This research presents a computational social science approach to preserving Telugu Chandassu, the metrical poetry tradition representing centuries of collective cultural intelligence. We develop the first comprehensive digital framework for analyzing Telugu prosodic patterns, bridging traditional community knowledge with modern computational methods. Our social computing approach involves collaborative dataset creation of 4,651 annotated padyams, expert-validated linguistic patterns, and culturally-informed algorithmic design. The framework includes AksharamTokenizer for prosody-aware tokenization, LaghuvuGuruvu Generator for classifying light and heavy syllables, and PadyaBhedam Checker for automated pattern recognition. Our algorithm achieves 91.73% accuracy on the proposed Chandassu Score, with evaluation metrics reflecting traditional literary standards. This work demonstrates how computational social science can preserve endangered cultural knowledge systems while enabling new forms of collective intelligence around literary heritage. The methodology offers insights for community-centered approaches to cultural preservation, supporting broader initiatives in digital humanities and socially-aware computing systems.

Rodriguez, the President of the National Hispanic Christian Leadership Conference, spoke at Trump's first presidential inauguration in 2017. Moore, who is also the founder of the public relations firm Kairos, served on Trump's Evangelical executive board during his first presidential candidacy. The letter is a sign of growing ties between religious and AI safety groups, which share some of the same worries. It was shared with journalists by representatives of the Future of Life Institute--an AI safety organization that campaigns to reduce what it sees as the existential risk posed by advanced AI systems. The world's biggest tech companies now all believe that it is possible to create so-called "artificial general intelligence"--a form of AI that can do any task better than a human expert. Some researchers have even invoked this technology in religious terms--for example, OpenAI's former chief scientist Ilya Sutskever, a mystical figure who famously encouraged colleagues to chant "feel the AGI" at company gatherings.

Re your article ('Dystopian' tool aims to predict murder, 9 April), the collection and automation of data has repeatedly led to the targeting of racialised and low-income communities, and must come to an end. This has been found by both Amnesty International in our Automated Racism report and by Statewatch in its findings on the "murder prediction" tool. For many years, successive governments have invested in data-driven and data-based systems, stating they will increase public safety – yet individual police forces and Home Office evaluations have found no compelling evidence that these systems have had any impact on reducing crime. Feedback loops are created by training these systems using historically discriminatory data, which leads to the same areas being targeted once again. These systems are neither revelatory nor objective.

Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle platooning framework based on model-predictive-control (MPC) strategies and present its objective optimization as a cooperative quadratic cost function. Then, we propose a distributed algorithm to locally optimize this objective at every vehicle subject to data quantization over the communication network of vehicles. In contrast to most existing literature that assumes ideal communication channels, log-scale data quantization over the network is addressed in this work, which is more realistic and practical. In particular, we show by simulation that the proposed log-quantized algorithm reaches optimal convergence with less residual and optimality gap. This outperforms the existing literature considering uniform quantization which leads to a large optimality gap and residual.

Naomi Klein's article about the dangers of generative AI makes many valid points about the economic and social consequences of the new technology (AI machines aren't'hallucinating'. But their makers are, 8 May). But her choice of language about how to describe the mistakes that the new AI makes seems to suggest she is committed mainly to providing an ideological interpretation of the new technology. Saying that mistakes are the results of glitches in the code rather than the tech hallucinating suggests the simulation is a simple one, involving a kind of power of the false rather than a more complex one that allows the possibility of some form of fabulation. This is important because it means that the technology can't be seen simply as a control technology, like nuclear fusion or self-driving cars, but instead indicates a switch to an adaptive form of technology, ie, ones that are based on adapting what is already out there rather than trying to reinvent what exists, as in some form of innovation.

AI is not only about Alexa and Siri; it also is used in facial recognition technology, Deepfakes, YouTube recommendations, autonomous cars, weapons and more. Consequently, AI has serious effects on privacy, surveillance, our access to information, and how we the people might be steered in regards to elections and governance. Thus, the effects of AI on the First, Fourth, Ninth and 10th amendments are worthy of reasoned and thoughtful conversations, conversations that even academics at Harvard, Duke and elsewhere indicate are unresolved.

You are right to acknowledge the work of Donald Michie (full disclosure: I'm his son) on artificial intelligence developing new insights rather than relying on brute force, and on the importance of AI communicating these insights to humans (The Guardian view on bridging human and machine learning: it's all in the game, 30 March). This pioneering work is important for the reasons you explain; it also speaks to debates on whether the rise of the robots will result in them enslaving us. My father argued that it was vital that the robots and AI of the future must be required (programmed) to explain what they were doing and why in terms understandable to humans. Without that, we really will be in trouble – from the routine (why did the driverless car crash?) to the existential. Your editorial was interesting, but NooK, the AI system it discussed, did not play bridge.

As the director of Just Alternatives, a nonprofit that supports survivors of criminal violence and helps enable dialogues between victims and offenders, I was inspired by Eren Orbey's piece about Katie Kitchen's forgiveness of Joseff Deon White, the man convicted of murdering her father ("Fault Lines," January 24th). In my twenty-one years of working with survivors, I have found that most of the ones who initiate victim-offender dialogues (V.O.D.s), in the states that allow them to do so, aren't necessarily looking to forgive. Rather, they are often seeking to express some of the anguish and trauma that they have endured, and to ask questions that only the offenders can answer. Those of us who facilitate these dialogues understand that we cannot compel offenders to tell the whole story; admittedly, when they don't, some of us are left with the feeling that they don't "deserve" forgiveness. Although forgiveness may seem like the highest ideal, V.O.D.s are much more about giving survivors a chance to work through, and to convey to offenders, the impact of a crime.

However, I believe that the distinction of "neats" and "scruffies" raised at Cog Sci in '81 didn't define scruffies as people who built expert systems [they didn't really exist as a "real" part of MAD. Instead, I believe AI These are the researchers who read Hawkings and say "gee, if his model of the lo-23 second big bang is right, then the distribution of intergalactic gases should be relatively even. Let's go see if that's true. However, to run our experiments we'll need a more sensitive space-based sensing device, so let's work with the engineers to design one." I think one could make the case (although not from the data collected in Cohen's survey) that the two methodologies are not informed and influenced by each other to the extent they should or could be.

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