computational


A Roadmap to AIOps -- Part 2 - DZone AI

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This article is a continuation of Part 1. You can find the first part here. It is likely that when you begin your AIOps journey, you will already have certain analytics in place. I do not mean the analytics that are embedded in your IT tools. I mean offline, mostly manual analytics, that you do regularly, irregularly, or periodically to identify areas for process improvement, reduce costs, improve performance, etc.


IAGON – Global Supercomputing meets AI, BigData and Blockchain Technology Coinfeeds

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IAGON is the first decentralized Artificial Intelligence Blockchain-enabled Supercomputing Grid Technology for harnessing the storage capacities and processing power of multiple smart devices over a decentralized Blockchain/Tangle grid. The platform creates an AI decentralized architecture that manages and optimizes spare/idle distributed computing power and storage around the world, creating a truly decentralized Global Smart Computing Network solution for web-centric and decentralized applications. Utilizing a new consensus mechanism known as Proof-of Utilitarian (PoUW), it promotes decentralized cloud services, where multiple grid miners are rewarded by conducting decentralized parallel computing tasks and storing user's files. IAGON represents a new wave of High Performance Computing powered by Artificial Intelligence, Blockchain, BigData and wrapped in a sophisticated Encryption/Decryption philosophy catering for both individual and corporate clients. About IAGON IAGON is Global Supercomputing powered by Artificial Intelligence, BigData and Blockchain Technology that harnesses the storage capacities and processing power of multiple smart devices over a decentralized network Blockchain/Tangle grid. It's design philosophy is simple in that enables storing of BigData and repositories, as well as smaller scales of files, and carries out complex computational processes through a smart computation grid such as those needed for Artificial Intelligence and Machine Learning operations. IAGON operates a fully secure and encrypted platform that integrates Multiple Blockchain Support/Tangle Technologies, AI-Based Computational Processing, Smart Computational Grid and Secure Lake Technologies in an intuitive and user-friendly environment. Under IAGON's platform you can imagine a world where anyone can profit by joining a massive processing grid. IAGON will provide a fully automated platform for carrying out the storage and processing tasks of users on the basis of unutilized storage and processing capacities that are contributed by participating nodes or "miners".


Augmenting Pathology Labs With Big Data And Machine Learning

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Pathology laboratories are big data environments. However, these big data are often hidden behind expert humans who manually and with great care visually parse large complex and detailed datasets to provide critical diagnoses. Humans it turns out, are amazingly detailed and accurate large data visualization, segmentation and interpretation devices. Experts are able to zoom in and identify the potentially five or six tumor glands from a large area of stained tissue that comprise the average cancer positive needle biopsy. However, pathology is still an extremely manual and detailed process requiring great skill and accuracy to avoid any potential misdiagnosis.


This wild, AI-generated film is the next step in "whole-movie puppetry"

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Two years ago, Ars Technica hosted the online premiere of a weird short film called Sunspring, which was mostly remarkable because its entire script was created by an AI. The film's human cast laughed at odd, computer-generated dialogue and stage direction before performing the results in particularly earnest fashion. That film's production duo, Director Oscar Sharp and AI researcher Ross Goodwin, have returned with another AI-driven experiment that, on its face, looks decidedly worse. Blurry faces, computer-generated dialogue, and awkward scene changes fill out this year's Zone Out, a film created as an entry in the Sci-Fi-London 48-Hour Challenge--meaning, just like last time, it had to be produced in 48 hours and adhere to certain specific prompts. That 48-hour limit is worth minding, because Sharp and Goodwin went one bigger this time: they let their AI system, which they call Benjamin, handle the film's entire production pipeline.


Understanding Mainstream Chips Used in Artificial Intelligence - DZone AI

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On January 2018, the International Consumer Electronics Show (CES) kicked off in Las Vegas, Nevada, featuring more than 4,000 exhibitors. CES is the world's largest consumer electronics show and the "SuperBowl" for global consumer electronics and consumer technology. Industry giants such as Qualcomm, NVIDIA, Intel, LG, IBM, Baidu, took this opportunity to publicly reveal their latest and greatest AI chips, products, and strategies. AI related technologies and products were one of the hot topics at this year's show, with embedded AI products receiving the most widespread attention. The current advanced AI development strategy is deep learning with a learning process divided into two parts: training and inference.


SDSC Researchers Use Machine Learning to More Accurately Model Water

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Water – H2O – is a simple but fascinating (and useful) compound. San Diego Supercomputing Center researchers used machine learning techniques to develop models for simulations of water with "unprecedented accuracy." This blend of machine learning with traditional simulation is happening quickly and proving potent in scientific research. "Although computer simulations have become a powerful tool for the modeling of water and for molecular sciences in general, they are still limited by a tradeoff between the accuracy of the molecular models and the associated computational cost," said Francesco Paesani, professor of chemistry and biochemistry at UCSD, quoted in an account of the work posted today on the SDSC site. "Now that we've proved this concept with a model of water using machine learning techniques, we are currently extending this novel approach to generic molecules," he added, "meaning that scientists will be able to predict the properties of molecules and materials with unprecedented accuracy."


Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres

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Pablo Marquez-Neila, Chloe Fisher, Raphael Sznitman, Kevin Heng (Submitted on 11 Jun 2018) The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model. Known as atmospheric retrieval, it is a technique that originates from the Earth and planetary sciences. Such methods are very time-consuming and by necessity there is a compromise between physical and chemical realism versus computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods.


Deep Convolutional Neural Networks as Models of the Visual System: Q&A

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Yes. First, artificial neural networks as whole were inspired--as their name suggests--by the emerging biology of neurons being developed in the mid-20th century. Artificial neurons were designed to mimic the basic characteristics of how neurons take in and transform information. Second, the main features and computations done by convolutional networks were directly inspired by some of the early findings about the visual system. In 1962 Hubel and Wiesel discovered that neurons in primary visual cortex respond to specific, simple features in the visual environment (particularly, oriented edges). Furthermore, they noticed two different kinds of cells: simple cells--which responded most strongly to their preferred orientation only at a very particular spatial location--and complex cells--which had more spatial invariance in their response.


Artificial intelligence and the stability of markets

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Artificial intelligence (AI) is useful for optimally controlling an existing system, one with clearly understood risks. Given enough observations and a strong signal, it can identify deep dynamic structures much more robustly than any human can and is far superior in areas that require the statistical evaluation of large quantities of data. It can do so without human intervention. We can leave an AI machine in the day-to-day charge of such a system, automatically self-correcting and learning from mistakes and meeting the objectives of its human masters. This means that risk management and micro-prudential supervision are well suited for AI.


The US Again Has World's Most Powerful Supercomputer

WIRED

Plenty of people around the world got new gadgets Friday, but one in Eastern Tennessee stands out. Summit, a new supercomputer unveiled at Oak Ridge National Lab is, unofficially for now, the most powerful calculating machine on the planet. It was designed in part to scale up the artificial intelligence techniques that power some of the recent tricks in your smartphone. America hasn't possessed the world's most powerful supercomputer since June 2013, when a Chinese machine first claimed the title. Summit is expected to end that run when the official ranking of supercomputers, from an organization called Top500, is updated later this month.