Here is how to make a Stereolithography 3D Printer. It is still a bit of a work in progress but so far it is working pretty well. This is mainly an experiment which started as a Delta Robot Stereolithography Printer but ended as a more traditional Cartesian Stereolithography Printer. "I'll be honest, we're throwing science at the walls here to see what sticks. No idea what it'll do." - Cave Johnson Stereolithography (SL or SLA from Stereolithography Apparatus) is an additive manufacturing process using a vat of liquid UV-curable photopolymer "resin" and a UV laser to build parts one layer at a time. On each layer, the laser beam traces a cross-section pattern of the part onto the surface of the liquid resin. Exposure to the UV laser light cures, solidifies the pattern traced on the resin and adheres it to the layer below. I have wanted a 3D ...
Kurzweil 08/12/26 : "MIT researchers have designed a new machine-learning system that can learn by itself to extract text information for statistical analysis when available data is scarce.This new “information extraction” system turns machine learning on its head...
Esta animación de Science on a Sphere (parte del NOAA) muestra todos los terremotos que se registraron entre 2001 y 2015 con un círculo de diferente tamaño según su magnitud (los colores indican la profundidad; rojo = más superficial; verde-azul-morado = más profundo). Es bastante ilustrativo cómo quedan marcadas las zonas limítrofes de las placas tectónicas, que son las que al moverse y colisionar producen los temblores de tierra. También impresiona el efecto time-lapse al comprimir el tiempo en una sola foto que muestra lo que sucedió cada día de todos esos años.
Impresionan también los terremotos más grandes, como los de Sumatra/Indonesia (2004) o el de Japón (2011), el de Chile (2010), que produjeron terribles pérdidas humanas sobre todo por los efectos de los maremotos asociados.
Of course, machine translation is still far from perfect. Despite its advances, GNMT can still mistranslate, particularly when it encounters proper names or rare words, which prompt the system to, again, translate individual words instead of looking at them within the context of the whole. Clearly, there is still a gap between human and machine translations, but with GNMT, it is getting smaller.
Big data has been a game changer for almost every industry. While it has created many new opportunities for businesses to improve efficiency and provide higher quality services, it also raises some other concerns.
En mars, Google annonçait sa nouvelle suite Google Analytics 360 : 6 outils complémentaires, pour permettre aux marketeurs d’analyser plus finement leurs données. Un de ces outils, Data Studio, sera disponible en freemium, puisque Google vient d’annoncer la mise à disposition d’une version gratuite de Data Studio. Objectif : permettre aux professionnels et aux petites équipes de visualiser leurs données et générer facilement des reportings.
Below, you’ll find a collection of innovations which are (re)defining customer expectations. These innovations – and the 5 big consumer trends they represent – show you glimpses of what your customers will want next.Understanding these trends will help you stay ahead of accelerating expectations, and lead you to actionable innovation opportunities.
Readers who are serious about understanding the direction of consumerism across multiple dimensions will have already spotted that these trends don’t sit in splendid isolation. They are a handful of near-future fragments of the Bigger Picture – more on that below.
But each one is also a killer opportunity to take to your team right now.So: read, think, debate. Then get going with building brands that matter, products that delight and campaigns people can't stop talking about!...
More data, more types of data, and the need to leverage it all to create more business value -- those are some of the drivers behind enterprise adoption of master data management platforms.
Guest blog post by Rebecca Kaye, Data Visualisation Specialist at Numbertelling -- In 2020, it is predicted that the world will produce 50 times the amount of data it did in 2011 (IDC, 2011). Within this data lies huge opportunities for discovery and improvement. Data from devices such as strava are helping to shape cities designed for people rather than cars, the explosion in medical data combined with innovative technology is transforming global health. The list could go on.
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