Full-Duplex Speech-to-Speech Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling real-time spoken dialogue systems. However, benchmarking and modeling these models remains a fundamental challenge. We introduce FLEXI, the first benchmark for full-duplex LLM-human spoken interaction that explicitly incorporates model interruption in emergency scenarios. FLEXI systematically evaluates the latency, quality, and conversational effectiveness of real-time dialogue through six diverse human-LLM interaction scenarios, revealing significant gaps between open source and commercial models in emergency awareness, turn terminating, and interaction latency. Finally, we suggest that next token-pair prediction offers a promising path toward achieving truly seamless and human-like full-duplex interaction.

Large language models (LLMs), due to their advanced natural language capabilities, have seen significant success in applications where the user interface is usually a conversational artificial intelligence (AI) agent and engages the user through multi-round conversations. However, many scenarios require the agents to exhibit stronger social and conversational intelligence and demonstrate more human-like (anthropomorphic) reactions. This is an aspect that foundational LLMs have yet to fully address such that a single call of foundational models might be insufficient. To bridge this gap, we propose a two-stage solution. In this work, we focus on the first stage, introducing a multi-module framework designed to replicate the key aspects of human intelligence involved in conversations. This framework comprises thinking modules for reasoning, resource modules for managing knowledge and external information, and response modules for generating contextually appropriate interactions. With all the modules cooperating, the framework would empower the agents to provide a better human-like conversation experience. In the second stage of our approach, these conversational data, after filtering and labeling, can serve as training and testing data for reinforcement learning, enabling AI to better capture human preferences. This stage is left for future work. In our experiments, volunteers engaged in over 3000 rounds of conversation with the same AI character powered by a standalone LLM and our framework which integrates the same LLM. A separate group of evaluators rated the conversation samples, revealing that our framework significantly enhanced the social and conversational intelligence, even without fine-tuning the LLM.

This research explores the factors driving user acceptance of Rabbit R1, a newly developed portable intelligent personal assistant (PIPA) that aims to redefine user interaction and control. The study extends the technology acceptance model (TAM) by incorporating artificial intelligence-specific factors (conversational intelligence, task intelligence, and perceived naturalness), user interface design factors (simplicity in information design and visual aesthetics), and user acceptance and loyalty. Using a purposive sampling method, we gathered data from 824 users in the US and analyzed the sample through partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The findings reveal that all hypothesized relationships, including both direct and indirect effects, are supported. Additionally, fsQCA supports the PLS-SEM findings and identifies three configurations leading to high and low user acceptance. This research enriches the literature and provides valuable insights for system designers and marketers of PIPAs, guiding strategic decisions to foster widespread adoption and long-term engagement.

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.

As the shift to digital continues, traditional marketing is increasingly being disrupted by digital marketing. Digital marketing teams are under enormous pressure to manage this transition, seize the opportunities offered by digital marketing and deliver outsized gains. Here are seven tactics for doing that. Improving workflow efficiency is a great way to enhance the client experience, reduce costs of service and improve agency profitability. A decade ago, venture capitalist Marc Andreessen said that "software is eating the world."

Microsoft Dynamics 365 Customer Service and Dynamics 365 Sales users will get new features later this month with an emphasis on conversational intelligence and Teams integrations. Dynamics 365 Customer Service will be more tightly embedded in Microsoft Teams. Contact center agents will be able to collaborate with co-workers by sharing AI-generated conversation summaries to resolve customer issues faster, and Teams chat will be embedded within Dynamics 365, which enables customer records of sales and service cases to be shared in Teams outside of their department or with supervisors and peers. Microsoft also previewed Azure Communication Services, a bundle of software workflow APIs that route public switched telephone network or VoIP calls within apps. All of the above features will also be compatible with Microsoft Digital Contact Center, a contact-center-as-a-service platform released earlier this year.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Unstructured data is by its very nature difficult to wrangle. It is one of the hardest sources of data to manage, said Amy Brown, founder and CEO of B2B software-as-a-service (SaaS) startup Authenticx. "AI allows organization of this really messy data source," Brown said. Still, she said, "it takes a commitment and a desire to use that data source."

Natural language processing (NLP) and conversational AI are often used together with machine learning, natural language understanding (NLU) to create sophisticated applications that enable machines to communicate with human beings. This article will look at how NLP and conversational AI are being used to improve and enhance the Call Center. NLP is a technological process that facilitates the ability to convert text or speech into encoded, structured information. By using NLP and NLU, machines are able to understand human speech and can respond appropriately, which, in turn, enables humans to interact with them using conversational, natural speech patterns. Predictive algorithmic forecasting is a method of AI-based estimation in which statistical algorithms are provided with historical data in order to predict what is likely to happen in the future.

"Please listen to our entire menu as our options have changed. Say or press one for product information..." Sometimes, these automated customer service experiences are effective and efficient--other times, not so much. Many organizations are already using chatbots and virtual assistants to help better serve their customers. These intelligent, automated self-service agents can handle frequently asked questions, provide relevant knowledge articles and resources to address customer inquiries, and help customers fill out forms and do other routine procedures. In the case of more complex inquiries, these automated self-service agents can triage those requests to a live human agent.

With the demand for automation and AI growing phenomenally, everyone wants a piece of this action. The chatbot industry, in particular, is growing at a rather high rate. But, not everyone can build their own chatbot platform from scratch. The good news is that they don't need to build their own platform to enter the chatbot business. Opting for a White Label partnership would be the best way to get into the chatbot game.