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PubMed2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Artificial Intelligence↗
PubMed2004

A machine learning method for extracting symbolic knowledge from recurrent neural networks.

Neural networks do not readily provide an explanation of the knowledge stored in their weights as part of their information processing. Until recently, neural networks were considered to be black boxes, with the knowledge stored in their weights not readily accessible. Since then, research has resulted in a number of algorithms for extracting knowledge in symbolic form from trained neural networks. This article addresses the extraction of knowledge in symbolic form from recurrent neural networks trained to behave like deterministic finite-state automata (DFAs). To date, methods used to extract knowledge from such networks have relied on the hypothesis that networks' states tend to cluster and that clusters of network states correspond to DFA states. The computational complexity of such a cluster analysis has led to heuristics that either limit the number of clusters that may form during training or limit the exploration of the space of hidden recurrent state neurons. These limitations, while necessary, may lead to decreased fidelity, in which the extracted knowledge may not model the true behavior of a trained network, perhaps not even for the training set. The method proposed here uses a polynomial time, symbolic learning algorithm to infer DFAs solely from the observation of a trained network's input-output behavior. Thus, this method has the potential to increase the fidelity of the extracted knowledge.

Algorithms↗
PubMedDate not supplied

Looking ahead: implications of the present.

On its seventy-fifth anniversary, HBR asked five of the business world's most insightful thinkers to comment on the challenges taking shape for executives as they move into the next century. In "The Future That Has Already Happened," Peter Drucker examines the effects of the increasing underpopulation of the world's developed countries. With growing imbalances in labor resources worldwide, he writes, executives in the developed countries will need to improve the productivity of knowledge and of knowledge workers to maintain a competitive advantage. Esther Dyson's article "Mirror, Mirror on the Wall" reveals the mind shift executives will need to make in a networked world, where companies will be known for what they do rather than for what they say. Executives will have to respond openly and intelligently to feedback about their organizations. The old language of property and ownership no longer serves executives, writes Charles Handy in "The Citizen Corporation." The corporation should be thought of no longer as property but as a community, where members are regarded as citizens. Technology has given executives more information than today's machines can help them understand, explains Paul Saffo in "Are You Machine Wise?" Machine-wise executives will know when to turn their computers off and take their own counsel, he writes. Peter Senge's article "Communities of Leaders and Learners" urges executives to reject the myth of leaders as isolated heroes and instead to build a community of leaders. Sustained institutional learning, he writes, requires organizations to reintegrate their typically fragmented learning processes.

Administrative Personnel↗
PubMed2006

On the design of robotic hands for brain-machine interface.

Brain-machine interface (BMI) is the latest solution to a lack of control for paralyzed or prosthetic limbs. In this paper the authors focus on the design of anatomical robotic hands that use BMI as a critical intervention in restorative neurosurgery and they justify the requirement for lower-level neuromusculoskeletal details (relating to biomechanics, muscles, peripheral nerves, and some aspects of the spinal cord) in both mechanical and control systems. A person uses his or her hands for intimate contact and dexterous interactions with objects that require the user to control not only the finger endpoint locations but also the forces and the stiffness of the fingers. To recreate all of these human properties in a robotic hand, the most direct and perhaps the optimal approach is to duplicate the anatomical musculoskeletal structure. When a prosthetic hand is anatomically correct, the input to the device can come from the same neural signals that used to arrive at the muscles in the original hand. The more similar the mechanical structure of a prosthetic hand is to a human hand, the less learning time is required for the user to recreate dexterous behavior. In addition, removing some of the nonlinearity from the relationship between the cortical signals and the finger movements into the peripheral controls and hardware vastly simplifies the needed BMI algorithms. (Nonlinearity refers to a system of equations in which effects are not proportional to their causes. Such a system could be difficult or impossible to model.) Finally, if a prosthetic hand can be built so that it is anatomically correct, subcomponents could be integrated back into remaining portions of the user's hand at any transitional locations. In the near future, anatomically correct prosthetic hands could be used in restorative neurosurgery to satisfy the user's needs for both aesthetics and ease of control while also providing the highest possible degree of dexterity.

Brain↗
PubMed2006

Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model.

Minimally invasive surgery (MIS) involves a multidimensional series of tasks requiring a synthesis between visual information and the kinematics and dynamics of the surgical tools. Analysis of these sources of information is a key step in defining objective criteria for characterizing surgical performance. The Blue DRAGON is a new system for acquiring the kinematics and the dynamics of two endoscopic tools synchronized with the endoscopic view of the surgical scene. Modeling the process of MIS using a finite state model [Markov model (MM)] reveals the internal structure of the surgical task and is utilized as one of the key steps in objectively assessing surgical performance. The experimental protocol includes tying an intracorporeal knot in a MIS setup performed on an animal model (pig) by 30 surgeons at different levels of training including expert surgeons. An objective learning curve was defined based on measuring quantitative statistical distance (similarity) between MM of experts and MM of residents at different levels of training. The objective learning curve was similar to that of the subjective performance analysis. The MM proved to be a powerful and compact mathematical model for decomposing a complex task such as laparoscopic suturing. Systems like surgical robots or virtual reality simulators in which the kinematics and the dynamics of the surgical tool are inherently measured may benefit from incorporation of the proposed methodology.

Computer Simulation↗
PubMed2007

Second-order cone programming formulations for robust multiclass classification.

Multiclass classification is an important and ongoing research subject in machine learning. Current support vector methods for multiclass classification implicitly assume that the parameters in the optimization problems are known exactly. However, in practice, the parameters have perturbations since they are estimated from the training data, which are usually subject to measurement noise. In this article, we propose linear and nonlinear robust formulations for multiclass classification based on the M-SVM method. The preliminary numerical experiments confirm the robustness of the proposed method.

Artificial Intelligence↗
PubMed2002

Support vector machines for predicting the specificity of GalNAc-transferase.

Support Vector Machines (SVMs) which is one kind of learning machines, was applied to predict the specificity of GalNAc-transferase. The examination for the self-consistency and the jackknife test of the SVMs method were tested for the training dataset (305 oligopeptides), the correct rate of self-consistency and jackknife test reaches 100% and 84.9%, respectively. Furthermore, the prediction of the independent testing dataset (30 oligopeptides) was tested, the rate reaches 76.67%.

Algorithms↗
PubMed2006

SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures.

Reversible addition of NO to Cys-sulfur in proteins, a modification termed S-nitrosylation, has emerged as a ubiquitous signaling mechanism for regulating diverse cellular processes. A key first-step toward elucidating the mechanism by which S-nitrosylation modulates a protein's function is specification of the targeted Cys (SNO-Cys) residue. To date, S-nitrosylation site specification has been laboriously tackled on a protein-by-protein basis. Here we describe a high-throughput proteomic approach that enables simultaneous identification of SNO-Cys sites and their cognate proteins in complex biological mixtures. The approach, termed SNOSID (SNO Site Identification), is a modification of the biotin-swap technique [Jaffrey, S. R., Erdjument-Bromage, H., Ferris, C. D., Tempst, P. & Snyder, S. H. (2001) Nat. Cell. Biol. 3, 193-197], comprising biotinylation of protein SNO-Cys residues, trypsinolysis, affinity purification of biotinylated-peptides, and amino acid sequencing by liquid chromatography tandem MS. With this approach, 68 SNO-Cys sites were specified on 56 distinct proteins in S-nitrosoglutathione-treated (2-10 microM) rat cerebellum lysates. In addition to enumerating these S-nitrosylation sites, the method revealed endogenous SNO-Cys modification sites on cerebellum proteins, including alpha-tubulin, beta-tubulin, GAPDH, and dihydropyrimidinase-related protein-2. Whereas these endogenous SNO proteins were previously recognized, we extend prior knowledge by specifying the SNO-Cys modification sites. Considering all 68 SNO-Cys sites identified, a machine learning approach failed to reveal a linear Cys-flanking motif that predicts stable transnitrosation by S-nitrosoglutathione under test conditions, suggesting that undefined 3D structural features determine S-nitrosylation specificity. SNOSID provides the first effective tool for unbiased elucidation of the SNO proteome, identifying Cys residues that undergo reversible S-nitrosylation.

Animals↗
PubMed2006

Molecular mechanisms of aquaporin biogenesis by the endoplasmic reticulum Sec61 translocon.

The past decade has witnessed remarkable advances in our understanding of aquaporin (AQP) structure and function. Much, however, remains to be learned regarding how these unique and vitally important molecules are generated in living cells. A major obstacle in this respect is that AQP biogenesis takes place in a highly specialized and relatively inaccessible environment formed by the ribosome, the Sec61 translocon and the ER membrane. This review will contrast the folding pathways of two AQP family members, AQP1 and AQP4, and attempt to explain how six TM helices can be oriented across and integrated into the ER membrane in the context of current (and somewhat conflicting) translocon models. These studies indicate that AQP biogenesis is intimately linked to translocon function and that the ribosome and translocon form a highly dynamic molecular machine that both interprets and is controlled by specific information encoded within the nascent AQP polypeptide. AQP biogenesis thus has wide ranging implications for mechanisms of translocon function and general membrane protein folding pathways.

Animals↗
PubMed2004

Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

Algorithms↗
PubMed2000

Event discovery in medical time-series data.

Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting Otrue alarmO situations in the ICU, where currently as many as 86% of bedside monitor alarms are false.

Artificial Intelligence↗
PubMed1993

[Anesthesia simulators and training devices].

Simulators and training devices are used extensively by educators in 'high-tech' occupations, especially those requiring an understanding of complex systems and co-ordinated psychomotor skills. Because of advances in computer technology, anaesthetised patients can now be realistically simulated. This paper describes several training devices and a simulator currently being employed in the training of anaesthesia personnel at the University of Florida. This Gainesville Anesthesia Simulator (GAS) comprises a patient mannequin, anaesthesia gas machine, and a full set of normally operating monitoring instruments. The patient can spontaneously breathe, has audible heart and breath sounds, and palpable pulses. The mannequin contains a sophisticated lung model that consumes and eliminates gas according to physiological principles. Interconnected computers controlling the physical signs of the mannequin enable the presentation of a multitude of clinical signs. In addition, the anaesthesia machine, which is functionally intact, has hidden fault activators to challenge the user to correct equipment malfunctions. Concealed sensors monitor the users' actions and responses. A robust data acquisition and control system and a user-friendly scripting language for programming simulation scenarios are key features of GAS and make this system applicable for the training of both the beginning resident and the experienced practitioner. GAS enhances clinical education in anaesthesia by providing a non-threatening environment that fosters learning by doing. Exercises with the simulator are supported by sessions on a number of training devices. These present theoretical and practical interactive courses on the anaesthesia machine and on monitors. An extensive system, for example, introduces the student to the physics and clinical application of transoesophageal echocardiography.(ABSTRACT TRUNCATED AT 250 WORDS)

Anesthesiology↗
PubMed1999

Comparison of genetic algorithms and other classification methods in the diagnosis of female urinary incontinence.

Galactica, a newly developed machine-learning system that utilizes a genetic algorithm for learning, was compared with discriminant analysis, logistic regression, k-means cluster analysis, a C4.5 decision-tree generator and a random bit climber hill-climbing algorithm. The methods were evaluated in the diagnosis of female urinary incontinence in terms of prediction accuracy of classifiers, on the basis of patient data. The best methods were discriminant analysis, logistic regression, C4.5 and Galactica. Practically no statistically significant differences existed between the prediction accuracy of these classification methods. We consider that machine-learning systems C4.5 and Galactica are preferable for automatic construction of medical decision aids, because they can cope with missing data values directly and can present a classifier in a comprehensible form. Galactica performed nearly as well as C4.5. The results are in agreement with the results of earlier research, indicating that genetic algorithms are a competitive method for constructing classifiers from medical data.

Algorithms↗
PubMed1994

COMPETE: a model for vocational evaluation, training, employment, and community for integration for persons with cognitive impairments.

This paper describes a job skills training program for young adults with cognitive impairments called COMPETE, an acronym for Computer Preparation: Evaluation, Training, and Employment. Participants in COMPETE trained in use of information-age machines, (e.g., computers, fax machines). The 3-year demonstration project resulted in successful placement of 17 of 27 persons who were unemployed before entering the program.

Activities of Daily Living↗
PubMed2001

SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb.yale.edu/nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123 from Saccharomyces cerevisiae, 93 from Caenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features. We are able to extract a number of key rules from our trees, in particular that soluble proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.

Animals↗
PubMed2004

Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems.

This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the sequences of mimicking FSMs are encoded utilizing the transient regions rather than the invariant sets of the evolved dynamical systems of the RNNs.

Computer Simulation↗
PubMed2000

Off-road machine controls: investigating the risk of carpal tunnel syndrome.

Occupationally induced hand and wrist repetitive strain injuries (RSI) such as carpal tunnel syndrome (CTS) are a growing problem in North America. The purpose of this investigation was to apply a modification of the wrist flexion/ extension models of Armstrong and Chaffin (1978, 1979) to determine if joystick controller use in off-road machines could contribute to the development of CTS. A construction equipment cab in the laboratory was instrumented to allow force, displacement and angle measurements from 10 operators while they completed an approximately 30-min joystick motion protocol. The investigation revealed that both the external fingertip and predicted internal wrist forces resulting from the use of these joysticks were very low, indicating that the CTS risk associated with this factor was slight. However, the results also indicated that, particularly for the 'forward' and 'left' right side motions and for all left side motions, force was exerted by other portions of the fingers and hand, thereby under-predicting the tendon tension and internal wrist forces. Wrist angles observed were highest for motions that moved the joysticks to the sides rather than front to back. Thus, the 'right' and 'left' motions for both hands posed a higher risk for CTS development. When the right hand moved into the 'right' position and the left hand moved into the 'left' position, the wrist went into extension in both cases. Results indicate that neither learning nor fatigue affected the results.

Analysis of Variance↗
PubMed2003

Supervised classification for gene network reconstruction.

One of the central problems of functional genomics is revealing gene expression networks - the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers--functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.

Genes↗