Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks

An insightful investigation into the mechanisms underlying the predictive functions of neural networks--and their ability to chart a new path for AI.

Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems.

Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today's deep learning. Digging into the connections between predictions and gradients, and their manifestation in the brain and neural networks, is one compelling example of how Downing enriches both our understanding of such relationships and their role in strengthening AI tools.

Synthesizing critical research in neuroscience, cognitive science, and connectionism, Gradient Expectations offers unique depth and breadth of perspective on predictive neural-network models, including a grasp of predictive neural circuits that enables the integration of computational models of prediction with evolutionary algorithms.

一個深入探討神經網絡預測功能機制的洞察性調查，以及它們為人工智慧開拓新道路的能力。

預測是一種與生存和繁榮密切相關的認知優勢。我們的大腦充滿了體現預測的信號。我們能否更明確地將這種能力擴展到合成神經網絡中，以改善人工智慧的功能並增強其在我們世界中的地位？ 《梯度期望》是Keith L. Downing的一項大膽努力，旨在對自然和人工神經網絡的起源和解剖進行映射，以探索如何將其組件設計為預測模塊，從而為進化模擬高級神經網絡系統奠定基礎。

Downing深入研究了哺乳動物大腦已知的神經結構，以闡明預測網絡的結構，並更精確地確定預測能力如何從更原始的神經回路進化而來。然後，他回顧了過去和現在利用具有生物合理性的預測機制的計算神經模型，識別出自然和人工網絡共享的元素，例如梯度。Downing發現，良好基礎的預測背後存在著梯度，但其範圍與當今深度學習的梯度不同。深入探討預測和梯度之間的聯繫，以及它們在大腦和神經網絡中的表現，是Downing豐富我們對這些關係的理解以及它們在增強人工智慧工具中的作用的一個引人注目的例子。

《梯度期望》綜合了神經科學、認知科學和聯結主義的重要研究，提供了對預測神經網絡模型獨特的深度和廣度的觀點，包括對預測神經回路的把握，使得預測計算模型能夠與進化算法相結合。

Keith L. Downing is Professor of Artificial Intelligence and Artificial Life at the Norwegian University of Science and Technology and the author of Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems (MIT Press).

Keith L. Downing 是挪威科技大學的人工智慧和人工生命學教授，也是《Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems》（麻省理工學院出版社）的作者。