Ben Goertzel

Using Dependency Parsing and Probabilistic Inference to Extract Relationships between Genes, Proteins and Malignancies Implicit Among Multiple Biomedical Research Abstracts

We describe BioLiterate, a prototype software system which infers relationships involving relationships between genes, proteins and malignancies from research abstracts, and has initially been tested in the domain of the molecular genetics of oncology. The architecture uses a natural language processing module to extract entities, dependencies and simple semantic relationships from texts, and then feeds these features into a probabilistic reasoning module which combines the semantic relationships extracted by the NLP module to form new semantic relationships. One application of this system is the discovery of relationships that are not contained in any individual abstract but are implicit in the combined knowledge contained in two or more abstracts.

Patterns, Hypergraphs and Embodied General Intelligence

It is proposed that the creation of artificial general intelligence (AGI) at the human level and ultimately beyond is a problem addressable via integrating computer science algorithms and data structures within a cognitive architecture oriented toward experiential learning. A general conceptual framework for AGI is presented, beginning with a philosophy of mind based on the concept of pattern, then moving to a general mathematical and conceptual framework for modeling intelligent systems, self-modifying evolving probabilistic hypergraphs (SMEPH), and finally to an overview of a specific design for AGI, the Novamente AI engine. The problem of teaching an AGI system is discussed, in the context of Novamentes embodiment in the AGI-SIM simulation world. An educational program based loosely on Piagets developmental stages is outlined, followed by more detailed consideration of the learning by Novamente in AGI-SIM of the Piagetan infant-level capability of object permanence.

Support Vector Machines to Weight Voters in a Voting System of Entity Extractors

Support vector machines are used to combine the outputs of multiple entity extractors, thus creating a composite entity extraction system. The composite system has a significantly higher f-measure than any of the component systems. Compared to a standard voting technique for combining the results of multiple entity extractors, the SVM approach produces comparable precision and recall statistics but tends to utilize fewer of the component entity extractors, thus providing superior computational efficiency, which is critical in practical applications. In this paper, we present our experimental results of comparing a standard voting technique with SVM that each aggregate four entity extractors. We also describe our future plans of integrating agent-based technology into our experimental testbed where we examine the evolution of composite techniques as part of the analysis stream. Given that much of the improvement comes from tuning the algorithms to the data stream with a human-in-the-loop, we are considering the merits of employing cognitive agents that are strategically embedded in the workflow for processing data. As we tune the algorithms for better performance on the data streams, we envision agents learning the patterns of data streams and apply the appropriate tuning to ensure optimality.

Accurate SVM Text Classification for Highly Skewed Data Using Threshold Tuning and Query-Expansion-Based Feature Selection

A novel technique is described, wherein Support Vector Machines are used to perform relatively effective text categorization based on small numbers of positive examples (fewer than 10 in some cases). It is assumed that in addition to the positive examples a query describing the positive category is given (in the form of a set of key phrases or a sentence). The technique combines two innovations: a special way of altering the SVM score threshold based on looking at the distribution of scores across the training set; and, a method of feature selection that involves retaining only features that display semantic association to the content words in the query (according to a word-association database produced by statistical analysis of a parsed corpus). Examples are given on a number of test cases drawn from the Reuters and FBIS news archives.

The Coming Evolution

Nearly everyone who has seriously thought about the evolution of technology over the next few hundred years has come to the same conclusion: We live at a crucial point in history — an incredibly exciting and frightening point; a point that is stimulating to the point of excess, intellectually, philosophically, physically and emotionally. A number of really big technologies are brewing. Virtual reality, which lets us create synthetic worlds equal in richness to the physical world, thus making the Buddhist maxim “reality is illusion” a palpable technical fact. Biotechnology, allowing us to modify our bodies in various ways, customizing our genes and jacking our brains, organs and sense organs into computers and other devices. Nanotechnology, allowing us to manipulate molecules directly, creating biological, computational, micromechanical, and other kinds of systems that can barely be imagined today. Artificial intelligence, enabling mind, intelligence and reason to emerge out of computer systems — thinking machines built by humans. And advances in unified field theory in physics will in all likelihood join the party, clarifying the physical foundation of life and mind, and giving the nanotechnologists new tricks no one has even speculated about yet.