lunes, 24 de abril de 2017

10 uses cases - Artificial Intelligence and Machine Learning in Education #AI

Hoy traemos a este espacio esta slideshare titulada "10 uses cases - Artificial Intelligence and Machine Learning in Education" de ai.business ... que comienzan explicándonos así, en su post titulado ...
In the beginning of 2016 Jill Watson, an IBM-designed bot, has been helping graduate students at Georgia Institute of Technology solve problems with their design projects. Responding to questions over email and posted on forums, Jill had a casual, colloquial tone, and was able to offer nuanced and accurate responses within minutes. A robot has been teaching graduate students for 5 months and none of them realized. Here are just a few of artificial intelligence tools and technologies that will shape and define the educational experience of the future.
Duolingo: voice recognition for language learning
Duolingo is the world’s most popular platform to learn a language. App predicts your word strength, figures out which sentences will help you best practice your weakest words/skills, recommends immersion practice documents (translations) based on your progress and estimates the quality of a translation-in-progress.
Plexuss: college comparison and recruitment platform
Plexuss facilitates contact between universities and future students, and aims to help students make an informed decision when it comes to choosing the right university. It allows users to take a virtual tour of their selected campuses, compare colleges, and chat with universities of their choice. The platform includes a college ranking system, which collates data from trustworthy sources including Forbes, Reuters and Shanghai Ranking. Algorithm compares data using a variety of criteria like in- and out-of-state tuition, ...(leer más...) 
 Fuente: [ slideshare ]

domingo, 23 de abril de 2017

A ENCRUZILHADA DA UNIVERSIDADE EUROPEIA. Boaventura de Sousa Santos

Hoy traemos a este espacio, para la lectura un artículo de Boaventura de Sousa Santos, titulado A ENCRUZILHADA DA UNIVERSIDADE EUROPEIA. publicado en Revista / Ensino Superior 41 - Revista do SNESup: Julho - Agosto - Setembro 2011 (leer más...)

 Fuente: [ slideshare , vía Revista Ensino Superior]

sábado, 22 de abril de 2017

MAGENTA. Make Music and Art Using Machine Learning. @douglas_eck



´Hoy traemos a este espacio a Make Music and Art Using Machine Learning, que nos presentan así;

About Magenta

Magenta is a Google Brain project to ask and answer the questions, “Can we use machine learning to create compelling art and music? If so, how? If not, why not?” Our work is done in TensorFlow, and we regularly release our models and tools in open source. These are accompanied by demos, tutorial blog postings and technical papers. To follow our progress, watch our GitHub and join our discussion group.
Magenta encompasses two goals. It’s first a research project to advance the state-of-the art in music, video, image and text generation. So much has been done with machine learning to understand content—for example speech recognition and translation; in this project we explore content generation and creativity. Second, Magenta is building a community of artists, coders, and machine learning researchers. To facilitate that, the core Magenta team is building open-source infrastructure around TensorFlow for making art and music. This already includes tools for working with data formats like MIDI, and is expanding to platforms that help artists connect with machine learning models

Douglas Eck

I’m a research scientist working on Magenta, an effort to generate music, video, images and text using machine intelligence. Magenta is part of the Google Brain team and is using TensorFlow (www.tensorflow.org), an open-source library for machine learning. The question Magenta asks is, “Can machines make music and art? If so, how? If not, why not?” The goal if Magenta is to produce open-source tools and models that help creative people be even more creative. I’m primarily looking at how to use so-called “generative” machine learning models to create engaging media. Additionally, I’m working on how to bring other aspects of the creative process into play. For example, art and music is not just about generating new pieces. It’s also about drawing one’s attention, being surprising, telling an interesting story, knowing what’s interesting in a scene, and so on.
Before starting the Magenta project, I worked on music search and recommendation for Google Play Music. My research goal in this area was to use machine learning and audio signal processing to help listeners find the music they want when they want it. This involves both learning from audio and learning from how users consume music. In the audio domain, the main goal is to transform the ones and zeros in a digital audio file into something where musically-similar songs are also numerically similar, making it easier to do music recommendation. This is (a) user-dependent: my idea of similar is not the same as yours and (b) changes with context: my idea of similarity changes when I make a playlist for jogging versus making a playlist for a dinner party. I might choose the same song (say "Taxman" by the Beatles) but perhaps it would be the tempo for jogging that drove the selection of that specific song versus "I like the album Revolver and want to add it to the dinner party mix" for a dinner party playlist.
I joined Google in 2010. Before then, I was an Associate Professor in Computer Science at University of Montreal. I helped found the BRAMS research center (Brain Music and Sound; www.brams.org) and was involved at the McGill CIRMMT center (Centre for Interdisciplinary Research in Music Media and Technology; www.cirmmt.org). Aside from audio signal processing and machine learning, I worked on music performance modeling. What exactly does a good music performer add to what is already in the score? I treated this as a machine learning question: Hypothetically, if we showed a piano-playing robot a huge collection of Chopin performances--- from the best in the world all the way down to that of a struggling teenage pianist---could it learn to play well by analyzing all of these examples? If so, what’s the right way to perform that analysis? In the end I learned a lot about the complexity and beauty of human music performance, and how performance relates to and extends composition.

.(leer más...)
Fuente: [ magenta ]

viernes, 21 de abril de 2017

21 Stratégies pédagogiques ou technologiques. Être prof au 21e siècle @ThierryUdM


Hoy traemos a este espacio esta infografía titulada "21 Stratégies pédagogiques ou technologiques. Être prof au 21e siècle"  de Thierry KARSENTI, M.A., M.Ed., Ph.D. Titulaire de la Chaire de recherche du Canada sur les technologies en éducation, Université de Montréal @ThierryUdM http://karsenti.ca/etreprof.pdf  (leer más...)

 Fuente: [ slideshare vía Thierry]

jueves, 20 de abril de 2017

E-innovación en la educación superior . Revista Comunicar @Rev_Comunicar Vol. XXV, nº 51, 2º trimestre, 1 abril 2017


Hoy traemos a este espacio el último número de la revista Comunicar . Revista Científica de Comunicación y Educación. edia Education Research journal.

E-innovación en la educación superior

Vol. XXV, nº 51, 2º trimestre, 1 abril 2017
Editores temáticos
Dr. Ramón López-Martín - Universidad de Valencia - España
Dr. Paulo Dias - Universidad Abierta de Lisboa - Portugal
Dr. Alejandro Tiana-Ferrer - UNED - España
DOSSIER




01 La investigación formativa en ambientes ubicuos y virtuales en Educación Superior
Formative Research in Ubiquitous and Virtual Environments in Higher Education

Cristian Velandia-Mesa, Bogotá (Colombia), Francisca-José Serrano-Pastor, Murcia (España) & María-José Martínez-Segura, Murcia (España).

https://doi.org/10.3916/C51-2017-01

02 Uso de Twitter en Educación Superior en España y Estados Unidos
Using Twitter in Higher Education in Spain and the USA

Gemma Tur, Palma de Mallorca (España), Victoria Marín-Juarros, Oldenburg (Alemania)Jeffrey Carpenter, Elon (Estados Unidos).

https://doi.org/10.3916/C51-2017-02

03 Laboratorios sociales en universidades: Innovación e impacto en Medialab UGR
Social Labs in Universities: Innovation and impact in Medialab UGR

Esteban Romero-Frías, Granada (España) & Nicolás Robinson-García, Valencia (España).

https://doi.org/10.3916/C51-2017-03

04 La educación superior a distancia y el e-Learning en las prisiones en Portugal
Higher Education Distance Learning and e-Learning in Prisons in Portugal

José-António Moreira, Lisboa (Portugal), Angélica Reis-Monteiro, Oporto (Portugal)Ana Machado, Lisboa (Portugal).

https://doi.org/10.3916/C51-2017-04

05 Uso de cuestionarios online con feedback automático para la e-innovación en el alumnado universitario
Online Questionnaires Use with Automatic Feedback for e-Innovation in University Students

Ana Remesal, Barcelona (España), Rosa M. Colomina, Barcelona (España), Teresa Mauri, Barcelona (España) & M. José Rochera, Barcelona (España).

https://doi.org/10.3916/C51-2017-05



06 El uso de las redes sociales y la cultura popular para una mejor comprensión intercultural
The Use of Social Media and Popular Culture to Advance Cross-Cultural Understanding

Sait Tuzel, Rhode Island (Estados Unidos)Renee Hobbs, Rhode Island (Estados Unidos).

https://doi.org/10.3916/C51-2017-06

07 El rol del cine en versión original en el espacio digital europeo
The Role of Original Version Cinema into the European Digital Space

Margarita Ledo-Andión, Santiago de Compostela (España), Antía López-Gómez, Santiago de Compostela (España) & Enrique Castelló-Mayo, Santiago de Compostela (España).

https://doi.org/10.3916/C51-2017-07

08 La percepción de los periodistas españoles acerca de sus roles profesionales
Spanish Journalists’ Perception about their Professional Roles

Rosa Berganza, Madrid (España), Eva Lavín, Madrid (España) & Valeriano Piñeiro-Naval, Covilhã (Portugal).

https://doi.org/10.3916/C51-2017-08

09 Stop-motion para la alfabetización digital en Educación Primaria
Stop-motion to Foster Digital Literacy in Elementary School

Koun-Tem Sun, Tainan (Taiwán), Chun-Huang Wang, Tainan (Taiwán) & Ming-Chi Liu, Tainan (Taiwán).

https://doi.org/10.3916/C51-2017-09

10 Ecosistemas de formación y competencia mediática: Valoración internacional sobre su implementación en la educación superior
Ecosystems of Media Training and Competence: International Assessment of Its Implementation in Higher Education

Emilio Álvarez-Arregui, Oviedo (España), Alejandro Rodríguez-Martín, Oviedo (España), Rafael Madrigal-Maldonado, México D.F. (México), Beatriz-Ángeles Grossi-Sampedro, Oviedo (España) & Xavier Arreguit, Neuchatel (Suiza).

https://doi.org/10.3916/C51-2017-10

(leer más...)

 Fuente: [ COMUNICAR ]

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