Many important real-world scenarios involve data structured as graphs.

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.

Readers respond to Rachel Aviv's report on a schizophrenia patient who turned out to have an autoimmune disease, Zach Helfand's Talk of the Town story about Youman Wilder, and Hua Hsu's article on A.I. and education. As a practicing rheumatologist (that is, a doctor who cares for patients with autoimmune diseases), I read Rachel Aviv's article about schizophrenia and immune disorders with great interest ("Second Life," July 28th). I frequently meet patients with lupus whose disease has caused severe neuropsychiatric symptoms, and watching them emerge from their cognitive cocoons after immunosuppressive treatment is always breathtaking. Other autoimmune neurologic phenomena can present in equally bizarre ways. Some people with antiphospholipid syndrome--a condition associated with the formation of blood clots--can experience chorea (involuntary muscle movements) or acute changes in cognition (one of my patients was diagnosed after becoming markedly confused).

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.

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Sam Altman is leaving no room for doubt about his views on an Elon Musk-led bid to take control of OpenAI. In a letter to OpenAI staff Monday, the CEO put the words "bid" and "deal" in scare quotes and said the startup's board has no interest in the offer. "Our structure exists to ensure that no individual can take control of OpenAI," Altman wrote, according to two sources with knowledge of the letter. "Elon runs a competitive AI company, and his actions are not about OpenAI's mission or values." Altman has also told employees that OpenAI's board, which he sits on, has yet to receive an official offer from Musk and the other investors.

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.

Siri on your phone, online shopping showing you items that it has learned that you'll want, social media advertisements based on your online habits, automatic lane assist in your car, and so much more.