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Steve Mitchell
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Steve Mitchell

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Brillo is interesting and makes sense.  But what's most interesting to me is Weave.  How will it facilitate device-device communication?  Authentication?  Discovery? Entitlements?  Will it just be an RPC thing as the screenshot might indicate? 
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Konekt is hardware, messaging services, and cellular plans for building internet connected IoT devices or adding connectivity to large numbers of existing devices at (presumably) reasonable prices based on device volume and data usage.
Global Cellular Plans to Connect Hardware to the Web. Whether you're building an Internet connected energy meter or a toaster that tweets, Konekt has the right setup for you.
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It’s Happening! NVIDIA is bulking up its GRID cloud gaming service with the introduction of 1080p 60 FPS streaming. This is big news for gamers as...
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"No More .. Sun Microsystems"  Silicon Valley is great.. 
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It's not just me, right?  The Office 365 Mail icon is supposed to represent an envelope with a 5.25" floppy disk in front of it.  Right?  That was supposed to be on purpose, correct?
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It's a Rorschach test - I see an Ubiquiti Access Point.
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The answer to that is it never is worth it!
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This could be an interesting study for anybody working on face recognition 
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Spencer Kent stands nervously in front of Team D.R.A.D.I.S.' booth at Rice University's annual Engineering Design Showcase. Judging begins in about 10 minutes, and his teammate Galen Schmidt is frantically typing computer code into a laptop beside the team's custom-made radar system.
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Is that a Panda, or a Gibbon? Investigating the mystery of Adversarial Examples

Machine Learning (ML) models show great promise in the field of computer vision, which focuses on enabling systems to model and understand digital images automatically (https://goo.gl/whsbSH). 

But sometimes those ML systems get it wrong. As it turns out, many machine learning models, including Neural Networks, have intriguing properties. One such property, “blind spots” (http://goo.gl/FPuCzk), causes them to misclassify adversarial examples - images that are formed by applying very small, but intentional, perturbations to existing correctly labeled examples. Moreover, when these different models misclassify an adversarial example, they often agree with each other on its class. But why does this happen? 

At the 2015 International Conference on Learning Representations (http://goo.gl/DxCNM1), Google Research Scientists +Ian Goodfellow, +Jon Shlens, and +Christian Szegedy presented Explaining and Harnessing Adversarial Examples (http://goo.gl/XCMCun), where they investigate neural networks’ vulnerability to adversarial perturbation.

Previously, the thinking was that adversarial examples were due to overfitting and the non-linear nature of Deep Neural Networks. In this paper, the authors argue that, rather, existing models are too linear, and that generalization of adversarial examples across different models can be explained as a result of the  different models learning similar functions when trained to perform the same task. In doing so, they propose a fast method of generating adversarial examples that can be used to help train models to resist adversarial perturbation.
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Kid's Day at Pelco
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Great read on engineering, specifications, and perception.  Money quote right in the first caption: "To the public, a car either is or isn’t faulty. To an engineer, imperfections and compromises are inevitable." 
To the public, a car either is or isn’t faulty. To an engineer, imperfections and compromises are inevitable. Credit Construction by Stephen Doyle / Photograph by Grant Cornett
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Very interesting about risk.  I was waxing floors  for minimum wage that summer in Elkhart when the Ulrich crash happened.  The conversations about it with my father in law (PhD in reformation history, i.e., a long way from engineering) taught me a lot about thinking like an engineer, or not.

The other time that risk hit me in the face was at JPL at a meeting where the team (I was just a visitor) was looking at thousands of known risks and mitigations for a project.
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From a couple of years ago, but a pretty good case study illustrating the limits of facial recognition. 
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