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          「ウォーリーを探せ!」のウォーリーを最短4.45秒で見つけて指し示すロボットが開発される      Cache   Translate Page   Web Page Cache   
#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000
「ウォーリーをさがせ!」からウォーリーを見つけることだけに特化したロボットが開発されました。このロボットはページを確認してから最短4.45秒でウォーリーを見つけられるとのことです。This robot uses AI to find Waldo, thereby ruining Where’s Waldo - The Vergehttps://www.theverge.com/circuitbreaker/2018/8/8/17665268/wheres-waldo-finding-robot-google-cloud-automl-aiロボットが「ウォーリーをさがせ!」から実際にウォーリーを見つけ出している様子は、以下のムー 全文
GIGAZINE(ギガジン) 08月09日 11時35分


          Se acabó el juego: en 2018 es un robot con visión artificial el que encuentra a Wally (en segundos)      Cache   Translate Page   Web Page Cache   

¿Dónde está Wally?

"Aquí está Wally" (o Waldo, según el país) es un robot cuya misión en esta vida es pasar páginas de los libros de ¿Dónde está Wally?, tomar una foto de la página y localiza en ella a Wally

"El robot utiliza OpenCV para encontrar y extraer caras de la foto. Los rostros se envían al servicio Google Auto ML Vision que compara cada uno de ellos con el modelo Waldo entrenado. Si se encuentra una coincidencia segura del 95% (0,95) o superior, el brazo del robot recibe instrucciones de extenderse hasta las coordenadas de la cara coincidente y señalarla. Si hay varios wallys en una misma página el robot señalará dónde está cada uno de ellos."

Según la agencia Red Pepper el robot "Aquí está Wally" es un prototipo y tarda algo más de 4 segundos en localizar a los wallys, "más rápido que la mayoría de los niños de 5 años."

Vía The Verge.

# Enlace Permanente


          Un robot entrenado para encontrar a Wally      Cache   Translate Page   Web Page Cache   


El dispositivo es solo un prototipo para probar los sistemas de reconocimiento facial con dibujos, pero es capaz de encontrar a Wally en apenas 4,45 segundos, “más rápido que la mayoría de niños de 5 años”, bromea su autor, Matt Reed. El aparato dispone de un brazo robótico y una cámara que toma una primera foto de la página y la envía para que el sistema de reconocimiento de Google (AutoML Vision), que ha sido previamente entrenado con las caras de Wally, identifique y localice al personaje.



Reed se inspiró en el uso reciente del sistema de reconocimiento facial de Amazon para identificar caras de famosos. “Obtuve todas las imágenes de Wally para el entrenamiento de las búsqueda de Google Imágenes; 62 cabezas sueltas de Wally y 45 cabezas con cuerpo”, asegura en The Verge. “Pensé que no serían suficientes para construir un modelo robusto, pero hace predicciones sorprendentemente buenas sobre Wallys que no estaban en el set original de entrenamiento”.

Más info en: This robot uses AI to find Waldo, thereby ruining Where’s Waldo (The Verge)
          Un robot entrenado para encontrar a Wally #Fogonazos #noticias      Cache   Translate Page   Web Page Cache   



El dispositivo es solo un prototipo para probar los sistemas de reconocimiento facial con dibujos, pero es capaz de encontrar a Wally en apenas 4,45 segundos, "más rápido que la mayoría de niños de 5 años", bromea su autor, Matt Reed. El aparato dispone de un brazo robótico y una cámara que toma una primera foto de la página y la envía para que el sistema de reconocimiento de Google (AutoML Vision), que ha sido previamente entrenado con las caras de Wally, identifique y localice al personaje.



Reed se inspiró en el uso reciente del sistema de reconocimiento facial de Amazon para identificar caras de famosos. "Obtuve todas las imágenes de Wally para el entrenamiento de las búsqueda de Google Imágenes; 62 cabezas sueltas de Wally y 45 cabezas con cuerpo", asegura en The Verge. "Pensé que no serían suficientes para construir un modelo robusto, pero hace predicciones sorprendentemente buenas sobre Wallys que no estaban en el set original de entrenamiento".

Más info en: This robot uses AI to find Waldo, thereby ruining Where's Waldo (The Verge)
Entrada publicada en Fogonazos http://www.fogonazos.es/


          After NEXT 2018: Trends in higher education and research      Cache   Translate Page   Web Page Cache   

From classrooms to campus infrastructure, higher education is rapidly adapting to cloud technology. So it’s no surprise that academic faculty and staff were well represented among panelists and attendees at this year’sGoogle Cloud Next. Several of our more than 500 breakout sessions at Next spoke to the needs of higher education, as as did critical announcements like our partnership with the National Institutes of Health to make make public biomedical datasets available to researchers. Here are ten major themes that came out our higher education sessions at Next:

  1. Collaborating across campuses. Learning technologists from St. Norbert College, Lehigh University, University of Notre Dame, and Indiana University explained how G Suite and CourseKit, Google’s new integrated learning management tool, are helping teachers and students exchange ideas.
  2. Navigating change.Academic IT managers told stories of how they’ve overcome the organizational challenges of cloud migration and offered some tips for others: start small, engage key stakeholders, and take advantage of Google’s teams of engineers and representatives, who are enthusiastic and knowledgeable allies. According to Joshua Humphrey, Team Lead, Enterprise Computing, Georgia State University, "We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to. Usability and savings."
  3. Fostering student creativity. In our higher education booth at Next, students demonstrated projects that extended their learning beyond the classroom. For example, students at California State University at San Bernardino built a mobile rover that checks internet connectivity on campus, and students at High Tech High used G Suite and Chromebooks to help them create their own handmade soap company.
  4. Reproducing scientific research. Science is built on consistent, reliable, repeatable findings. Academic research panelists at the University of Michigan are using Docker on Compute Engine to containerize pipeline tools so any researcher can run the same pipeline without having to worry about affecting the final outcome.
  5. Powering bioinformaticsToday’s biomedical research often requires storing and processing hundreds of terabytes of data. Teams at SUNY Downstate, Northeastern, and the University of South Carolina demonstrated how they used BigQuery and Compute Engine to build complex simulations and manage huge datasets for neuroscience, epidemiology, and environmental research.
  6. Accelerating genomics research. Moving data to the cloud enables faster processing to test more hypotheses and uncover insights. Researchers from Stanford, Duke, and Michigan showed how they streamlined their genomics workloads and cut months off their processing time using GCP.
  7. Democratizing access to deep learningAutoML Vision, Natural Language, and Translation, all in beta, were announced at Next and can help researchers build custom ML models without specialized knowledge in machine learning or coding. As Google’s Chief Scientist of AI and Machine Learning Fei-Fei Li noted in her blog post, Google’s aim “is to make AI not just more powerful, but more accessible.”
  8. Transforming LMS analytics. Scalable tools can turn the data collected by learning management systems and student information services into insights about student behavior. Google’s strategic partnership with Unizen allows a consortium of universities to integrate data and learning sciences, while Ivy Tech used ML Engine to build a predictive algorithm to improve student performance in courses.
  9. Personalizing machine learning and AI for student services. We’re seeing a growing trend of universities investigating AI to create virtual assistants. Recently Strayer University shared with us how they used Dialogflow to do just that, and at Next, Carnegie Mellon walked us through their process of building SARA, a socially-aware robot assistant.
  10. Strengthening security for academic IT: Natural disasters threaten on-premise data centers, with earthquakes, flooding, and hurricanes demanding robust disaster-recovery planning. Georgia State, the University of Minnesota, and Stanford’s Graduate School of Business shared how they improved the reliability and cost-efficiency of their data backup by migrating to GCP.
We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to: usability and savings Joshua Humphrey
Enterprise Computing, Georgia State University



To learn more about our solutions for higher education, visit our website, explore our credits programs for teaching and research, or speak with a member of our team.


          This Robot Uses Computer Vision to Find Waldo In 4.5 Seconds      Cache   Translate Page   Web Page Cache   
An arm-and-camera pointer uses Google AutoML to spot Waldo way faster than a kid can.
          ¿Sabes dónde está Wally? Pues este robot no solo lo sabe, sino que lo señala antes que tú       Cache   Translate Page   Web Page Cache   

Wally

¿Te acuerdas de Wally? Es ese simpático personaje con el jersey de rayas rojas y blancas que parece no querer separarse de su gorro bajo ningún concepto. Lo creó el dibujante inglés Martin Handford en 1987, y aún hoy podemos encontrarlo en una colección de libros infantiles que tienen como único objetivo entretener y poner a prueba nuestra agudeza visual.

Algunas de las ilustraciones de los libros de la colección ‘¿Dónde está Wally?’ representan un reto para cualquiera. Bueno, para cualquiera no. Y es que Matt Reed, un experto en tecnología que trabaja en la agencia de marketing estratégico RedPepper, ha construido un robot equipado con inteligencia artificial que es capaz de localizar a Wally en cualquier ilustración en unos pocos segundos. ¿Su récord? Según su creador, nada menos que 4,45 s, y es posible que consiga mejorarlo en el futuro porque, por el momento, este robot solo es un prototipo.

Así es como este robot da con Wally

Para construir su robot Matt Reed ha utilizado un brazo robótico articulado uArm Swift Pro, fabricado por UFACTORY, que está controlado por una Raspberry Pi y que cuenta con una cámara diseñada para aplicaciones de visión computacional que abarcan desde el reconocimiento facial hasta la percepción del color. El procedimiento que sigue es bastante intuitivo.

Lo primero que hace el brazo robótico cuando colocamos delante de la cámara uno de los libros de Wally es corregir el encuadre y tomar una fotografía de la doble página en la que, en principio, se oculta este personaje. La cámara es una OpenMV y cuenta con un procesador ARM Cortex M7 y 512 Kbytes de RAM. Una vez que ha identificado los rostros de todos los personajes que aparecen en la viñeta se los envía al servicio Cloud AutoML Vision de Google, que previamente ha sido entrenado para reconocer el rostro de Wally.

Este servicio es un paquete de algoritmos de inteligencia artificial especializado en aprendizaje automático que puede ser utilizado, al menos sobre el papel, por desarrolladores con poca experiencia, o, incluso, sin ninguna experiencia, en inteligencia artificial y redes neuronales. Lo interesante es que los algoritmos de Google son capaces de identificar a Wally con una precisión del 95%.

Matt Reed utilizó 62 imágenes de la cabeza de Wally que encontró en Internet, y 45 imágenes de su cuerpo completo, para llevar a cabo el entrenamiento de la inteligencia artificial. ¿El resultado? Cuando menos, sorprendente. Os sugiero que no os perdáis el vídeo que ilustra el artículo porque dura menos de un minuto y no tiene desperdicio. Ingenioso, ¿verdad?

Vía | RedPepper
En Xataka | Los rayos gamma y el aprendizaje automático son dos de las bazas en las que los físicos confían para encontrar la materia oscura

También te recomendamos

Move Mirror, el nuevo experimento de Google que compara tus movimientos con miles de imágenes en tiempo real

Las matemáticas seguirán siendo la base del futuro, ¿estamos preparados?

Los rayos gamma y el aprendizaje automático son dos de las bazas en las que los físicos confían para encontrar la materia oscura

-
La noticia ¿Sabes dónde está Wally? Pues este robot no solo lo sabe, sino que lo señala antes que tú fue publicada originalmente en Xataka por Juan Carlos López .


          After NEXT 2018: Trends in higher education and research      Cache   Translate Page   Web Page Cache   

From classrooms to campus infrastructure, higher education is rapidly adapting to cloud technology. So it’s no surprise that academic faculty and staff were well represented among panelists and attendees at this year’sGoogle Cloud Next. Several of our more than 500 breakout sessions at Next spoke to the needs of higher education, as as did critical announcements like our partnership with the National Institutes of Health to make make public biomedical datasets available to researchers. Here are ten major themes that came out our higher education sessions at Next:

  1. Collaborating across campuses. Learning technologists from St. Norbert College, Lehigh University, University of Notre Dame, and Indiana University explained how G Suite and CourseKit, Google’s new integrated learning management tool, are helping teachers and students exchange ideas.
  2. Navigating change.Academic IT managers told stories of how they’ve overcome the organizational challenges of cloud migration and offered some tips for others: start small, engage key stakeholders, and take advantage of Google’s teams of engineers and representatives, who are enthusiastic and knowledgeable allies. According to Joshua Humphrey, Team Lead, Enterprise Computing, Georgia State University, "We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to. Usability and savings."
  3. Fostering student creativity. In our higher education booth at Next, students demonstrated projects that extended their learning beyond the classroom. For example, students at California State University at San Bernardino built a mobile rover that checks internet connectivity on campus, and students at High Tech High used G Suite and Chromebooks to help them create their own handmade soap company.
  4. Reproducing scientific research. Science is built on consistent, reliable, repeatable findings. Academic research panelists at the University of Michigan are using Docker on Compute Engine to containerize pipeline tools so any researcher can run the same pipeline without having to worry about affecting the final outcome.
  5. Powering bioinformaticsToday’s biomedical research often requires storing and processing hundreds of terabytes of data. Teams at SUNY Downstate, Northeastern, and the University of South Carolina demonstrated how they used BigQuery and Compute Engine to build complex simulations and manage huge datasets for neuroscience, epidemiology, and environmental research.
  6. Accelerating genomics research. Moving data to the cloud enables faster processing to test more hypotheses and uncover insights. Researchers from Stanford, Duke, and Michigan showed how they streamlined their genomics workloads and cut months off their processing time using GCP.
  7. Democratizing access to deep learningAutoML Vision, Natural Language, and Translation, all in beta, were announced at Next and can help researchers build custom ML models without specialized knowledge in machine learning or coding. As Google’s Chief Scientist of AI and Machine Learning Fei-Fei Li noted in her blog post, Google’s aim “is to make AI not just more powerful, but more accessible.”
  8. Transforming LMS analytics. Scalable tools can turn the data collected by learning management systems and student information services into insights about student behavior. Google’s strategic partnership with Unizen allows a consortium of universities to integrate data and learning sciences, while Ivy Tech used ML Engine to build a predictive algorithm to improve student performance in courses.
  9. Personalizing machine learning and AI for student services. We’re seeing a growing trend of universities investigating AI to create virtual assistants. Recently Strayer University shared with us how they used Dialogflow to do just that, and at Next, Carnegie Mellon walked us through their process of building SARA, a socially-aware robot assistant.
  10. Strengthening security for academic IT: Natural disasters threaten on-premise data centers, with earthquakes, flooding, and hurricanes demanding robust disaster-recovery planning. Georgia State, the University of Minnesota, and Stanford’s Graduate School of Business shared how they improved the reliability and cost-efficiency of their data backup by migrating to GCP.
We've been using GCP for almost three years now and we've seen an average yearly savings of 44%. Whenever people ask why we moved to the cloud this is what we point to: usability and savings Joshua Humphrey
Enterprise Computing, Georgia State University



To learn more about our solutions for higher education, visit our website, explore our credits programs for teaching and research, or speak with a member of our team.


          Este robot sabe muito bem onde está o Wally      Cache   Translate Page   Web Page Cache   
Criado com base no AutoML, da Google, este robot consegue identificar o Wally com uma precisão cirúrgica, uma vez que o hardware e o software equipado nesta máquina conferem-lhe capacidades de reconhecimento facial


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