Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

49417 results in Computer Science

Page 17 of 2471

  • 4 - Technical Solutions to AI Risks and Harms

  • Onur Bakiner, Seattle University
  • Book:
    Governing AI
    Published online:
    20 February 2026
    Print publication:
    19 February 2026, pp 73-89

  • Summary

    Chapter 4 is devoted to technical solutions to rectify AI risks and harms. AI for social good projects, human-in-the-loop solutions, de-biasing, AI-generated text, image, and voice detection and testing are presented as potential technical fixes. Detecting AI-generated content remains a major challenge. Human-in-the-loop solutions and testing have proven to be such common-sense practices that they are promoted by AI-producing or -using businesses themselves as well as by laws. AI for social good projects and de-biasing produce a positive impact, but there seems to be a gap between expectations and reality. As is documented throughout this book, the roots of AI risks and harms are not technical; therefore, technical solutions cannot bring about transformative change in the face of AI risks and harms.

  • 7 - Forcing

  • Antonio Montalbán, University of California, Berkeley
  • Book:
    Computable Structure Theory
    Published online:
    30 January 2026
    Print publication:
    19 February 2026, pp 101-124

  • Summary

    Forcing is extensively used in computability and computable structure theory. We use it to show various classical computable structure theory results.The version we developed here is only aesthetically new, and matches the style of the definitions of the book.

  • 14 - Nonconcave Games: A Challenge for Game Theory’s Next 100 Years

  • from Part IV - Computer Science
  • Edited by Mark Voorneveld, Stockholm School of Economics, Jörgen W. Weibull, Tommy Andersson, Roger Myerson, Jean-François Laslier, Rida Laraki, Yukio Koriyama
  • Book:
    One Hundred Years of Game Theory
    Published online:
    03 March 2026
    Print publication:
    19 February 2026, pp 124-144

  • Summary

    A pervasive assumption in game theory is that players’ utilities are concave, or at least quasiconcave, with respect to their own strategies. While mathematically instrumental, enabling the existence of many kinds of equilibria in many kinds of settings, (quasi)concavity of payoffs is too restrictive an assumption. For the same reasons that (quasi)concave utilities can only go so far in capturing single-agent optimization problems, they can only go so far in modeling the considerations of an agent in a strategic interaction. Besides, the study of games with nonconcave utilities is increasingly coming to the fore as deep learning ventures into multiagent learning applications. This chapter studies whcih types of equilibria exist in such games, and whether they are computationally tractable, proposing paths for game theory and multiagent learning in the next 100 years.

  • Can a higher-order Markov chain be treated as a first-order Markov chain?

  • Jianhong Xu
  • Journal:
    Probability in the Engineering and Informational Sciences , First View
    Published online by Cambridge University Press:
    19 February 2026, pp. 1-16
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • It is well known that any higher-order Markov chain can be associated with a first-order Markov chain. In this primarily expository article, we present the first fairly comprehensive analysis of the relationship between higher-order and first-order Markov chains, together with illustrative examples. Our main objective is to address the central question as posed in the title.

Page 17 of 2471