KEENON Robotics, ("KEENON"), a leading global AI company focusing on indoor intelligent service robots, showcased its service and sanitation robotic solutions at the 2022 Foodservice Australia Sydney held from October 23 to 25, debuting its latest floating tray developed for the DINERBOT T8. Foodservice Australia is a leading food industry trade show for cafes, restaurants, caterers and food retailers, and a unique platform to reach and connect with existing and potential partners. More than 450 exhibitors participated in this year's event to showcase their latest food, drink and equipment. "We are excited to join Foodservice Australia to present our robotic solutions, connect, and build strong relationships with customers from the food and hospitality industry," said Derren Wong, Head of Sales, Australia, KEENON Robotics. "KEENON offers a wide range of products and automation solutions to meet various business needs, and as the food and hospitality industry is recovering and thriving, we hope our robots can help local businesses in their daily customer-facing operations and improve the efficiency and productivity of their food services." KEENON's robots are equipped with a self-developed, fully autonomous positioning and navigation system, coupled with highly sensitive perception and obstacle avoidance technologies with auto-charging function that can readily adapt in complex real-world application scenarios to provide safe, reliable and efficient services.

Dr. Stephen Thaler developed DABUS (Device for Autonomous Bootstrapping of Unified Science), an artificial intelligence (AI) system that can autonomously create patentable inventions. Thaler has filed patent applications in various jurisdictions for two inventions created by DABUS – a food container with side walls having a fractal profile, and a beacon for attracting enhanced attention for example in a search and rescue scenario[1]. In each application, Thaler listed DABUS as the sole inventor, forcing patent offices in various jurisdictions to address the issue of whether an AI system can be an inventor on a patent application. Thus far, the DABUS patent applications have found very limited success in patent offices and courts around the world. In the latest decision, the United States Court of Appeals for the Federal Circuit (CAFC) held that the US Patent Act requires an inventor to be a natural person, and consequently, an AI system cannot be an inventor on a United States patent application.[2] The DABUS applications were initially rejected by the United States Patent and Trademark Office (USPTO).

This is mostly pretty cool. Beats the alternative, as they say. But one decidedly uncool thing about being almost 50 is that my "bad" LDL cholesterol is too high. The obvious solution here is to improve my diet and get more exercise. But if that were so easy, no one would have high cholesterol, right?

At first glance, generative AI might seem like more of a curiosity than an enterprise-technology tool, said Peter van der Putten, director of the AI Lab at software firm Pegasystems Inc. "Creating cute pictures of a corgi in a house made of sushi isn't exactly a profitable business case, at least not for large enterprises," Mr. van der Putten said. And yet, he said, "generative AI startups are popping up left and right, in areas such as marketing, support, service and other content creation." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Jasper, an Austin, Texas-based startup launched last year, has developed a generative AI platform designed to auto-generate promotional blog posts and other marketing materials. Amid a sharp decline in venture-capital investing deals, Jasper last week announced a $125 million Series A fundraising round, which set its private-market valuation above $1 billion, the company said.

The predictions of question answering (QA)systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the true performance of systems, and is typically addressed by extending over exact match (EM) with pre-defined rules or with the token-level F1 measure. In this paper, we present the first systematic conceptual and data-driven analysis to examine the shortcomings of token-level equivalence measures. To this end, we define the asymmetric notion of answer equivalence (AE), accepting answers that are equivalent to or improve over the reference, and publish over 23k human judgments for candidates produced by multiple QA systems on SQuAD. Through a careful analysis of this data, we reveal and quantify several concrete limitations of the F1 measure, such as a false impression of graduality, or missing dependence on the question. Since collecting AE annotations for each evaluated model is expensive, we learn a BERT matching (BEM) measure to approximate this task. Being a simpler task than QA, we find BEM to provide significantly better AE approximations than F1, and to more accurately reflect the performance of systems. Finally, we demonstrate the practical utility of AE and BEM on the concrete application of minimal accurate prediction sets, reducing the number of required answers by up to x2.6.

GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about the impact it will have on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60\% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development.

We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021). The method begins by fitting a flexible density regression model using monotonic splines whose weights are modeled as data-dependent functions using artificial neural networks. Subsequently, estimates of conditional density and quantile process can all be obtained. Unlike many approaches to quantile regression that assume a linear model, SPQR allows for virtually any relationship between the covariates and the response distribution including non-linear effects and different effects on different quantile levels. To increase the interpretability and transparency of SPQR, model-agnostic statistics developed by Apley and Zhu (2020) are used to estimate and visualize the covariate effects and their relative importance on the quantile function. In this article, we detail how this framework is implemented in SPQR and illustrate how this package should be used in practice through simulated and real data examples.

Pre-trained language models encode undesirable social biases, which are further exacerbated in downstream use. To this end, we propose MABEL (a Method for Attenuating Gender Bias using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations. Key to our approach is the use of a contrastive learning objective on counterfactually augmented, gender-balanced entailment pairs from natural language inference (NLI) datasets. We also introduce an alignment regularizer that pulls identical entailment pairs along opposite gender directions closer. We extensively evaluate our approach on intrinsic and extrinsic metrics, and show that MABEL outperforms previous task-agnostic debiasing approaches in terms of fairness. It also preserves task performance after fine-tuning on downstream tasks. Together, these findings demonstrate the suitability of NLI data as an effective means of bias mitigation, as opposed to only using unlabeled sentences in the literature. Finally, we identify that existing approaches often use evaluation settings that are insufficient or inconsistent. We make an effort to reproduce and compare previous methods, and call for unifying the evaluation settings across gender debiasing methods for better future comparison.

ASR Error Detection (AED) models aim to post-process the output of Automatic Speech Recognition (ASR) systems, in order to detect transcription errors. Modern approaches usually use text-based input, comprised solely of the ASR transcription hypothesis, disregarding additional signals from the ASR model. Instead, we propose to utilize the ASR system's word-level confidence scores for improving AED performance. Specifically, we add an ASR Confidence Embedding (ACE) layer to the AED model's encoder, allowing us to jointly encode the confidence scores and the transcribed text into a contextualized representation. Our experiments show the benefits of ASR confidence scores for AED, their complementary effect over the textual signal, as well as the effectiveness and robustness of ACE for combining these signals. To foster further research, we publish a novel AED dataset consisting of ASR outputs on the LibriSpeech corpus with annotated transcription errors.

Task-Oriented Dialogue (TOD) systems are drawing more and more attention in recent studies. Current methods focus on constructing pre-trained models or fine-tuning strategies while the evaluation of TOD is limited by a policy mismatch problem. That is, during evaluation, the user utterances are from the annotated dataset while these utterances should interact with previous responses which can have many alternatives besides annotated texts. Therefore, in this work, we propose an interactive evaluation framework for TOD. We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues. Besides, we introduce a sentence-level and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation. Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates in the interactive evaluation of MultiWOZ dataset and the proposed scores measure the response quality besides the inform and success rates. We are hoping that our work will encourage simulator-based interactive evaluations in the TOD task.