Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.

Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is more practical in real scenarios. Simply attending longer transcription history for a vanilla neural transducer model shows no much gain in our preliminary experiments, since the prediction network is not a pure language model. This motivates us to leverage the factorized neural transducer structure, containing a real language model, the vocabulary predictor. We propose the {LongFNT-Text} architecture, which fuses the sentence-level long-form features directly with the output of the vocabulary predictor and then embeds token-level long-form features inside the vocabulary predictor, with a pre-trained contextual encoder RoBERTa to further boost the performance. Moreover, we propose the {LongFNT} architecture by extending the long-form speech to the original speech input and achieve the best performance. The effectiveness of our LongFNT approach is validated on LibriSpeech and GigaSpeech corpora with 19% and 12% relative word error rate~(WER) reduction, respectively.

The development of autonomous vehicles arises new challenges in urban traffic scenarios where vehicle-pedestrian interactions are frequent e.g. vehicle yields to pedestrians, pedestrian slows down due approaching to the vehicle. Over the last years, several datasets have been developed to model these interactions. However, available datasets do not cover near-accident scenarios that our dataset covers. We introduce I see you, a new vehicle-pedestrian interaction dataset that tackles the lack of trajectory data in near-accident scenarios using YOLOv5 and camera calibration methods. I see you consist of 170 near-accident occurrences in seven intersections in Cusco-Peru. This new dataset and pipeline code are available on Github.

Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT-3, the most widely deployed language model in production, can be easily misaligned by simple handcrafted inputs. In particular, we investigate two types of attacks -- goal hijacking and prompt leaking -- and demonstrate that even low-aptitude, but sufficiently ill-intentioned agents, can easily exploit GPT-3's stochastic nature, creating long-tail risks. The code for PromptInject is available at https://github.com/agencyenterprise/PromptInject.

We study the fairness of dynamic resource allocation problem under the $\alpha$-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests.

Abstract-- A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work. Figure 1: A collage of the 73 existing robotic hands that we evaluated in an online study.

The scowl on Cristiano Ronaldo's face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. Few footballers agree with a manager's decision to substitute them in favour of a fresh replacement. During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored.

MedicReS Artificial Intelligence World Congress 2023 | Courses

Israel blamed Iran on Wednesday after what it said was a drone strike hit a tanker operated by an Israeli-owned firm carrying gas oil off the coast of Oman. The Pacific Zircon was "hit by a projectile approximately 150 miles off the coast of Oman ... on 15 November," Singapore-based firm Eastern Pacific Shipping which operates the vessel said in a statement, adding that there were no reports of casualties or any leakage of the cargo. "There is some minor damage to the vessel's hull but no spillage of cargo or water ingress," said the company which is owned by Israeli billionaire Idan Ofer -- one of two sons of shipping magnate Sammy Ofer, who died in 2011. The tanker was carrying 42,000 tonnes of gas oil and bound for Buenos Aires, according to Samir Madani, co-founder of website TankerTrackers.com, The Bahrain-based United States Fifth Fleet said it was "aware of the incident".

A vast majority of the populations in the emerging markets of Southeast Asia, Latin America, and India are at the cusp of financial inclusion, thanks to the growing availability and adoption of digital lending services. The fintech-as-a-service market is predicted to grow to around US$ 949 Billion by 2028 due to the popularity of the alternative payment solution Buy Now Pay Later in these markets. With increased acceptability for digital lending in segments that had never been a part of the financial mainstream, organizations must enhance risk decisioning while ensuring faster turnaround on credit applications. Maintaining a high rate of credit approvals and managing risk while lending to people with little credit information is a challenge that more and more financial institutions are looking to solve by leveraging machine learning and artificial intelligence. Fintech companies are automating these processes by enriching their machine learning techniques with data and scores that improve predictive risk modeling.