En esta charla veremos como utilizar servicios cognitivos en la nube desde python de dos de los grandes actores: IBM Watson y Microsoft Azure Cognitive Services. Presentaremos una introducción a dichos servicios y finalmente veremos una aplicación sobre la construcción de un robot asistente para personas mayores.
El ponente será Javier Palanca, investigador en IA en la UPV.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sahil Bansal, Senior Director of Product Management at Qualcomm, presents the "Achieving High-Performance Vision Processing for Embedded Applications with Qualcomm SoC Platforms" tutorial at the May 2018 Embedded Vision Summit.
Advancements in machine learning are making it possible to equip devices such as connected cameras, robots, drones and smart home solutions with improved on-device vision processing and analytics capabilities. This talk addresses how a hybrid approach, combining deep learning with traditional computer vision and utilizing Qualcomm Technologies System-on-Chips (SoCs) and solutions can deliver significant performance and power-efficiency improvements for embedded applications requiring vision processing. Bansal presents the benefits and trade-offs, use cases and results from concrete implementations using this hybrid approach.
What are the safety risks with autonomous robots? Are autonomous robot a threat to human workers? How can robots achieve intelligent behavior? What mechanisms are needed to reach human agility?
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For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sahil Bansal, Senior Director of Product Management at Qualcomm, presents the "Achieving High-Performance Vision Processing for Embedded Applications with Qualcomm SoC Platforms" tutorial at the May 2018 Embedded Vision Summit.
Advancements in machine learning are making it possible to equip devices such as connected cameras, robots, drones and smart home solutions with improved on-device vision processing and analytics capabilities. This talk addresses how a hybrid approach, combining deep learning with traditional computer vision and utilizing Qualcomm Technologies System-on-Chips (SoCs) and solutions can deliver significant performance and power-efficiency improvements for embedded applications requiring vision processing. Bansal presents the benefits and trade-offs, use cases and results from concrete implementations using this hybrid approach.
What are the safety risks with autonomous robots? Are autonomous robot a threat to human workers? How can robots achieve intelligent behavior? What mechanisms are needed to reach human agility?
You know what's cool? Running on a billion devicesDaniel Stenberg
About curl, libcurl and the open source project behind them. A small project effecting every single human daily in the connected world. FOSDEM, February 2017
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For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
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To get access to the webinar kindly contact your respective department heads.
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6. •Puesta en marcha muy
rápida
•Acceso a grandes
recursos hardware
•APIs de acceso variadas
(http, python, …)
•Interfaces gráficas
•Permiten bajar de nivel
•Servicios de pago
•Dependencia de una empresa
•Pocos algoritmos pre-
determinados
•Velocidad de acceso
(Auth tokens, …)
•No es Software Libre
31. Rasselrobot assistant for the elderly
Maite Giménez · Jaume Jordán · Javier Palanca · Jaime Rincón
IBM Challenge Cogs For Good
32. Detección del nicho
DEFINICIÓN DE LA NECESIDAD:
Incremento de población de tercera edad y de-
pendientes.
Necesidad de acompañamiento y asistencia.
Aumentar la autosuficiencia de la persona
asistida.
Garantizar la seguridad de la persona con de-
pendencia en el hogar.
33. Propuesta de valor
Asistente conversacional.
Recomendaciones para una vida activa y satisfactoria.
Monitorización de constantes vitales.
Detección de caidas y de situaciones de riesgo.
Sistema de alertas a familiares.
RASSEL TE CUIDA COMO ELLA TE CUIDÓ A TI.
36. características de la pulsera
pulsera
SENSORES:
Acelerómetro y giroscopio IMU.
Sensor de fotopletismografía.
Electrocardiografía: requiere interacción
de la persona.
GCR: resistencia de piel
39. funcionalidad de rassel
CONVERSACIÓN: Watson assistant, text2speech, speech2text.
RECOMENDADOR ACTIVIDADES: Collaborative filtering y Reinforce-
ment Learning.
RECORDATORIOS MEDICACIÓN.
MONITORIZACIÓN DE CONSTANTES VITALES: IBM IoT.
DETECCIÓN DE CAÍDAS: Acelerómetro y Visual Recognition IBM.
ALERTAS Y AVISOS.