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The article focuses on the reconsideration of paper-based knowledge management (KM), acknowledging past successes and advocating for a more selective approach to data accumulation. Topics discussed include the limitations of digital KM, the need for quality over quantity, and the importance of human oversight in assessing data integrity and relevance.

Anil Ananthaswamy's book, "Why Machines Learn: The Elegant Math Behind Modern AI," attempts to explain how artificial intelligence (AI) works by scanning data for patterns and making predictions. However, the book's detailed breakdown of probability, principal component analysis, and eigenvectors may be difficult for readers without an advanced math degree to understand. While there are some interesting examples, such as how statisticians determined the authorship of the Federalist Papers, the book is primarily filled with complex equations and proofs. General readers may find it challenging to follow. [Extracted from the article]

In this paper, an effective identification method of wavelength variable selection to rapidly discriminate the grape seed oil adulteration by near-infrared (NIR) spectroscopy is investigated. The extreme learning machine (ELM) is employed to build a stable and accurate model, and a firefly algorithm combined with a successive projections algorithm (FA-SPA) is developed to eliminate redundant wavelengths (The model used throughout is called FA-SPA-ELM). The comparison among different models--the partial least squares discriminant analysis (PLS-DA) model, the support vector machine (SVM) model, the least squares support vector machine (LS-SVM), and the FA-SPA-ELM model--demonstrates that the wavelength number of the FA-SPA model can be effectively reduced with a wavelength variable of 17, and the model of FA-SPA-ELM presents the excellent predictive capability. The experimental results show that the proposed novel method could be used to identify adulterated grape seed oil quickly, effectively, and nondestructively. [ABSTRACT FROM AUTHOR]

Video service providers are dealing with hyper-competition in the media industry. To remain competitive, they must deliver not only excellent service quality, but also be agile and fast in terms of service development. Service failures need to be eliminated quickly and prevented before they happen. There are two challenges here, the first one being that raw data is siloed and needs to be transformed into insights readily available for different stakeholders. The second problem is that video ecosystems are becoming increasingly complex; how to manage that complexity with limited resources? The answers lie in a shift in focus to a holistic, silo meshing, data strategy and the power of automation, machine learning and artificial intelligence. In this paper, we share the steps that Divitel is taking towards the goal of building fully automated video services and explain how we plan to use Artificial Intelligence in the execution. [ABSTRACT FROM AUTHOR]

The article discusses a webinar titled "Conquering ECM Challenges in the Modern Enterprise" hosted by KMWorld and sponsored by Access and Hyland. Topics include challenges posed by legacy paper and digital records, the need to modernize content strategy, and the solutions presented by Access and Hyland. The webinar featured several notable speakers including Matt Hillery, chief technology officer at Access; and Sean Baird, director of product marketing at Hyland.

The article discusses whether scientists have developed a full-scale production memristor, a chip which is supposed to change its resistance according to its past electrical history and to work like a human brain's neurons. It focuses on the U.S. Defense Advanced Research Projects Agency's efforts to develop prototypes of memristor. It also refers to white papers from startup Knowm, founded by Alex Nugent, which claims to develop memristors with bidirectional incremental learning capability.