Toward Human-Level Artificial Intelligence

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A01=Eitan Michael Azoff
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Author_Eitan Michael Azoff
automatic-update
brain-inspired computing
Category1=Non-Fiction
Category=PSAN
Category=UYQ
cognition
cognitive neuroscience
computational modelling
COP=United Kingdom
deep learning
Delivery_Pre-order
eq_bestseller
eq_computing
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eq_nobargain
eq_non-fiction
eq_science
GPU
Language_English
machine perception
neural networks
neurons
neurorobotics systems
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Price_€50 to €100
PS=Forthcoming
scientific method in artificial intelligence
softlaunch
synaptic plasticity

Product details

  • ISBN 9781032829074
  • Weight: 320g
  • Dimensions: 156 x 234mm
  • Publication Date: 18 Sep 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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Is a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book’s mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts and provides in-depth surveys of neuroscience and AI research related to the book's mission, providing all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including:

  • The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence.
  • Diffuse decision making by ensembles of neurons.
  • The inside-out model to give HLAI an inner "life" and the possible role for cognitive architecture implementing the scientific method through the plan-do-check-act cycle within that model (learning to learn).
  • A neuromodulation feature such as a machine equivalent of dopamine that reinforces learning.
  • The embodied HLAI machine, a neurorobot, that interacts with the physical world as it learns.

This book concludes by explaining the hypothesis that computer simulation is sufficient to take AI research further toward HLAI and that the scientific method is our means to enable that progress. This book will be of great interest to a broad audience, particularly neuroscientists and AI researchers, investors in AI projects, and lay readers looking for an accessible introduction to the intersection of neuroscience and artificial intelligence.

Eitan Michael Azoff, PhD, is Chief Analyst at Cloud and Data Center Research Practice, Omdia, part of Informa.

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