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This week, Lille, France hosts the 2015 International Conference on Machine Learning ( #ICML2015  - http://goo.gl/LZDfXu), a premier annual Machine Learning event supported by the International Machine Learning Society (IMLS).

As a Platinum Sponsor and leader in Machine Learning research, Google will have a strong presence at ICML 2015, with many Googlers publishing work and hosting workshops. Check out the Google Research blog, linked below, to see some of the research being presented.
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Training a Conversational Engine

Advances in end-to-end training of neural networks have led to remarkable progress in the fields of speech recognition, computer vision, and language processing. But can neural networks also allow one to have a productive conversation with a computer?

In A Neural Conversational Model, a paper being presented in the Workshop on Deep Learning at the 2015 International Conference on Machine Learning (http://icml.cc/2015/), Google Research Scientists +Oriol Vinyals and Quoc V. Le present a simple approach for conversational modeling that allows the model to remember facts, understand contexts and perform common sense reasoning, all with fewer hand-crafted rules.

Read the full paper at http://goo.gl/f05SjL, and check out a summary by Bloomberg, linked below, to learn more.
The search giant has a fresh development in artificial intelligence that could one day lead to a wise personal assistant
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would this be visible?
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We congratulate the recipients of the Google Computational Journalism Research Awards, supporting researchers working on exciting projects to create innovative new tools and open source software that will support online journalism and benefit readers.
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Thanks a lot +Research at Google 
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Using Natural Language Understanding technology, the Google Research team taught +Inbox by Gmail to recognize to-dos in email so it can suggest adding Reminders. Hopefully, this little extra help gets you back to what matters more quickly and easily. 
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I noticed this today... but it suggested to me finding a place for coffee or tea ... tea? hmm algorithm needs a little tweaking there... haha
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Catching up on new research from the Spam and Abuse team
posted by +Elie Bursztein, Google Spam and Abuse Research Team

The Google Spam and Abuse team has been busy lately with new research focused on helping to improve users' experiences and keep the web safe. As an important part of our efforts to educate and protect people online, we’d like to highlight two of these studies which which have been presented at conferences recently.

Deceptive Ad Injection Hurting Users
The Spam and Abuse research team - in collaboration with Safe Browsing, Ad Spam, and university partners - investigated the prevalence of deceptive ad injection and the tangled web of different players involved (http://goo.gl/K3oV0l). The paper Ad Injection at Scale: Assessing Deceptive Advertisement Modifications (http://goo.gl/5kMyVS), was presented in May at the IEEE Symposium on Security & Privacy (http://goo.gl/qzfL4O) and was awarded Best Paper.

The Ineffectiveness of Secret Questions
Secret questions are used by many services as an additional layer of security to protect against suspicious logins. Despite the prevalence of security questions, their safety and usability have rarely been studied in depth. In collaboration with the Identity Team and university partners, we recently investigated these questions (http://goo.gl/AdV7Og), concluding that secret questions are neither secure nor reliable enough to be used as a standalone account recovery mechanism. The paper Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google (http://goo.gl/xtbEhF), was presented in May at the International World Wide Web Conference (http://goo.gl/amSYRj) and was awarded Best Paper.

The team is constantly working on new projects and we’ll look forward to sharing more with you soon.
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Congratulations to +Richard Newcombe (University of Washington), +Dieter Fox (University of Washington) and +Steve Seitz (University of Washington / Google) for authoring the #cvpr2015  Best Paper DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time (http://goo.gl/slKrwB).

In the paper, these researchers introduced a real-time dense dynamic scene reconstruction system that removes the requirement that the subject to remain still, opening up a range of interesting applications for real-time 3D scanning.
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Impressive! Several applications from VR, games, tele presence, remote diagnosis, 3D scanning... Wonderful!
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At #cvpr2015 Google teams have participated in two competitions that are designed to spur advances in computational scene understanding, focusing on scene classification, saliency prediction, room layout estimation, and caption generation.
 
Congratulations to +Christian Szegedy+Julian Ibarz and +Vincent Vanhoucke for placing first in the Scene Classification category of  the Large Scale Scene Understanding Challenge (LSUN) and +Oriol Vinyals, +Alexander Toshev, +Samy Bengio, and +Dumitru Erhan for placing first in the Microsoft COCO Captioning Challenge (http://goo.gl/MK7FrV).

To learn more of the science behind the Caption Generation entry, visit https://goo.gl/xEml19 and read Show and Tell: A Neural Image Caption Generator at  http://goo.gl/J530X6. To learn more about the Scene Classification entry, check out Going Deeper with Convolutions (http://goo.gl/ylJDxT) and Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (http://goo.gl/rIWMBb).
Introduction. PASCAL VOC and ImageNet ILSVRC challenges have enabled significant progress for object recognition in the past decade. We plan to borrow this mechanism to speed up the progress for scene understanding as well. Complementary to the object-centric ImageNet ILSVRC Challenge hosted at ...
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Magistral +Research at Google 
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Generate your own Neural Network inspired images with DeepDream

Two weeks ago we blogged about a visualization tool designed to help us understand how neural networks work and what each layer has learned (http://goo.gl/pUfbyH). In addition to gaining some insight on how these networks carry out classification tasks, we found that this process also generated some beautiful art.

Now you can make your own images using an open source IPython notebook, which allows you to choose which layers in the network to enhance, how many iterations to apply and how far to zoom in. Alternatively, different pre-trained networks can be plugged in.

It'll be interesting to see what imagery people are able to generate. If you post images to Google+, Facebook, or Twitter, be sure to tag them with #deepdream so other researchers can check them out too.
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Last week we took a peek inside neural networks, showing how they carry out classification tasks, and in the process discovered that they can generate some beautiful images (goo.gl/ENFbHZ). But how would another network, designed to caption images, describe them?

University of Toronto PhD student Ryan Kiros used a caption generating neural network with “visual attention” mechanisms (http://goo.gl/JQu0eq) to describe the images in our gallery. Check out the link below to see more.
Image, Attention. A dog is standing on a cart with a large horse . A large group of animals are in a field of grass . A large clock tower in the middle of a city . A large piece of cake with a colorful design on it . A large brown bear sitting on the side of a road .
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wonder when it'll be possible to see this in video..  you know,  the fractal grid moving in its wave like form in the background and all those animal heads come and go into existance morphing to other faces and objects..   
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Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t.

Over on the Google Research blog, we take a look at some simple techniques for peeking inside these networks, yielding a qualitative sense of the level of abstraction that particular layers of neural networks have achieved in their understanding of images. This helps us visualize how neural networks are able to carry out difficult classification tasks, improve network architecture, and check what the network has learned during training. 

It also makes us wonder whether neural networks could become a tool for artists—a new way to remix visual concepts—or perhaps even shed a little light on the roots of the creative process in general.
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+Frederik Egger 
No, sorry :(
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Happy Birthday to +Project Loon! Check out their post below to see the evolution of balloon designs - such as the Pterodactyl, the Grackle, the Merlin, and the Nighthawk - in the pursuit of bringing Internet connectivity to rural and remote communities around the world.
 
Today, Project Loon turns two! It’s been quite a journey—16 million kilometers to be precise—since we first connected sheep farmer Charles Nimmo to the Internet during our 2013 pilot test.

Our earliest tests started back in 2011, using a weather balloon and basic, off-the-shelf radio parts. These tests showed that balloon-powered Internet might just work, but the team knew that weather balloons wouldn't be a long term solution since they aren’t built to last in the stratosphere. So, our balloon enthusiasts got down to work and asked: if we wanted to bring balloon powered Internet to the whole world, what type of balloon would we need to build?

We started by building much, much bigger balloons able to hold equipment capable of beaming connectivity 20 km down to the earth below—starting with our modestly larger early Albatross design, all the way up to our 141-foot-long Hawk and beyond. To ensure there’s always a balloon overhead to provide connection, we needed to build a system that can manufacture these balloons at scale, leading to our latest balloon design, the Nighthawk, the likes of which has never been seen before.

Take a peek into our archives to see how our balloons have developed over time to deal with these challenges, from our very first ‘prehistoric’ balloons all the way to our latest flock design.
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Google Research aims to leverage the power of machine learning to improve real-world applications. One example is Google Photos (https://goo.gl/RWvg69), that makes use of a convolutional neural network architecture, dubbed Inception, which was responsible for setting the new state of the art for classification and detection in the 2014 ImageNet Large-Scale Visual Recognition Challenge. Learn more about Inception in the paper Going deeper with convolutions (https://goo.gl/uumIic), being presented at #cvpr2015 , and in our Research blog post at http://goo.gl/1EB6Xt.
An exclusive look under the hood of the product that wants to be the world’s scrapbook
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+Julie Letford but we can't simply say "abracadabra" and all our photos of cats and dogs are sorted without us having to do anything. we do need an app for that. or an A.I. to be more specific.
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Introduction
Google is full of smart people working on some of the most difficult problems in computer science today. Most people know about the research activities that back our major products, such as search algorithms, systems infrastructure, machine learning, and programming languages. Those are just the tip of the iceberg; Google has a tremendous number of exciting challenges that only arise through the vast amount of data and sheer scale of systems we build.

What we discover affects the world both through better Google products and services, and through dissemination of our findings by the broader academic research community.  We value each kind of impact, and often the most successful projects achieve both.