Cancel Culture Comes for Artists Who Posted About Charlie Kirk's Death A episode was taken off air, a DC comic series was canceled, and several artists were fired in the aftermath of the shooting. Almost immediately after she posted about the shooting of Charlie Kirk, author and transwoman Gretchen Felker-Martin started having second thoughts. Felker-Martin, who wrote the latest iteration of DC Comics' series, said "thoughts and prayers you Nazi bitch" on Bluesky in response to the killing of Kirk, a right-wing influencer and Trump ally who was staunchly anti-trans rights. "Hope the bullet's okay after touching Kirk," she added. Kirk died after being shot at a stop on his American Comeback Tour organized by the conservative youth organization he founded, Turning Point USA.

This paper analyses Conversational AI multi-agent interoperability frameworks and describes the novel architecture proposed by the Open Voice Interoperability initiative (Linux Foundation AI and DATA), also known briefly as OVON (Open Voice Network). The new approach is illustrated, along with the main components, delineating the key benefits and use cases for deploying standard multi-modal AI agency (or agentic AI) communications. Beginning with Universal APIs based on Natural Language, the framework establishes and enables interoperable interactions among diverse Conversational AI agents, including chatbots, voicebots, videobots, and human agents. Furthermore, a new Discovery specification framework is introduced, designed to efficiently look up agents providing specific services and to obtain accurate information about these services through a standard Manifest publication, accessible via an extended set of Natural Language-based APIs. The main purpose of this contribution is to significantly enhance the capabilities and scalability of AI interactions across various platforms. The novel architecture for interoperable Conversational AI assistants is designed to generalize, being replicable and accessible via open repositories.

The article you are about to read was written by a human. This kind of disclaimer will become an everyday occurrence as chatbots, or large language models, infiltrate deeper into our media space. Doubts about the veracity of such disclaimers will also become commonplace. With the leaps and bounds registered by machine learning and large language models over the past couple of years, it is becoming increasingly difficult to prove that a human is on the other side of a written or spoken communication. How would I prove to you that these words were the product of human creativity and exertion?

On the day that Jonah Peretti, the C.E.O. of BuzzFeed, announced that he was closing BuzzFeed News, he held a video all-hands meeting with the site's staff. "People always yell at Jonah in meetings," Katie Notopoulos, a tech reporter, said, "but I've seen worse." Peretti had already sent the attendees a memo acknowledging the bad moves that had led up to this moment. "I made the decision to overinvest in BuzzFeed News because I love their work and mission so much," he had written. "This made me slow to accept that the big platforms wouldn't provide the distribution or financial support required to support premium, free journalism purpose-built for social media."

The artificial-intelligence (AI) chatbot ChatGPT that has taken the world by storm has made its formal debut in the scientific literature -- racking up at least four authorship credits on published papers and preprints. Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it's appropriate to cite the bot as an author. Publishers are racing to create policies for the chatbot, which was released as a free-to-use tool in November by tech company OpenAI in San Francisco, California. AI bot ChatGPT writes smart essays -- should professors worry? ChatGPT is a large language model (LLM), which generates convincing sentences by mimicking the statistical patterns of language in a huge database of text collated from the Internet. The bot is already disrupting sectors including academia: in particular, it is raising questions about the future of university essays and research production.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

As Editor-in-Chief, it is my pleasure to introduce a new program: Communications' digital initiative that connects leading-edge research and technology insights and breakthroughs from ACM's conferences to a much larger audience. The idea is to select compelling topics of broad interest and highlight them in a vibrant conversation with key leaders in an interactive digital event--one you can participate with live or view later via the ACM Digital Library. We held our first digital initiative in February. Below are some details about the event and link to watch it. Communications' first Digital Event was an exciting discussion with AI research leaders from academia and industry who explored how science and AI are transforming each other.

The first three entries in my "2020: A consumer electronics forecast for the year(s) ahead" piece, published back in January, all had to do with deep learning. Why? Here's part of what I wrote back then: The ability to pattern-match and extrapolate from already-identified data ("training") to not-yet-identified data ("inference") has transformed the means by which many algorithms are developed nowadays, with impact on numerous applications. This transformation is already well underway, as even a casual perusal of the titles and coverage topics of content published at EDN, EE Times, and elsewhere will make clear. Don't panic: there's still time to "catch the wave," especially if your focus is on resource-constrained implementations. But you don't want to wait too long lest you end up stuck bobbing around in the water while more foresighted colleagues are already at the beach enjoying the AI "party."

Polymeric membranes assist with a wide variety of tasks, including water filtration and gas-vapor separation. Designing a membrane for the desired function is more time-consuming than people may expect. However, researchers at Columbia Engineering, Germany's Max Planck Society and the University of South Carolina applied data science to the task to streamline their efforts. More specifically, they combined big data with machine learning to strategically design polymer membranes to act as gas filters. People frequently depend on plastic films and membranes to separate mixtures of simple cases, such as carbon dioxide and methane.

During an evening reception hosted by Computer Weekly, Oracle, Equinix and Cintra, C-level business leaders from four application providers/independent software vendors (ISVs) will discuss their strategies on machine learning, automation and cloud transformation. Facilitated by Bryan Glick, Editor-in-Chief of Computer Weekly, the session will explore how these businesses drive innovation, new market opportunities and revenue growth.