GITEX Global–Uniphore, the leader in Conversational AI and Automation, at GITEX Global 2022, announced a strategic partnership with Avaya, a global leader in solutions to enhance and simplify communications and collaboration, to bring its integrated Conversational AI and communications platform to customers across the Middle East and African (MEA) region. "In today's uncertain world, consumers want brands to address their needs quickly and efficiently; this makes the customer experience more important than ever" Uniphore's Conversational AI and Automation products will add deep functionality to the Avaya OneCloud CCaaS platform. Avaya OneCloud CCaaS makes it easy to connect chat, video, voice, and messaging to deliver enhanced experiences for customers and employees at every touchpoint. With Uniphore, Avaya OneCloud CCaaS users will be able to track, measure, and improve their contact center journey with increased self-serve capabilities, frictionless agent experience, and needle-moving insights. Avaya's customers will have access to Uniphore's conversational AI and automation solutions and will be well-placed to digitally onboard customers, including from social media platforms driven by AI-powered solutions.

We hear from Steven Kolawole about his paper on sign-to-speech models for Nigerian sign language. Steven told us about the goals of this research, his methodology, and how the work has inspired research in other languages. The biggest goal of the research was to reduce the communication barrier between the hearing-impaired community and the general populace, focusing on sub-Saharan Africa. Sub-Saharan Africa is one of the regions with the highest number of cases of hearing disabilities and, additionally, the region with the lowest number of solutions targeted towards solving this problem. And investigating why this is the status quo was very interesting.

Ai-Da the robot has admitted she was'nervous' about speaking at the House of Lords and named her favourite artist as Yoko Ono in an exclusive interview with MailOnline. Ai-Da made history on Tuesday by becoming the first robot to address the House of Lords – although she suffered a slight hiccup after'falling asleep' mid-speech. During the session, the bot had to be rebooted by her creator Aidan Meller, after a technical issue rendered her cross-eyed and zombie-like. Shortly after, MailOnline asked Ai-Da a couple of questions about the address. Wearing dungarees and an orange blouse, Ai-Da said the address to the House of Lords went well and that she feels'quite nervous when speaking in public' Ai-Da is an artificial intelligence robot built in 2019 that creates drawings, paintings and sculptures.

AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind An artificial intelligence (AI) developed by machine-learning company DeepMind in London has tackled a type of calculation called matrix multiplication. The system -- called AlphaTensor -- leverages the skills that DeepMind's game-playing AIs use to beat human players at games such as Go and chess. Matrix multiplication is a widely used mathematical technique that involves multiplying numbers arranged in grids, or matrices, that might represent sets of pixels in images, air conditions in a weather model or the internal workings of an artificial neural network. AlphaTensor broke ground by finding shortcuts to solve these problems with fewer steps (A. The same general approach could have applications in other kinds of mathematical operation, its developers say, such as decomposing complex waves or other mathematical objects into simpler ones.

A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely heavily on expert knowledge and may be sub-optimal. In this paper, we propose a principled and universal method for learning better representations with auxiliary loss functions, named Automated Auxiliary Loss Search (A2LS), which automatically searches for top-performing auxiliary loss functions for RL. Specifically, based on the collected trajectory data, we define a general auxiliary loss space of size $7.5 \times 10^{20}$ and explore the space with an efficient evolutionary search strategy. Empirical results show that the discovered auxiliary loss (namely, A2-winner) significantly improves the performance on both high-dimensional (image) and low-dimensional (vector) unseen tasks with much higher efficiency, showing promising generalization ability to different settings and even different benchmark domains. We conduct a statistical analysis to reveal the relations between patterns of auxiliary losses and RL performance.

Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models. Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. To tackle the challenge, we propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. We conduct extensive experiments on 40 datasets, which show that our model can not only serve as the strong foundation backbone for a wide range of tasks but also be feasible as a probing tool for analyzing task relationships. The task relationships reflected by the prefixes align transfer learning performance between tasks. They also suggest directions for data augmentation with complementary tasks, which help our model achieve human-parity results on commonsense reasoning leaderboards. Code is available at https://github.com/cooelf/CompassMTL

Revision game is a very new model formulating the real-time situation where players dynamically prepare and revise their actions in advance before a deadline when payoffs are realized. It is at the cutting edge of dynamic game theory and can be applied in many real-world scenarios, such as eBay auction, stock market, election, online games, crowdsourcing, etc. In this work, we novelly identify a class of strategies for revision games which are called Limited Retaliation strategies. An limited retaliation strategy stipulates that, (1) players first follow a recommended cooperative plan; (2) if anyone deviates from the plan, the limited retaliation player retaliates by using the defection action for a limited duration; (3) after the retaliation, the limited retaliation player returns to the cooperative plan. A limited retaliation strategy has three key features. It is cooperative, sustaining a high level of social welfare. It is vengeful, deterring the opponent from betrayal by threatening with a future retaliation. It is yet forgiving, since it resumes cooperation after a proper retaliation. The cooperativeness and vengefulness make it constitute cooperative subgame perfect equilibrium, while the forgiveness makes it tolerate occasional mistakes. limited retaliation strategies show significant advantages over Grim Trigger, which is currently the only known strategy for revision games. Besides its contribution as a new robust and welfare-optimizing equilibrium strategy, our results about limited retaliation strategy can also be used to explain how easy cooperation can happen, and why forgiveness emerges in real-world multi-agent interactions. In addition, limited retaliation strategies are simple to derive and computationally efficient, making it easy for algorithm design and implementation in many multi-agent systems.

Creativity, i.e., the process of generating and developing fresh and original ideas or products that are useful or effective, is a valuable skill in a variety of domains. Creativity is called an essential 21st-century skill that should be taught in schools. The use of educational technology to promote creativity is an active study field, as evidenced by several studies linking creativity in the classroom to beneficial learning outcomes. Despite the burgeoning body of research on adaptive technology for education, mining creative thinking patterns from educational data remains a challenging task. In this paper, to address this challenge, we put the first step towards formalizing educational knowledge by constructing a domain-specific Knowledge Base to identify essential concepts, facts, and assumptions in identifying creative patterns. We then introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. Finally, we present a rule-based approach to learning from the Knowledge Base, and facilitate mining creative thinking patterns from contextualized data and knowledge. We evaluate our approach with real-world datasets and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students' activities and assessment tasks.

Neural Networks are known to be sensitive to initialisation. The methods that rely on neural networks for feature ranking are not robust since they can have variations in their ranking when the model is initialized and trained with different random seeds. In this work, we introduce a novel method based on parameter averaging to estimate accurate and robust feature importance in tabular data setting, referred as XTab. We first initialize and train multiple instances of a shallow network (referred as local masks) with "different random seeds" for a downstream task. We then obtain a global mask model by "averaging the parameters" of local masks. We show that although the parameter averaging might result in a global model with higher loss, it still leads to the discovery of the ground-truth feature importance more consistently than an individual model does. We conduct extensive experiments on a variety of synthetic and real-world data, demonstrating that the XTab can be used to obtain the global feature importance that is not sensitive to sub-optimal model initialisation.

Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation and response management procedures related to these systems. Network reliability approaches commonly consider network-level responses, and due to computational cost do not consider the more detailed node-level responses. In this paper, we propose a rapid seismic reliability assessment approach for bridge networks based on graph neural networks, where node-level connectivities, between points of interest and other nodes, are quantified under probabilistic bridge conditions and earthquake events. Via numerical experiments on transportation systems in California, we demonstrate the accuracy, computational efficiency and robustness of the proposed approach compared to the Monte Carlo approach.