Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines

Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems.

The reduced error logistic regression (RELR) method is proposed as such a "Calculus of Thought." This book reviews how RELR's completely automated processing may parallel important aspects of explicit and implicit learning in neural processes. It emphasizes the fact that RELR is really just a simple adjustment to already widely used logistic regression, along with RELR's new applications that go well beyond standard logistic regression in prediction and explanation. Readers will learn how RELR solves some of the most basic problems in today’s big and small data related to high dimensionality, multi-colinearity, and cognitive bias in capricious outcomes commonly involving human behavior.

• Provides a high-level introduction and detailed reviews of the neural, statistical and machine learning knowledge base as a foundation for a new era of smarter machines
• Argues that smarter machine learning to handle both explanation and prediction without cognitive bias must have a foundation in cognitive neuroscience and must embody similar explicit and implicit learning principles that occur in the brain

《思維微積分：認知機器中的神經形態邏輯回歸》是一本對於科學家來說必讀的書籍，介紹了一種非常簡單的計算方法，旨在模擬大數據神經處理。這本書的靈感來自於哥德弗里德·萊布尼茨的「邏輯計算器」概念，即機器計算應該發展成能夠模擬人類認知過程，從而避免在解決實際和科學問題時出現問題性的主觀偏見。

書中提出了一種名為「簡化誤差邏輯回歸（RELR）」的方法，作為這種「思維微積分」的實現。本書回顧了RELR的完全自動化處理如何與神經過程中的顯性和隱性學習相平行。它強調了RELR實際上只是對已廣泛使用的邏輯回歸進行了簡單的調整，以及RELR在預測和解釋方面的新應用，遠遠超出了標準邏輯回歸的範疇。讀者將了解到RELR如何解決當今與人類行為常見的多維度、多重共線性和認知偏見相關的大數據和小數據中的一些基本問題。

本書提供了高層次的介紹，並詳細回顧了神經、統計和機器學習知識庫，作為智能機器新時代的基礎。書中主張，要使更智能的機器學習能夠在解釋和預測方面處理認知偏見，必須建立在認知神經科學的基礎上，並體現出與大腦中發生的顯性和隱性學習原理相似的特點。