Reflections on the Evolution of Computer Science Education

• URL: http://arxiv.org/abs/2208.04713v1
• Date: Sat, 9 Jul 2022 07:07:12 GMT
• Title: Reflections on the Evolution of Computer Science Education
• Authors: Sreekrishnan Venkateswaran
• Abstract summary: Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory; the programme structure did not allow much of a choice or variety. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education to reflect the on-going lateral acceleration of Computer Science. Fundamental discoveries in artificial intelligence, machine learning, virtualization and cloud computing are several decades old. Many core theories in data science are centuries old. Yet their leverage exploded only after Circa 2010, when the stage got set for people-centric problem solving in massive scale. This was due in part to the rush of innovative real-world applications that reached the common man through the ubiquitous smart phone. AI/ML modules arrived in popular programming languages; they could be used to build and train models on powerful - yet affordable - compute on public clouds reachable through high-speed Internet connectivity. Academia responded by adapting Computer Science curricula to align it with the changing technology landscape. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education.

Related papers

• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. We release QUATRO, a collection of quantum computing applications from cognitive models.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• Redefining Computer Science Education: Code-Centric to Natural Language Programming with AI-Based No-Code Platforms [0.0]
  This paper delves into the evolving relationship between humans and computers in the realm of programming. The advent of AI-based no-code platforms is revolutionizing this dynamic. As educators, it's imperative to integrate this new dynamic into curricula.
  arXiv  Detail & Related papers  (2023-08-19T02:44:35Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Intelligent Computing: The Latest Advances, Challenges and Future [42.996665904233375]
  Intelligent computing is a new computing paradigm that is reshaping traditional computing. We present the first comprehensive survey of literature on intelligent computing. It covers the theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives.
  arXiv  Detail & Related papers  (2022-11-21T09:15:13Z)
• Physical Computing for Materials Acceleration Platforms [81.09376948478891]
  We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
  arXiv  Detail & Related papers  (2022-08-17T23:03:54Z)
• Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures [6.440694188229122]
  This course aims to teach students for designing AI accelerators on FPGA platforms. The elaborated course contents include lecture notes and related technical materials. Some teaching experiences and effects are discussed as well as some potential improvements in the future.
  arXiv  Detail & Related papers  (2022-06-22T11:48:04Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Computer-Aided Personalized Education [15.811740322935476]
  The number of US students taking introductory courses has grown three-fold in the past decade. Massive open online courses (MOOCs) have been promoted as a way to ease this strain. Personalized education relying on computational tools can address this challenge.
  arXiv  Detail & Related papers  (2020-07-07T18:00:04Z)
• Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing [25.16076541420544]
  Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning has been successfully applied in areas such as object/pattern recognition, speech and natural language processing, self-driving vehicles, intelligent self-diagnostics tools, autonomous robots, knowledgeable personal assistants, and monitoring. This paper reviews the case for a novel beyond CMOS hardware technology, memristors, as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks.
  arXiv  Detail & Related papers  (2020-04-30T16:49:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.