With recent advancements in multi-agent generative AI (Gen AI), technology organizations like Microsoft are adopting these complex tools, redefining AI agents as active collaborators in complex workflows rather than as passive tools. In this study, we investigated how early adopters and developers conceptualize multi-agent Gen AI tools, focusing on how they understand human-AI collaboration mechanisms, general collaboration dynamics, and transparency in the context of AI tools. We conducted semi-structured interviews with 13 developers, all early adopters of multi-agent Gen AI technology who work at Microsoft. Our findings revealed that these early adopters conceptualize multi-agent systems as "teams" of specialized role-based and task-based agents, such as assistants or reviewers, structured similar to human collaboration models and ranging from AI-dominant to AI-assisted, user-controlled interactions. We identified key challenges, including error propagation, unpredictable and unproductive agent loop behavior, and the need for clear communication to mitigate the layered transparency issues. Early adopters' perspectives about the role of transparency underscored its importance as a way to build trust, verify and trace errors, and prevent misuse, errors, and leaks. The insights and design considerations we present contribute to CSCW research about collaborative mechanisms with capabilities ranging from AI-dominant to AI-assisted interactions, transparency and oversight strategies in human-agent and agent-agent interactions, and how humans make sense of these multi-agent systems as dynamic, role-diverse collaborators which are customizable for diverse needs and workflows. We conclude with future research directions that extend CSCW approaches to the design of inter-agent and human mediation interactions.

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine.

Time is running short to limit global warming to 1.5 C (2.7 F) above preindustrial levels, but there are feasible and effective solutions on the table, according to a new UN climate report. Despite decades of warnings from scientists, global greenhouse-gas emissions are still climbing, hitting a record high in 2022. If humanity wants to limit the worst effects of climate change, annual greenhouse-gas emissions will need to be cut by nearly half between now and 2030, according to the report. That will be complicated and expensive. But it is nonetheless doable, and the UN listed a number of specific ways we can achieve it.

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.

Ever spend much too long trying -- and failing -- to rediscover articles you've partially read? This reporter's been there, and it seems I'm not the only one. According to 2021 Carnegie Mellon study on browser tab usage, many participants admitted to feeling overwhelmed by the amount of tabs they kept open but were compelled not to close them out of fear of missing out on valuable information. Samiur Rahman is familiar with the feeling -- so much so that he co-created a product, Heyday, to alleviate it. Launched in 2021, Heyday is designed to automatically save web pages and pull in content from cloud apps, resurfacing the content alongside search engine results and curating it into a knowledge base. Investors include Spark Capital, which led a $6.5 million seed round in the company that closed today.

Wish your network could predict its own problems and fix them automatically? Cisco believes it has the technology you need. The networking tech giant announced today what it said is the culmination of two years of work: an analytics engine that can predict network issues before they happen, and with enough integration and training even fix problems itself, Cisco said. Citing data from an in-house study, Cisco said that 45 percent of IT leaders it surveyed cited responding to disruptions as their biggest networking challenge of 2021. Predictive analytics technology, coupled with "enormous amounts of historical [networking] data," is a potential solution, Cisco said.

Artificial intelligence (AI) is transforming digital settings. It points to a future in which boring jobs are automated using machine learning techniques. These self-driving cars and robotised solutions are entering various sectors of life, and scientific groups rely heavily on AI to investigate and invent. Businesses continue to invest in projects that leverage AI capabilities. According to Accenture, artificial intelligence will enhance profitability by about 38% by 2035.

While customer onboarding and risk detection are the primary uses of AI cited by 29% of respondents, data management and customer insights are the most common use cases. "Data is becoming ever more important as organizations increasingly digitize," said Vishal Marria, CEO and founder of Quantexa (which conducted the survey). "However, these huge waves of data often lead to decision gaps that plague organizations, leaving them unable to extract meaningful value. AI and technological advances such as entity resolution are helping close this data decision gap in a strategic and measured way, allowing organizations to connect siloed data to create a meaningful connected view, that directly leads to higher accuracy, productivity and ultimately trusted decision making." Data readiness, integrating internal/external data sources (15%), making AI operational (14%) and the availability of skills (14%) are reported as the biggest challenges in adopting AI in financial services firms.

With 5G, real-time computing will become a reality. The high speeds, high data throughput, and high number of connections that 5G enables will effectively erase the lag time between when data gets generated to when we can act on it. And, while self-driving vehicles might be the most visible new example of real-time processing most of us see, they are really only the tip of the iceberg, especially as private networks and network slicing roll out to bring the power of carrier-grade infrastructure to more locations and situations. IDC estimates that real-time data will grow by 50 times between 2000 and 2030 and constitute 30% of all data by then. Manufacturing will be one of the first places where a real-time data revolution takes place.

There's no question that the world around us is getting smarter. In recent years, the exponential growth of artificial intelligence (AI) has created an increasing demand for AI solutions across a broad range of industries. Accelerated in part by the global pandemic, businesses and governments around the world are recognising the benefits of becoming early adopters of AI as part of ongoing digital transformation. A regional hub for AI entrepreneurs At the forefront of this movement (and as far back as 2017), the UAE government released a first-of-its-kind strategy setting a clear roadmap for developing its regional capabilities and becoming the world's premier AI destination. And it's looking to become a game-changer, with the region as a whole estimated to accrue 2 per cent of the global benefits of AI – $320bn by 2030.