This is the outline of a talk given at the "Mind and Machine" Seminar at Shanghai in June 2007.
Anniversaries are good time to review the big picture of the field. In the following collections and events, many people raised the topic of general-purpose and human-level intelligence:
"AI" and "AGI" were originally the same, but currently different. Other similar notions include "strong AI", "human-level intelligence", "real AI", and "thinking machine".
AGI research has a science (theory) aspect and an engineering (technique) aspect. A complete AGI work normally includes
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.
Each project is linked to the project website and selected publications, where the following descriptions are extracted. The focus of the descriptions is on the research goal (the 1st question) and technical path (the 3rd question).
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.
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.
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].
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.
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.
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.
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.
The principal virtue of OSCAR’s epistemic reasoning is not that it is an efficient deductive reasoner, but that it is capable of performing defeasible reasoning. Deductive reasoning guarantees the truth of the conclusion given the truth of the premises. Defeasible reasoning makes it reasonable to accept the conclusion, but does not provide an irrevocable guarantee of its truth. Conclusions supported defeasibly might have to be withdrawn later in the face of new information.
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.
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.
Avoiding flighty anthropomorphism, you can consider Cog to be a set of sensors and actuators which tries to approximate the sensory and motor dynamics of a human body. Except for legs and a flexible spine, the major degrees of motor freedom in the trunk, head, and arms are all there. Sight exists, in the form of video cameras. Hearing and touch are on the drawing board. Proprioception in the form of joint position and torque is already in place; a vestibular system is on the way. Hands are being built as you read this, and a system for vocalization is also in the works. Cog is a single hardware platform which seeks to bring together each of the many subfields of Artificial Intelligence into one unified, coherent, functional whole.
Neural networks are based on cellular automata, and are evolved using a Genetic Algorithm (GA) at electronic speeds using the latest in FPGAs (field programmable gate arrays)... . CA based neural circuits can be grown and evaluate totally in hardware in microseconds, making possible a complete run of a GA (i.e. tens of thousands of circuit growths and evaluations (fitness measurements)) in less than a second.
Up to 64,000 evolved neural net modules can be assembled into humanly designed artificial brain architectures, and each CA cell in the whole brain of millions of cells (stored in RAM) can be updated (using the CBM) thousands of times a second, which is easily fast enough for real time control of robots.
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.
| goal \ path | hybrid | integrated | unified |
| principle | AIXI, NARS, OSCAR | ||
| function | LIDA, Novamente, Polyscheme | Cog, SNePS, Soar | |
| capability | Cyc | ||
| behavior | ACT-R | ||
| structure | CAM-Brain, 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.