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.

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.

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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Editor: Jerome Feldman's "Essay Concerning Robotic Understanding" (AI Magazine, Fall 1990) shows a remarkable naivete about humans. Although he admits to some limitations on human understanding (understanding/h): "We actually use understanding/h loosely, normally excluding infants, idiots and so on. We acknowledge that there are strong limitations on the extent to which we can convey understanding/h across barriers of gender, race and culture." If we are using understanding/h in Locke's sense, to mean reason, with all its eighteenth century freight, including the exclusion of women and blacks from the category of reasoning beings, there are, strangely enough, no barriers to this category for machines. After all, Boole later invented his logic to help mechanize the process of jurisprudence.

At the risk of being scolded again for "employing universal truths and unarguable facts" in support of my position, I must point out that it is the responsibility of a scientist or engineer to document clearly the known limitations of any method he develops and publishes. In addition to truth in packaging, a clear and unblinking examination of the limitations of one's own work is an invaluable guide to further research. Akman observes, correctly, that QSIM is a purely mathematical formalism for expressing qualitative differential equation models of the world, and not a physical modeling methodology. Our research group has also been concerned with this limitation, so we have developed modelbuilding methods which compile QDEs for QSIM to simulate, either from a component-connection description of a device (Franke and Dvorak 1989, 1990), or from a physical scenario description via qualitative views and processes (Crawford, Farquhar, and Kuipers 1990). These two model-building methods are important elements of the QSIM perspective on qualitative reasoning (Kuipers 1989).

A basic promise of AI research is that what we observe as human intelligence is in fact a computation either directly or as an emergent effect. An attempt at classifying and distinguishing types of AI researchers was to call them all either scruffy (those that wrote code and implemented systems) or neat (those that base AI on some formalism like first order predicate calculus). Out of necessity, researchers tend to focus on a particular aspect of intelligence to simulate. When this is done, the effect is to restrict the class of computations that are being considered. The goal is build pieces of intelligence.

Definition 2. An agent's knowledge is the set of all statements that the agent knows (i.e., the set [s: the agent knows s]). An agent's problem-solving behavior is

Although they then cite a "slow, rigorous proof" by K. Hornik et al. that implies the superiority of neural systems to digital ones, the contrast needs to be reemphasized. In practice, since neural nets and Turing-equivalent systems are simulated on systems constructed from digital integrated circuits, they are of course equivalent. However, in theory, the fundamental computations of neural networks depend on the arithmetic of real numbers rather than integers. The ideal neural unit computes in a noisefree, infinite precision fashion. These computations can be simulated arbitrarily closely by a Turing machine, yet as the Greek philosopher Zeno observed 2200 years ago, the continuous computation can attain values in a fixed time that the digital approximation with uniform timestep will take infinite time to reach.