The Road to General Intelligence

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

   Pages 1-4Open Access

3. Requirements

   1. Front Matter

      Pages 5-5

      PDF

   2. Background

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 7-15Open Access

   3. Where is My Mind?

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 17-22Open Access

   4. Challenges for Deep Learning

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 23-32Open Access

   5. Challenges for Reinforcement Learning

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 33-38Open Access

   6. Work on Command: The Case for Generality

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 39-47Open Access

4. Semantically Closed Learning

   1. Front Matter

      Pages 49-49

      PDF

   2. Philosophy

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 51-63Open Access

   3. Architecture

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 65-72Open Access

   4. A Compositional Framework

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 73-90Open Access

   5. 2nd Order Automation Engineering

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 91-107Open Access

   6. Prospects

      • Jerry Swan, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, Bas Steunebrink

      Pages 109-118Open Access

5. Back Matter

   Pages 119-136

   PDF

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory.
• Details the pragmatic requirements for real-world General Intelligence.
• Describes how machine learning fails to meet these requirements.
• Provides a philosophical basis for the proposed approach.
• Provides mathematical detail for a reference architecture.
• Describes a research program intended to address issues of concern in contemporary AI.
The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts

This is an open access book.

• Computational Intelligence
• Artificial Intelligence
• Machine Learning
• General Intelligence
• AI
• Open Access

• NNAISENSE SA, Lugano, Switzerland

  Jerry Swan, Eric Nivel, Timothy Atkinson, Bas Steunebrink

• NVIDIA, Santa Clara, USA

  Neel Kant

• University of Strathclyde, Glasgow, UK

  Jules Hedges