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Category: Artificial Intelligence

Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Neuromorphic Computing: Statistical Mechanics & Criticality   Last week, I suggested that we were on the verge of something new, and referenced an article by von Bubnoff: A brain built from atomic switches [that] can learn, together with the follow-on article Brain Built on Switches. The key innovation described in this article was a silver mesh, as shown in the following figure. This mesh is a “network of microscopically thin intersecting silver wires,” grown via a combination of electrochemical and…

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Machine Learning: Multistage Boost Process

Machine Learning: Multistage Boost Process

Three Stages to Orbital Altitude in Machine Learning Several years ago, Regina Dugan (then Director of DARPA) gave a talk in which she showed a clip of epic NASA launch fails. Not just one, but many fails. The theme was that we had to risk failure in order to succeed with innovation. This YouTube vid of rocket launch failures isn’t the exact clip that she showed (the “action” doesn’t kick in for about a minute), but it’s pretty close. For…

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Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text by Matching Terms and Entities to Ontologies and Concepts Text analytics is the means by which computer algorithms can extract meaning and useful insights from raw text sources. This can have enormous impact in realms such as marketing, business intelligence, and political campaigns. However, text analytics is one of the toughest challenges in predictive analytics. The reason why this is so hard? It’s because – when done right – text analytics must effectively connect…

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Novelty Detection in Text Corpora

Novelty Detection in Text Corpora

Detecting Novelty Using Text Analytics Detecting novel events – new words, meaning new events – is one of the most important text analytics tasks, and is an important step towards predictive analytics using text mining. On July 24, 2015, The New York Times (and many other news sources) published an article identifying potential inclusion of classified information in the emails which Hillary Clinton had sent via private email and stored on her private email server. How would we use text…

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Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Graphs Need Specialized Data Storage and Computational Methods {A Working Blogpost – Notes for research & study} Processing large-scale graph data: A guide to current technology, by Sherif Sakr (ssakr@cse.unsw.edu.au), IBM Developer Works (10 June 2013). Note: Dr. Sherif Sakr is a senior research scientist in the Software Systems Group at National ICT Australia (NICTA), Sydney, Australia. He is also a conjoint senior lecturer in the School of Computer Science and Engineering at University of New South Wales. He…

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Survey of Text Mining II: Cluster-Preserving Dimension Reduction Methods (Chapter 1)

Survey of Text Mining II: Cluster-Preserving Dimension Reduction Methods (Chapter 1)

Some time ago, I promised a colleague a review of an excellent book’ Survey of Text Mining II: Clustering, Classification, and Retrieval, edited by Michael W. Berry and Malu Castellanos. Overall, this book would serve well as the basis for a one-semester graduate course in specialized methods for (textual) data analytics. It presupposes an expert’s (or at least a solid journeyman’s) understanding of basic algorithms along with the issues of textual data mining / analytics. Each chapter presents a new…

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