AI as a Whole
Even so, there are some serious attempts to build a "Thinking Machine" (or call it "Artificial General Intelligence (AGI)", "Human-Level Intelligence", "Strong AI"), though the projects are based on quite different theoretical assumptions, and all of them have long ways to go. The following is an incomplete list of ongoing projects, organized according to the categories listed above, and linked to the project websites containing the cited descriptions.
CCortex: Artificial Development is building CCortex, a massive spiking neural network simulation of the human cortex and peripheral systems. Upon completion, CCortex will represent up to 20 billion neurons and 20 trillion connections, achieving a level of complexity that rivals the mammalian brain, and making it the largest, most biologically realistic neural network ever built. The system is up to 10,000 times larger than any previous attempt to replicate primary characteristics of human intelligence.
HTM: Numenta is developing a new type of computer memory system modeled after the human neocortex. The applications of this technology are broad and can be applied to solve problems in computer vision, artificial intelligence, robotics and machine learning. The Numenta technology, called Hierarchical Temporal Memory (HTM), is based on a theory of the neocortex described in Jeff Hawkins' book entitled On Intelligence (with co-author Sandra Blakeslee). Numenta co-founder Dileep George implemented a mathematical formalism of Hawkins' theory, demonstrating it is possible to express this new type of memory system in software. Numenta was formed to develop HTM technology and to promote its use.
CAM-Brain: The long term aim of STARLAB's CAM-Brain Project is to build artificial brains, e.g. a billion neuron artificial brain by the year 2001. 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), i.e. Xilinx's XC6264 chips. CA based neural circuits can be grown and evaluated 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.
HAL: At Ai, we're raising a child machine from infancy to adulthood - thus bringing Turing’s vision to fruition - and creating entirely new approaches to machine learning. Sometimes this involves number-crunching and algorithm design; sometimes it means reading Green Eggs and Ham to a PC. In our research, we take a strong behaviorist approach, meaning that we work from the principle that language is a skill, not simply the output of brain functions, and, therefore, can be learned. Led by Jason Hutchens, a world-renowned chatbot developer and winner of the Loebner Prize in Artificial Intelligence, our scientists create computer programs that are capable of learning about language, but have no preconceived notions about language or about the world.
ACT-R: 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.
Soar: Our intention is for Soar to support all the capabilities required of a general intelligent agent. 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.
Cog: 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.
Cyc: 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, described below. 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. Cyc is not a frame-based system: the Cyc team thinks of the KB instead as a sea of assertions, with each assertion being no more "about" one of the terms involved than another.
Novamente: The Novamente AI Engine (NAIE) is a C++ software system designed for large-scale implementation on a distributed network of Linux machines, and founded on a unique AI design grounded in a systems theory of intelligence. Among the key cognitive mechanisms of the system are a probabilistic reasoning engine based on a novel variant of probabilistic logic called Probabilistic Logic Networks; an evolutionary learning engine that is based on a synthesis of probabilistic modeling and evolutionary programming called MOSES, pioneered in Moshe Looks’ 2006 PhD work at Washington University; and an artificial economics based system for attention allocation and credit assignment.
AIXI: Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. With a formal solution I mean a rigorous mathematically definition, uniquely specifying the solution. I unified both theories and gave strong arguments that the resulting universal AIXI model behaves optimally in any computable environment. I also made some progress towards a computable AI theory.
OSCAR: OSCAR is an architecture for rational agents based upon an evolving philosophical theory of rational cognition. OSCAR is based on a schematic view of rational cognition according to which agents have beliefs representing their environment and an evaluative mechanism that evaluates the world as represented by their beliefs. They then engage in activity designed to make the world more to their liking. This schematic view of rational cognition makes it natural to distinguish between epistemic cognition, which is cognition about what to believe, and practical cognition, which is cognition about what to do. We can think of the latter as including goal selection, plan construction, plan selection, and plan execution.
NARS: NARS (Non-Axiomatic Reasoning System) is a general-purpose reasoning system, coming from my study of Artificial Intelligence (AI) and Cognitive Sciences (CogSci). 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.