Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Minimally-Supervised Morphological Segmentation using Adaptor Grammars

This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.12 page(s

Minimal supervision for language learning: bootstrapping global patterns from local knowledge

A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Each step depends on prior lexical and syntactic knowledge. Where do children begin in solving this problem when learning their first languages? To experiment with different representations that children may use to begin understanding language, we have built a computational model for this early point in language acquisition. This system, BabySRL, learns from transcriptions of natural child-directed speech and makes use of psycholinguistically plausible background knowledge and realistically noisy semantic feedback to begin to classify sentences at the level of ``who does what to whom.'' Starting with simple, psycholinguistically-motivated representations of sentence structure, the BabySRL is able to learn from full semantic feedback, as well as a supervision signal derived from partial semantic background knowledge. In addition we combine the BabySRL with an unsupervised Hidden Markov Model part-of-speech tagger, linking clusters with syntactic categories using background noun knowledge so that they can be used to parse input for the SRL system. The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argument-identification stages. Finally, we enable the BabySRL to improve both an intermediate syntactic representation and its final semantic role classification. Using this system we show that it is possible for a simple learner in a plausible (noisy) setup to begin comprehending simple semantics when initialized with a small amount of concrete noun knowledge and some simple syntax-semantics mapping biases, before acquiring any specific verb knowledge

Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

Islands in the grammar? Standards of evidence

When considering how a complex system operates, the observable behavior depends upon both architectural properties of the system and the principles governing its operation. As a simple example, the behavior of computer chess programs depends upon both the processing speed and resources of the computer and the programmed rules that determine how the computer selects its next move. Despite having very similar search techniques, a computer from the 1990s might make a move that its 1970s forerunner would overlook simply because it had more raw computational power. From the naïve observer’s perspective, however, it is not superficially evident if a particular move is dispreferred or overlooked because of computational limitations or the search strategy and decision algorithm. In the case of computers, evidence for the source of any particular behavior can ultimately be found by inspecting the code and tracking the decision process of the computer. But with the human mind, such options are not yet available. The preference for certain behaviors and the dispreference for others may theoretically follow from cognitive limitations or from task-related principles that preclude certain kinds of cognitive operations, or from some combination of the two. This uncertainty gives rise to the fundamental problem of finding evidence for one explanation over the other. Such a problem arises in the analysis of syntactic island effects – the focu

A dynamic network analysis of emergent grammar

For languages to survive as complex cultural systems, they need to be learnable. According to traditional approaches, learning is made possible by constraining the degrees of freedom in advance of experience and by the construction of complex structure during development. This article explores a third contributor to complexity: namely, the extent to which syntactic structure can be an emergent property of how simpler entities – words – interact with one another. The authors found that when naturalistic child directed speech was instantiated in a dynamic network, communities formed around words that were more densely connected with other words than they were with the rest of the network. This process is designed to mirror what we know about distributional patterns in natural language: namely, the network communities represented the syntactic hubs of semi-formulaic slot-and-frame patterns, characteristic of early speech. The network itself was blind to grammatical information and its organization reflected (a) the frequency of using a word and (b) the probabilities of transitioning from one word to another. The authors show that grammatical patterns in the input disassociate by community structure in the emergent network. These communities provide coherent hubs which could be a reliable source of syntactic information for the learner. These initial findings are presented here as proof-of-concept in the hope that other researchers will explore the possibilities and limitations of this approach on a larger scale and with more languages. The implications of a dynamic network approach are discussed for the learnability burden and the development of an adult-like grammar