Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recognition systems are limited by fixed vocabularies, predefined labels, and rigid interaction categories that often rely on choreographed videos and overlook concurrent interactive groups. These limitations make such systems less adaptable to real-world scenarios, where interactions are diverse and unpredictable. In this paper, we propose an open vocabulary human-to-human interaction recognition (OV-HHIR) framework that leverages large language models to generate open-ended textual descriptions of both seen and unseen human interactions in open-world settings without being confined to a fixed vocabulary. Additionally, we create a comprehensive, large-scale human-to-human interaction dataset by standardizing and combining existing public human interaction datasets into a unified benchmark. Extensive experiments demonstrate that our method outperforms traditional fixed-vocabulary classification systems and existing cross-modal language models for video understanding, setting the stage for more intelligent and adaptable visual understanding systems in surveillance and beyond.

Trust in AI agents has been extensively studied in the literature, resulting in significant advancements in our understanding of this field. However, the rapid advancements in Large Language Models (LLMs) and the emergence of LLM-based AI agent frameworks pose new challenges and opportunities for further research. In the field of process automation, a new generation of AI-based agents has emerged, enabling the execution of complex tasks. At the same time, the process of building automation has become more accessible to business users via user-friendly no-code tools and training mechanisms. This paper explores these new challenges and opportunities, analyzes the main aspects of trust in AI agents discussed in existing literature, and identifies specific considerations and challenges relevant to this new generation of automation agents. We also evaluate how nascent products in this category address these considerations. Finally, we highlight several challenges that the research community should address in this evolving landscape.

While my area of expertise is customer service and experience, these predictions are applicable to ... [ ] all areas of business. Last week, I shared the first five of 10 predictions for what 2021 will bring. While my area of expertise is customer service and experience, these predictions are applicable to all areas of business. After all, every business was forced to adapt and adopt new policies and technologies, including increased AI, as a result of the Covid-19 pandemic. AI (Artificial Intelligence) continues to improve, along with capabilities we never imagined just a few years ago.

Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

Today I published a perspective paper on COVID-19. The paper is co-authored with members of the Cambridge Centre for AI in Medicine (which I recently founded and I am directing), and calls on governments and healthcare authorities to use proven AI and machine learning techniques and existing data to coordinate a response to the disease. If you'd like to ask me about the paper or discuss it further, please leave a question/comment below, and I'll get back to you. I've also provided a link to the full paper at the bottom of this post. Both the UK and the international community are still in the early stages of a crisis that will see an unbelievable amount of pressure put on social and healthcare infrastructure.

"Sorry, guys, I made the mistake this round," it says. "I know it may be hard to believe, but robots make mistakes too." This scenario occurred multiple times during a Yale-led study of robots' effects on human-to-human interactions. The study, which will publish on March 9 in the Proceedings of the National Academy of Sciences, showed that the humans on teams that included a robot expressing vulnerability communicated more with each other and later reported having a more positive group experience than people teamed with silent robots or with robots that made neutral statements, like reciting the game's score. "We know that robots can influence the behavior of humans they interact with directly, but how robots affect the way humans engage with each other is less well understood," said Margaret L. Traeger, a Ph.D. candidate in sociology at the Yale Institute for Network Science (YINS) and the study's lead author.

"Sorry, guys, I made the mistake this round," it says. "I know it may be hard to believe, but robots make mistakes too." This scenario occurred multiple times during a Yale-led study of robots' effects on human-to-human interactions. The study, which will publish on March 9 in the Proceedings of the National Academy of Sciences, showed that the humans on teams that included a robot expressing vulnerability communicated more with each other and later reported having a more positive group experience than people teamed with silent robots or with robots that made neutral statements, like reciting the game's score. "We know that robots can influence the behavior of humans they interact with directly, but how robots affect the way humans engage with each other is less well understood," said Margaret L. Traeger, a Ph.D. candidate in sociology at the Yale Institute for Network Science (YINS) and the study's lead author.

The robot makes a mistake, costing the team a round. Like any good teammate, it acknowledges the error. "Sorry, guys, I made the mistake this round," it says. "I know it may be hard to believe, but robots make mistakes too." This scenario occurred multiple times during a Yale-led study of robots' effects on human-to-human interactions.

The likes of Siri, Alexa, and Google Now have allowed us to interact with our world through a machine. And Facebook and Microsoft will also soon deploy conversational interfaces as Apple and Google expand their respective apps. The rise of the chatbot and other responsive bots has meant that conversation as an interface is the best way for machines to interact with us using a human technique we're all familiar with – language. As bots develop further, let's take a look at what kinds of chatbots are available and how each can help or hinder your business. Not all chatbots are created equal.