The Road to General Intelligence

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

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

What's a face worth: Noneconomic factors in game playing

Where behavior defies economic analysis, one explanation is that individuals consider more than the immediate payoff. We present evidence that noneconomic factors influence behavior. Attractiveness influences offers in the Ultimatum and Dictator Games. Facial resemblance, a cue of relatedness, increases trusting in a two-node trust game. Only by considering the range of possible influences will game-playing behavior be explained

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Architektury kognitywne, czyli jak zbudować sztuczny umysł

Architektury kognitywne (AK) są próbą stworzenia modeli komputerowych integrujących wiedzę o działaniu umysłu. Ich zadaniem jest implementacja konkretnych schematów działania funkcji poznawczych umożliwiająca testowanie tych funkcji na szerokiej gamie zagadnień. Wiele architektur kognitywnych opracowano w celu symulacji procesu komunikacji pomiędzy człowiekiem i złożonymi maszynami (HCI, Human-Computer Interfaces), symulowania czasów reakcji oraz różnych psychofizycznych zależności. Można to do pewnego stopnia osiągnąć budując modele układu poznawczego na poziomie symbolicznym, z wiedzą w postaci reguł logicznych. Istnieją też projekty, które próbują powiązać procesy poznawcze z aktywacją modułów reprezentujących konkretne obszary mózgu, zgodnie z obserwacjami w eksperymentach z funkcjonalnym rezonansem magnetycznym (fMRI). Dużą grupę stanowią architektury oparte na podejściu logicznym, które mają na celu symulację wyższych czynności poznawczych, przede wszystkim procesów myślenia i rozumowania. Niektóre z projektów rozwoju architektur poznawczych skupiają większe grupy badawcze działające od wielu dziesięcioleci. Ogólnie architektury kognitywne podzielić można na 3 duże grupy: architektury symboliczne (oparte na funkcjonalnym rozumieniu procesów poznawczych); architektury emergentne, oparte na modelach koneksjonistycznych; oraz architektury hybrydowe, wykorzystujące zarówno modele neuronowe jak i reguły symboliczne. W ostatnich latach znacznie wzrosło zainteresowanie architekturami inspirowanymi przez neurobiologię (BICA, Brain Inspired Cognitive Architectures). Jak sklasyfikować różne architektury, jakie wyzwania należy przed nimi postawić, jak oceniać postępy w ich rozwoju, czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju oraz wysunięcie własnych propozycji budowy nowej architektury

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Unifying Deduction, Induction, and Analogy by the AMBR Model

This paper presents a series of simulations performed with the AMBR model that demonstrate how deduction, induction, and analogy can emerge from the interaction of several simple mechanisms. First, a case of deductive reasoning is demonstrated when a problem is solved based on general knowledge. The system represents the target in different ways depending on the goal, and different solutions are generated. Second, the constructed solutions of the problems are remembered and later on used as a base for remote analogy. Finally, on the basis of the analogy made, a generalized solution of the class of problems is induced. One important characteristic of the model is that representation of the task, problem-solving, and learning are not viewed as separate modules. Instead, they are different aspects of one and the same joined work of the basic mechanisms of the architecture