What to Read: A Biased Guide to AI Literacy for the Beginner

Acknowledgements. It was Ken Forbus' idea, and he, Howie Shrobe, Dan Weld, and John Batali read various drafts. Dan Huttenlocher and Tom Knight helped with the speech recognition section. The science fiction section was prepared with the aid of my SF/AI editorial board, consisting of Carl Feynman and David Wallace, and of the ArpaNet SF-Lovers community. Even so, all responsibility rests with me.This note tries to provide a quick guide to AI literacy for the beginning AI hacker and for the experienced AI hacker or two whose scholarship isn't what it should be. most will recognize it as the same old list of classic papers, give or take a few that I feel to be under- or over-rated. It is not guaranteed to be thorough or balanced or anything like that.MIT Artificial Intelligence Laborator

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

COMPUTE DEPRESSION AND ANXIETY AMONG STUDENTS IN PAKISTAN, USING MACHINE LEARNING

The worldwide mechanical advancement in medical services digitizes the copious information, empowering the guide of the different types of human science all the more precisely than conventional estimating strategies. AI (ML) has been certified as a productive approach for dissecting the enormous measure of information in the medical services area. ML strategies are being used in emotional well-being to anticipate the probabilities of mental problems and, subsequently, execute potential treatment results. In the speedy present-day world, mental medical problems like depression and anxiety have become exceptionally normal among the majority. In this paper, forecasts of depression and anxieties were made utilizing AI calculations. Depression and anxiety have become emergent hindrances in the lives of human beings. It not only disturbs their daily decorum but has also become a prominent cause for their downfall in health. All around the world people are getting affected by this mental disorder yet the majority of such cases lie between ages 18-25 making university-going students a prime target for such mental diseases. Though the mental health of university students is known globally as a momentous public health matter. Academicals, social depression, and anxieties are playing quite a negative role in university student’s life, especially in forms of mental illness like depression and anxiety. These mental health issues are becoming a major constraint on their studies and career. Hence, this research is being conducted to develop a technological solution for mentally distorted students. This paper analyzes depression and anxiety amongst university students by effectively utilizing the k-nn algorithm (a conspicuous technique for detecting and analyzing mental depression and anxiety) and providing a technical solution for this mental hindrance. The experimental results show up to 76.5% accuracy in results after using k-nn without PCA while the accuracy was increased up to 76.6% when the results were generated with PCA

The epistemological and philosophical situation of Mind Techno-Science

1st version: Stanford Humanities Review, Special issue: Constructions of the mind: Artificial Intelligence and the Humanities, vol. 4, n° 2, 1995, pp 267-284.The goal is to analyze the presuppositions of cognitive sciences, to characterize and situate their argumentative models and explain the social and epistemological conjuncture: the reconstruction today of a theory of the subject as the source and ground of knowledg

Trustworthy Formal Natural Language Specifications

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants to formally verify the correctness of software with respect to a natural language specification. The translation from informal to formal remains a challenging, time-consuming process that is difficult to audit for correctness. This paper shows that it is possible to build support for specifications written in expressive subsets of natural language, within existing proof assistants, consistent with the principles used to establish trust and auditability in proof assistants themselves. We implement a means to provide specifications in a modularly extensible formal subset of English, and have them automatically translated into formal claims, entirely within the Lean proof assistant. Our approach is extensible (placing no permanent restrictions on grammatical structure), modular (allowing information about new words to be distributed alongside libraries), and produces proof certificates explaining how each word was interpreted and how the sentence's structure was used to compute the meaning. We apply our prototype to the translation of various English descriptions of formal specifications from a popular textbook into Lean formalizations; all can be translated correctly with a modest lexicon with only minor modifications related to lexicon size.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0781