveness of our method on the HM3D and MP3D ObjectNav datasets. We find that it achieves the state-of-the-art on both datasets, despite not using any additional data for training.

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.

During a normal April, the owners of the Island Grill would already have a stack of applications to wade through in preparation for the busy Jersey Shore summer. But as the pandemic has waned and business has returned, the applicants haven't lined up. Here in Ocean City, there just aren't enough hands to serve coconut shrimp, quesadillas, and clam chowder in a family-friendly setting. So Allison Yoa, one of the grill's owners, hired Peanut the robot, an autonomous machine that shuttles back and forth from the kitchen delivering food and bussing dirty dishes. It looks like a rolling bookshelf, with four trays, a touchscreen, and an upward-facing infrared camera that scans markings on the ceiling in order to navigate.

Artificial Intelligence (AI) is rapidly changing the way we work and live. There are a lot of myths and hype around AI, Machine Learning (ML), and what they can do. So, let's break down some walls and get a realistic view of them. There are many definitions of AI on the internet. So, let us try to dumb it down as much as we can without losing its essence.

HONG KONG – The deadly coronavirus outbreak, which has pushed the Chinese medical community into overdrive, has also prompted the country's hospitals to more quickly adopt robots as medical assistants. Telepresence bots that allow remote video communication, patient health monitoring and safe delivery of medical goods are growing in number on hospital floors in urban China. They are now acting as safe go-betweens that help curb the spread of the coronavirus. Keenon Robotics Co., a Shanghai-based company, deployed 16 robots of a model nicknamed "Little Peanut" to a hospital in Hangzhou after a group of Wuhan travelers to Singapore were held in quarantine. Siasun Robot and Automation Co. donated seven medical robots and 14 catering robots to the Shenyang Red Cross to help hospitals combat the virus on Wednesday, according to a media release on the company's website.

Careful with using Alexa or Google around your pet birds -- your feathered friend might end up ordering up some crackers. An African grey Congo parrot in Florida has apparently mastered Amazon's Alexa digital home assistant and has already moved on to conquering Google Home. Not so nice -- the first thing Petra the parrot learned to do was to order all the lights off, at random. "First, you're like half awake and … like, 'Was that a dream? The parrot has even earned its own YouTube channel, currently at more than 13,000 subscribers. Some of the more meta videos show Petra watching her own interviews on television, Facetiming with other parrots, and telling the dog to "Dance!