meta


META: A Unifying Framework for the Management and Analysis of Text Data

VideoLectures.NET

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people manage and analyze vast amount of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans for humans. First, since text data are generated by people, they are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. Second, since text is written for consumption by humans, humans play a critical role in any text data application system, and a text management and analysis system must involve them in the loop of text analysis.


Storage will continue to play a role in the advancement of AI: Pure Storage

ZDNet

Storage is an important component underpinning artificial intelligence (AI) and other emerging technologies with similar infrastructure demands, according to Robert Lee, VP and chief architect at Pure Storage, and therefore needs to be included in discussions about such technologies. Lee told ZDNet that significant advancements in technology -- particularly around parallelisation, compute, and networking -- enable new algorithms to apply more compute power against data. "Historically, the limit to how much data has been able to be processed, the limit to how much insight we've been able to garner from data has been bottlenecked by storage's ability to keep the compute fed," said Lee, who previously worked at Oracle before joining Pure Storage in 2013. "Somewhere around the early 2000s, the hardware part of compute, CPUs started getting more parallel. It started doing multi-socket architectures, hyper threading multi-core.


AI Is Changing Our Brains Co.Design

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We are in a similar pre-conscious state now, but the voice we hear is not the other side of our brains. It's our digital self–a version of us that is quickly becoming inseparable from our physical self. I call this comingled digital and analog self our "Meta Me." The more the Meta Me uses digital tools, the more conscious it will become–a development that will have tremendous social, ethical, and legal implications. Some are already coming to light.


The Next AI Milestone: Bridging the Semantic Gap – Intuition Machine – Medium

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Contextual Adaptation -- Where systems construct contextual explanatory models for classes of real world phenomena. I write about these two in previous articles (see: "The Only Way to Make Deep Learning Interpretable is to have it Explain Itself" and "The Meta Model and Meta Meta Model of Deep Learning" DARPA's presentation nails it, by highlighting what's going on in current state-of-the-art research. Deep Learning systems have flaws analogous to our own intuitions having flaws. Just to recap, here's the roadmap that I have ( explained here): It's a Deep Learning roadmap and does not cover developments in other AI fields.


New tools aim to automate the hunt for the latest research

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With so much going on around the world, it stands to reason that a computer could be trained to do a similar task, and that's certainly the aim of Semantic Scholar, a new tool launched by The Allen Institute for Artificial Intelligence. The tool offers users a means of hunting for papers in specific fields, and then filter your search by date, publication and so on. The developers believe that Semantic Scholar stands above the likes of Google Scholar due to the smarter way in which it hunts down relevant papers. "Essentially, it allows you to track at the concept level, or the technology level, rather than the article level," the team say.


Pure Storage Announces Vision for Self-Driving Storage; Powered by Pure1 META AI Platform

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Pure Storage (NYSE: PSTG), the market's leading independent all-flash data platform vendor for the cloud era, today announced Pure1 META, it's Artificial Intelligence (AI) platform for delivering on the vision of self-driving storage. This new capability will allow customers to answer questions about new workload deployment, interaction, performance and capacity growth, and workload optimization, helping reduce risk, increase consolidation, and provide better visibility to plan for upgrades or expansions. About Pure Storage Pure Storage (NYSE:PSTG) helps companies push the boundaries of what's possible. Customers who purchase Pure Storage's product offerings should make their purchase decisions based upon products, features and functions that are currently available.


Pure Storage outlines AI engine, bevy of software updates, 75-blade all-flash system ZDNet

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Pure Storage announced a 75-blade all-flash system that operates as one unit as well as an artificial intelligence engine called Meta that aims to make storage arrays more autonomous. At Pure Storage's Accelerate customer powwow, the company outlined a bevy of software updates and features for big data, analytics and artificial intelligence workloads as well as multi-cloud management tools. Kixmoeller also noted that it's interesting that machine learning is needed to predict workloads primarily for artificial intelligence. Pure's artificial intelligence engine learns from the entire customer base to predict how workloads will turn out.


New tools aim to automate the hunt for the latest research

#artificialintelligence

With so much going on around the world, it stands to reason that a computer could be trained to do a similar task, and that's certainly the aim of Semantic Scholar, a new tool launched by The Allen Institute for Artificial Intelligence. The tool offers users a means of hunting for papers in specific fields, and then filter your search by date, publication and so on. The developers believe that Semantic Scholar stands above the likes of Google Scholar due to the smarter way in which it hunts down relevant papers. "Essentially, it allows you to track at the concept level, or the technology level, rather than the article level," the team say.


A Kaggler's Guide to Model Stacking in Practice

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Stacking (also called meta ensembling) is a model ensembling technique used to combine information from multiple predictive models to generate a new model. With R's LiblineaR package we get two hyper parameters to tune: The grid of parameter combinations we'll test is the cartesian product of the 5 listed SVM types with cost values of (.01, .1, 1, 10, 100, 1000, 2000). Unsurprisingly, the SVM does a good job at classifying Bob's throws and Sue's throws but does poorly at separating Kate's throws and Mark's throws. Effectively what we've just done is built a predictive model that predicts user_i will purchase product_x with probability based on the percent of advertised products he purchased in the past and used those predictions as a meta feature for our real model.


Zuckerberg charity buys artificial intelligence startup to battle disease

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SAN FRANCISCO: A charitable foundation backed by Mark Zuckerberg and his wife has said it has bought a Canadian artificial intelligence startup as part of a mission to eradicate disease. The Chan Zuckerberg Initiative did not disclose financial terms of the deal to acquire Toronto-based Meta, which uses AI to quickly read and comprehend scientific papers and then provide insights to researchers. Zuckerberg and his doctor wife, Priscilla Chan, in September pledged $3 billion over the next decade to help banish or manage all disease, pouring some of the Facebook founder's fortune into innovative research. "This is a big goal," Zuckerberg said at a San Francisco event announcing the effort of the philanthropic entity established by the couple in 2015.