Consensus building is inherently challenging due to the diverse opinions held by stakeholders. Effective facilitation is crucial to support the consensus building process and enable efficient group decision making. However, the effectiveness of facilitation is often constrained by human factors such as limited experience and scalability. In this research, we propose a Parallel Thinking-based Facilitation Agent (PTFA) that facilitates online, text-based consensus building processes. The PTFA automatically collects textual posts and leverages large language models (LLMs) to perform all of the six distinct roles of the well-established Six Thinking Hats technique in parallel thinking. To illustrate the potential of PTFA, a pilot study was carried out and PTFA's ability in idea generation, emotional probing, and deeper analysis of ideas was demonstrated. Furthermore, a comprehensive dataset that contains not only the conversational content among the participants but also between the participants and the agent is constructed for future study.

Federated learning (FL) facilitates collaborative training of machine learning models among a large number of clients while safeguarding the privacy of their local datasets. However, FL remains susceptible to vulnerabilities such as privacy inference and inversion attacks. Single-server secure aggregation schemes were proposed to address these threats. Nonetheless, they encounter practical constraints due to their round and communication complexities. This work introduces Fluent, a round and communication-efficient secure aggregation scheme for private FL. Fluent has several improvements compared to state-of-the-art solutions like Bell et al. (CCS 2020) and Ma et al. (SP 2023): (1) it eliminates frequent handshakes and secret sharing operations by efficiently reusing the shares across multiple training iterations without leaking any private information; (2) it accomplishes both the consistency check and gradient unmasking in one logical step, thereby reducing another round of communication. With these innovations, Fluent achieves the fewest communication rounds (i.e., two in the collection phase) in the malicious server setting, in contrast to at least three rounds in existing schemes. This significantly minimizes the latency for geographically distributed clients; (3) Fluent also introduces Fluent-Dynamic with a participant selection algorithm and an alternative secret sharing scheme. This can facilitate dynamic client joining and enhance the system flexibility and scalability. We implemented Fluent and compared it with existing solutions. Experimental results show that Fluent improves the computational cost by at least 75% and communication overhead by at least 25% for normal clients. Fluent also reduces the communication overhead for the server at the expense of a marginal increase in computational cost.

Despite all the stories about big companies bailing out of CES 2022 amidst the latest surge in COVID-19 cases, the consumer electronics show in Las Vegas is still the place to be for robots, autonomous vehicles, smart gadgets, and their inventors -- an opportunity to take stock of what's required to build practical machine intelligence into a consumer product. OrCam and Sonatus are among the companies no longer planning to travel to Las Vegas or announce products at CES, and it's possible some of the other vendors VentureBeat interviewed in advance of the event will also be no-shows. Big names like Microsoft, Google, Intel, Amazon, and T-Mobile backed out in recent weeks. Augmented reality, virtual reality, and the metaverse will be topics of discussion that will have to proceed without Meta (the company formerly known as Facebook). Automotive tech will be a big theme of the event, but General Motors, BMW, and Mercedes-Benz decided not to make the drive (GM's all-digital presence is still supposed to include a video keynote from CEO Mary Barra on Wednesday).

Stuttering is a disorder that negatively affects personal and professional life. One of the factors which may impact the likelihood of stuttering is phonological patterns. Some words are more prone to cause stuttering than others, and people who stutter (PWS) can identify which words they might struggle with and then think of a way to manage. Recent advancements in AI, such as phonetic embeddings, can help to simplify these processes. Therefore, a recent paper presents a novel machine-in-the-loop writing tool for assisting PWS with writing scripts, which minimize the number of stuttering events.

Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stutter (PWS) may adopt different strategies to conceal their stuttering. One of the common strategies is word substitution where an individual avoids saying a word they might stutter on and use an alternative instead. This process itself can cause stress and add more burden. In this work, we present Fluent, an AI augmented writing tool which assists PWS in writing scripts which they can speak more fluently. Fluent embodies a novel active learning based method of identifying words an individual might struggle pronouncing. Such words are highlighted in the interface. On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak. The user is free to accept or ignore these suggestions. Based on such user interaction (feedback), Fluent continuously evolves its classifier to better suit the personalized needs of each user. We evaluated our tool by measuring its ability to identify difficult words for 10 simulated users. We found that our tool can identify difficult words with a mean accuracy of over 80% in under 20 interactions and it keeps improving with more feedback. Our tool can be beneficial for certain important life situations like giving a talk, presentation, etc. The source code for this tool has been made publicly accessible at github.com/bhavyaghai/Fluent.

Toronto, Canada - June 5, 2017 – Maple Leaf Angels (MLA) is proud to announce their members' investment in Canadian start-up Fluent.ai Inc. MLA members participated in a $2.0 million seed funding round involving several other high profile groups including BDC Capital, California's Danhua Capital, 500 Startups Canada and angels from Toronto's Creative Destruction Lab. Graduating from the Montreal incubator TandemLaunch, Fluent.ai is focused on developing highly accurate and flexible voice interfaces for the Internet-of-Things and beyond. "The investors at Maple Leaf Angels quickly recognized the disruptive potential of Fluent.ai's "We are proud to have the involvement of Toronto-based MLA, along with an international group of investors, who believe in the global market opportunity our technology represents".

Montreal is one of the pioneers in Deep Learning thanks to the work of computer and cognitive scientists like Yoshua Bengio from Montreal Institute for Learning Algorithms, which is hosted at Université de Montréal. They're spearheading research on neural networks, amongst other things, which is highly effective in recognizing complex patterns like vision and speech. Founded by Jean-François Gagné and Yoshua Bengio, Element AI is an amazing A.I. incubation initiative that brings research and business together to create the most cutting edge services to disrupt industries. They are shortening the time it takes for new technologies and research to be integrated with great products and companies. This lab is pioneering in some amazing research on deep learning and the next generation of A.I. technology. Ivado is the multidisciplinary hub for a massive network of scientists researching statistics, business intelligence, deep learning, applied mathematics, data-mining and cybersecurity.