bcruse

Discovering the Hidden Structure of Knowledge --- Though we don’t how the human brain is able to transcend from the data processing layer (where the brain too is just processing low level data coming in from the senses) to the realm of knowledge we can, through introspection, examine the structure of the end product of our thought processes, that is knowledge itself. What we find is a collection of ideas that are connected through various relationships that are themselves ideas. While many of these ideas represent specific objects in the real world, that tree, this car and so forth, many are abstractions, trees, cars. Each idea is connected to many others, some of which define its properties and some its relationship to others ideas. The power of abstract ideas as opposed to ideas representing particular things is that they are reusable. They can become components of new ideas. Complex concepts are built out of fundamentals. As the material world is composed of atoms, our knowledge of the world is composed of ideas. The English language is has over a million words each referring to an idea. Without some notion that only a small portion of these ideas are fundamental (atoms) and can only be combined in certain ways, the task of putting knowledge in machines is overwhelming. Democritus is known as the "laughing philosopher." There is speculation that he was laughing at his critics who clearly had not thought things out as well as [...]

Artificial General Intelligence (AGI) AGI or Artificial General Intelligence, the quest for software that does have genuine comprehension that would be recognizable as such by anyone because (in the spirit of the Turing Test) you can hold a general unscripted conversation with it. Today, outside of our work, AGI research efforts fall into two categories. Whole brain emulation The approach is that you first create a neural network with the size and complexity of the human brain and then program it to recapitulate, in some form, human cognitive processes that will eventually result in the production of world knowledge. The assumption here is that intelligence is kind of an emergent property of a vast neural network. We find this assumption extremely doubtful, and there are numerous other problems associated with this approach even should it produce something. Ray Kurzweil, who popularized the idea of an AI “singularity” and is currently VP of Engineering at Google is pursuing this approach (no doubt with lots of money – he will need it). The project at Google and numerous other whole brain research projects at DARPA, IBM and other places are described are artificialbrains.com Cognitive algorithms This approach seeks to discover one or a small number of immensely powerful algorithms that endow the human brain with intelligence and then reverse engineer them such that the program will be able to process raw inputs and turn them into real knowledge as humans can do. We call this the magic algorithm approach. Significant [...]

How do we know what we know? If we want to endow machines with knowledge we had better understand what it is. Epistemology, a term first used in 1854, is the branch of philosophy concerned with the theory of knowledge. It is not much studied in the schools these days and certainly not in computer science curriculums. Traditionally, epistemologists have focused on such concepts as truth, belief and justification as applied to any given assertions. From that perspective it is not much help since previous attempts to put knowledge into machines failed because they treated knowledge as just that, a vast collection of assertions (facts or opinions). That is not knowledge -that is data. We need to find an organizing structure for all these facts that will transform them into a road map of the world.  Since the dawn of civilization there have successive descriptions of the our world or reality. The ancients created, as beautify articulated by the theorems of the Alexandrian mathematician Ptolemy, an elegant geometric model of the universe with the earth at the center and everything else travelling around it on perfect circles, at a constant velocity. They had to put circles traveling on other circles to make the model match the actual celestial observations - but it worked![1] Claudius Ptolemy AD 100 - 170 The Ptolemaic System The Sextant Later this model was, (what should one say, refuted, replaced, superseded?) by [...]