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Showing 1–5 of 5 results
Advanced filters: Author: Risto Miikkulainen Clear advanced filters

  • For a third year in a row, we followed up with authors of several recent Comments and Perspectives in Nature Machine Intelligence about what happened after their article was published: how did the topic they wrote about develop, did they gain new insights, and what are their hopes and expectations for AI in 2022?

    • Cameron Buckner
    • Risto Miikkulainen
    • Vidushi Marda
    Special Features
    Nature Machine Intelligence
    Volume: 4, P: 5-10

  • Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

    • Risto Miikkulainen
    • Stephanie Forrest
    Reviews
    Nature Machine Intelligence
    Volume: 3, P: 9-15

  • Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • Jeff Clune
    • Risto Miikkulainen
    Reviews
    Nature Machine Intelligence
    Volume: 1, P: 24-35

  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
    Reviews
    Nature Machine Intelligence
    Volume: 4, P: 196-210