Artificial General Intelligence : A Gentle Introduction


[The video of a lecture based on this note is on-line.]

1. From AI to AGI

Historical development

Artificial Intelligence (AI) started with "thinking machine" of human-comparable intelligence as the ultimate goal, as documented by the following literature: In the past, there were some ambitious projects aiming at this goal, though they all failed. The best-known examples include the following ones: Partly due to the realized difficulty of the problem, in the 1970s-1980s mainstream AI moved away from general-purpose intelligent systems, and turned to domain-specific problems and special-purpose solutions, though there are opposite attitudes toward this change: Consequently, the field currently called "AI" consists of many loosely related subfields without a common foundation or framework, and suffers from an identity crisis:
  • External recognition: As soon as a problem is solved, it is no longer considered as requiring "intelligence" anymore, so AI rarely gets credit.
  • Internal fragmentation: The subfields of AI become less and less related to one another.

Recent attitude change

In recent years (since 2004), calls for research on general-purpose systems returned, both inside and outside mainstream AI.

Anniversaries are good time to review the big picture of the field. In the following collections and events, many well-established AI researchers raised the topic of general-purpose and human-level intelligence:

More or less coincidentally, from outside mainstream AI, there are several books with bold titles and novel technical plans to produce intelligence as a whole in computers: There are also several less technical but more influential books, with the same optimism on the possibility of building general-purpose AI: So after several decades, "general-purpose system", "integrated AI", and "human-level AI" become less forbidden (though far from popular) topics, as reflected by several recent meetings (an incomplete list):
  • Achieving Human-Level Intelligence through Integrated Systems and Research, AAAI Fall Symposium (2004)
  • Towards Human-Level AI?, NIPS Workshop (2005)
  • Integrated Intelligence, AAAI Special Track (since 2006)
  • Biologically Inspired Cognitive Architectures, AAAI Fall Symposiums (2008, 2009), conferences (2010, 2011)
  • Artificial General Intelligence, workshop (2006), conferences (2008, 2009, 2010, 2011)

 

2. AGI Overview

What is Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) differs from conventional Artificial Intelligence (AI) mainly in the following aspects:
  • AGI researchers usually treat "intelligence" as a general-purpose capability or mechanism, while AI researchers often treat it as a collection of special-purpose capabilities or mechanisms;
  • AGI researchers stress the holistic or integrated nature of intelligence, while AI researchers stress the modularity of intelligent capabilities or functions;
  • AGI researchers believe the time has come for the design and development of computer systems with intelligence comparable with that of human beings, while many AI researchers believe it is still too early to attempt that. 
Therefore, "AI" and "AGI" were originally the same, but currently different. Similar notions include "strong AI", "human-level AI", "real AI", "thinking machine", and many others.

AGI research has a science (theory) aspect and an engineering (technique) aspect. A complete AGI work normally includes

  1. a theory of intelligence,
  2. a formal model of the theory,
  3. a computational implementation of the model.
The book chapter "Aspects of Artificial General Intelligence" clarified the notion of AGI, and responded to the following common doubts and objections of this research:
  • "AGI is impossible"
  • "There is no such a thing as general intelligence"
  • "General-purpose systems are not as good as special-purpose ones"
  • "AGI is already included in the current AI"
  • "It is too early to work on AGI"
  • "AGI is nothing but hype"
  • "AGI research is not fruitful"
  • "AGI is dangerous"

Fundamental AI/AGI questions

The most general theoretical questions every AI (AGI) researcher needs to answer include:
  1. What is AI, accurately specified?
  2. Is it possible to build the AI as specified?
  3. If AI is possible, what is the most plausible way to achieve it?
  4. Even if we know how to achieve AI, should we really do it?
My own answers to these questions are summarized here.

Most AI (AGI) researchers answer "Yes" to the 2nd and 4th questions, though some outside people say "No" to one of them. In the following we will compare the different answers to the 1st and 3rd questions, which are about the research goal and technical strategy of AI (AGI), respectively.

Answers to the 1st question

What is the concrete goal of AI research? Of course, it is "to make computers that are similar to the human mind" — but in which level of description, generalization, or abstraction should this similarity be obtained? As analyzed in What Do You Mean by "AI"?, there are five types of typical answer:
  • structure — to model human brain
  • behavior — to simulate human performance
  • capability — to solve practical problems
  • function — to have cognitive faculties
  • principle — to obey rational norms
They are all valid scientific research goals, but lead to quite different results!

Answers to the 3rd question, in AGI context

Though the goal is to produce intelligence as a whole, each AGI project still needs to divide the problem into subproblems to be solved one by one. In doing so, existing AGI projects follow technical paths that can be roughly divided into three types:
  • hybrid — to connect existing AI techniques together
  • integrated — to combine modules based on different techniques into an overall architecture
  • unified — to extend and augment a core technique in various ways
Common techniques in AGI projects include, though not limited to:
  • logic
  • probability theory
  • production system
  • knowledge base
  • learning algorithms
  • robotics
  • neural network
  • evolutionary computation
  • multi-agent system
Though each of these techniques is also explored in mainstream AI, to use it in a general-purpose system leads to very different design decisions in technical details.

 

3. Representative AGI Projects

The following projects are selected to represent existing AGI research, because each of them (1) is clearly oriented to AGI, (2) is still very active, and (3) has ample publications of technical details.

Each project is linked to the project website and two selected publications, where the following quotations are extracted. The focus of the quotations is on the research goal (the 1st question) and technical path (the 3rd question).

Soar [Unified Theories of Cognition, A Gentle Introduction to Soar]

The ultimate in intelligence would be complete rationality which would imply the ability to use all available knowledge for every task that the system encounters. Unfortunately, the complexity of retrieving relevant knowledge puts this goal out of reach as the body of knowledge increases, the tasks are made more diverse, and the requirements in system response time more stringent. The best that can be obtained currently is an approximation of complete rationality. The design of Soar can be seen as an investigation of one such approximation.

For many years, a secondary principle has been that the number of distinct architectural mechanisms should be minimized. Through Soar 8, there has been a single framework for all tasks and subtasks (problem spaces), a single representation of permanent knowledge (productions), a single representation of temporary knowledge (objects with attributes and values), a single mechanism for generating goals (automatic subgoaling), and a single learning mechanism (chunking). We have revisited this assumption as we attempt to ensure that all available knowledge can be captured at runtime without disrupting task performance. This is leading to multiple learning mechanisms (chunking, reinforcement learning, episodic learning, and semantic learning), and multiple representations of long-term knowledge (productions for procedural knowledge, semantic memory, and episodic memory).

Two additional principles that guide the design of Soar are functionality and performance. Functionality involves ensuring that Soar has all of the primitive capabilities necessary to realize the complete suite of cognitive capabilities used by humans, including, but not limited to reactive decision making, situational awareness, deliberate reasoning and comprehension, planning, and all forms of learning. Performance involves ensuring that there are computationally efficient algorithms for performing the primitive operations in Soar, from retrieving knowledge from long-term memories, to making decisions, to acquiring and storing new knowledge.

ACT-R [The Atomic Components of Thought, An Integrated Theory of the Mind]

ACT-R is a cognitive architecture: a theory for simulating and understanding human cognition. Researchers working on ACT-R strive to understand how people organize knowledge and produce intelligent behavior. As the research continues, ACT-R evolves ever closer into a system which can perform the full range of human cognitive tasks: capturing in great detail the way we perceive, think about, and act on the world.

On the exterior, ACT-R looks like a programming language; however, its constructs reflect assumptions about human cognition. These assumptions are based on numerous facts derived from psychology experiments. Like a programming language, ACT-R is a framework: for different tasks (e.g., Tower of Hanoi, memory for text or for list of words, language comprehension, communication, aircraft controlling), researchers create models (aka programs) that are written in ACT-R and that, beside incorporating the ACT-R's view of cognition, add their own assumptions about the particular task. These assumptions can be tested by comparing the results of the model with the results of people doing the same tasks.

ACT-R is a hybrid cognitive architecture. Its symbolic structure is a production system; the subsymbolic structure is represented by a set of massively parallel processes that can be summarized by a number of mathematical equations. The subsymbolic equations control many of the symbolic processes. For instance, if several productions match the state of the buffers, a subsymbolic utility equation estimates the relative cost and benefit associated with each production and decides to select for execution the production with the highest utility. Similarly, whether (or how fast) a fact can be retrieved from declarative memory depends on subsymbolic retrieval equations, which take into account the context and the history of usage of that fact. Subsymbolic mechanisms are also responsible for most learning processes in ACT-R.

Polyscheme [Polyscheme, Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence]

Polyscheme is a cognitive architecture designed to model and achieve human-level intelligence by integrating multiple methods of representation, reasoning and problem solving.

A system will be said to have human-level intelligence if it can solve the same kinds of problems and make the same kinds of inferences that humans can, even though it might not use mechanisms similar to those humans in the human brain. The modifier "human-level" is intended to differentiate such systems from artificial intelligence systems that excel in some relatively narrow realm, but do not exhibit the wide-ranging cognitive abilities that humans do.

A key insight ... is that AI algorithms from different subfields based on different computational formalisms can all be conceived of as strategies guiding attention through propositions in the multiverse [the set of all possible worlds].

LIDA [The Lida Architecture, A Cognitive Theory of Everything]

Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive "theory of everything." With modules or processes for perception, working memory, episodic memories, "consciousness," procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA technology is based on the LIDA cognitive cycle, a sort of "cognitive atom." The more elementary cognitive modules play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles.

The LIDA architecture represents perceptual entities, objects, categories, relations, etc., using nodes and links .... These serve as perceptual symbols acting as the common currency for information throughout the various modules of the LIDA architecture.

SNePS [SNePS: A Logic for Natural Language Understanding and Commonsense Reasoning, The GLAIR Cognitive Architecture]

The long term goal of the SNePS Research Group is to understand the nature of intelligent cognitive processes by developing and experimenting with computational cognitive agents that are able to use and understand natural language, reason, act, and solve problems in a wide variety of domains.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePS-based agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as logical sentences. The effect is that propositions can occur as arguments of propositions, acts, and policies without limit, and without leaving first-order logic.

Cyc [Building Large Knowledge-Based Systems, Common Sense Reasoning]

Vast amounts of commonsense knowledge, representing human consensus reality, would need to be encoded to produce a general AI system. In order to mimic human reasoning, Cyc would require background knowledge regarding science, society and culture, climate and weather, money and financial systems, health care, history, politics, and many other domains of human experience. The Cyc Project team expected to encode at least a million facts spanning these and many other topic areas.

The Cyc knowledge base (KB) is a formalized representation of a vast quantity of fundamental human knowledge: facts, rules of thumb, and heuristics for reasoning about the objects and events of everyday life. The medium of representation is the formal language CycL. The KB consists of terms -- which constitute the vocabulary of CycL -- and assertions which relate those terms. These assertions include both simple ground assertions and rules.

AIXI [Universal Artificial Intelligence, Universal Algorithmic Intelligence: A mathematical top->down approach]

An important observation is that most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility function.

Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible.

The major drawback of the AIXI model is that it is uncomputable, ... which makes an implementation impossible. To overcome this problem, we constructed a modified model AIXItl, which is still effectively more intelligent than any other time t and length l bounded algorithm.

NARS [Rigid Flexibility: The Logic of Intelligence, From NARS to a Thinking Machine]

What makes NARS different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. NARS attempts to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, etc, so as to provide a unified theory, model, and system for AI as a whole. The ultimate goal of this research is to build a thinking machine.

The development of NARS takes an incremental approach consisting four major stages. At each stage, the logic is extended to give the system a more expressive language, a richer semantics, and a larger set of inference rules; the memory and control mechanism are then adjusted accordingly to support the new logic.

In NARS the notion of "reasoning" is extended to represent a system's ability to predict the future according to the past, and to satisfy the unlimited resources demands using the limited resources supply, by flexibly combining justifiable micro steps into macro behaviors in a domain-independent manner.

Novamente [The Hidden Pattern: A Patternist Philosophy of Mind, An Integrative Architecture for General Intelligence]

Novamente incorporates aspects of many previous AI paradigms such as agent systems, evolutionary programming, reinforcement learning, automated theorem-proving, and probabilistic reasoning. However, it is unique in its overall architecture, which confronts the problem of creating a holistic digital mind in a direct way that has not been done before.

General Intelligence is the ability to achieve complex goals in complex environments.

Novamente essentially consists of a framework for tightly integrating various AI algorithms in the context of a highly flexible common knowledge representation, and a specific assemblage of AI algorithms created or tweaked for tight integration in an integrative AGI context.

HTM [On Intelligence, Hierarchical Temporal Memory]

The brain uses vast amounts of memory to create a model of the world. Everything you know and have learned is stored in this model. The brain uses this memory-based model to make continuous predictions of future events. It is the ability to make predictions about the future that is the crux of intelligence.

Hierarchical Temporal Memory (HTM) is a technology that replicates the structural and algorithmic properties of the neocortex. HTM therefore offers the promise of building machines that approach or exceed human level performance for many cognitive tasks.

HTMs are organized as a tree-shaped hierarchy of nodes, where each node implements a common learning and memory function. HTMs store information throughout the hierarchy in a way that models the world. All objects in the world, be they cars, people, buildings, speech, or the flow of information across a computer network, have structure. This structure is hierarchical in both space and time. HTM memory is also hierarchical in both space and time, and therefore can efficiently capture and model the structure of the world.

A rough classification

The above AGI projects are roughly classified in the following table, according to the type of their answers to the previously listed 1st question (on research goal) and 3rd question (on technical path).

goal \ path hybrid integrated unified
principle     AIXI, NARS
function   LIDA, Novamente, Polyscheme SNePS, Soar
capability     Cyc
behavior     ACT-R
structure     HTM

Since this classification is made at a high level, projects in the same entry of the table are still quite different in the details of their research goals and technical paths.

In summary, the current AGI projects are based on very different theories and techniques.

 

4. AGI Literature and Resource

The earliest collection of AGI works is Artificial General Intelligence. Though this book was published in 2007, the manuscript was finished in 2003. The publisher website provides free download for the table of contents and the introductory chapter "Contemporary Approaches to Artificial General Intelligence". Most chapters in the collection can be found at the authors' websites.

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms is a post-conference proceedings of the 2006 AGI Workshop. The introductory chapter "Aspects of Artificial General Intelligence" clarified the notion of AGI and summarized the other chapters. The Workshop website contains links to all the chapters in the collection, plus some presentations and videos.

The annual AGI international conference series was started in 2008. The conference websites (AGI-08, AGI-09, AGI-10, AGI-11) link to all accepted papers, plus additional materials.

Journal of Artificial General Intelligence is a peer-reviewed journal with open access and public review procedure.

An AGI Network website is under construction. 

Many AGI related resources are collected in the AGIRI website. Lukasz Stafiniak maintains a list of AGI resources.

There is a mailing list, a Google Group, and a LinkedIn Group, all dedicated to AGI.

Here only resources dedicated to AGI are listed, though there are many other related works in AI and Cognitive Science literature. Some of them are assembled into the following reading lists:

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