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Vincent Vanhoucke
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Vincent Vanhoucke

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Gray Area Foundation for the Arts and Research at Google invite you to join us for a benefit auction and art exhibition of: DeepDream: The art of neural networks a special gallery show of artworks mad
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Huge news from our colleagues at DeepMind. Go was long considered a key problem for AI, because it's a game for which the 'try all combinations and see which does best' is simply not possible, and human Go players rely heavily on abstract pattern recognition to guide their decisions. This is further evidence that human-level pattern recognition is no longer a 'science' problem per-se (but still a mighty engineering problem). It's time for us to focus more on exploring higher levels of cognition.
Deep-learning software defeats human professional for first time.
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This accomplishment is exciting and incredible.  

The next milestone I'd love to see is pattern recognition above human level that can be trained with the same size of data sets a human would be exposed to.  Many real-world needs for AI wouldn't have big data available to them, and humans have to be good at recognizing patterns with limited data.

Deep-learning software often takes advantage of non-linearities by using functions that are convenient for GPU's to more easily do back-prop.  Is it possible that if we had specialized hardware that could handle more general non-linear functions in back-prop, that we could just as successfully train, but with smaller data sets?
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Found by my brother in a journeyman's museum in rural France, amidst a printer tool's exhibit. It reads: "Typographical correction system: this device enabled apprentices to correct spelling mistakes, known in the trade as 'typos'. On loan from l'Abbé Cé [Father Cé, 'A B C' if you say it out loud], priest at La Faute-sur-Mer [The-Mistake-on-Sea, which happens to be a real place]."


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Vincent Vanhoucke

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The origins of Deep Dream.
Why the neural net project creating wild visions has meaning for art, science, philosophy — and our view of reality
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Congratulations to Microsoft Research Asia for winning this year's ImageNet challenge!

Crossing 60% mean average precision on detection is a big result. In part, it suggests that we might soon be at the performance level users would actually consider ok if we showed detection results on a user-facing UI. This kind of milestone would dramatically change what we can do with computer vision technologies today.

Our team only entered in the classification competition and got 3.581% error, a tad behind the best 3.567%. In case you're wondering about our abysmal 19% localization error figure: we didn't even try ;-) This year's rules said we had to enter bounding box coordinates, so we just threw in the best proposal from a class-agnostic localizer.
Team name, Entry description, Number of object categories won, mean AP. MSRA, An ensemble for detection. 194, 0.620741. Qualcomm Research, NeoNet ensemble with bounding box regression. Validation mAP is 54.6, 4, 0.535745. Deep-ID Net, Combined multiple models with the region proposals of ...
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Vincent Vanhoucke

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Take a highly engineered state-of-the-art system, strip it down, replace it with a LSTM, and train it using a loss that matches your problem. Does that recipe sound familiar?
Venue. International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2016). Publication Year. 2016. Authors. Georg Heigold, Ignacio Moreno, Samy Bengio, Noam M. Shazeer. BibTeX. @inproceedings{44681, title = {End-to-End Text-Dependent Speaker Verification}, author = {Georg ...
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Can you explain the significance of this to the non-ML community?
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My Deep Learning Course with Udacity is live!

Four lectures and corresponding TensorFlow notebooks to learn about deep nets, convnets, embeddings, RNNs, and all the tricks to make them work!

Very excited to finally share, this was a lot of fun to put together.
Our brand new Deep Learning Course, a collaboration between Google and Udacity, will have you learning and mastering these techniques!
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+Dustin Soodak Problem solved -- We've got a unique discussion category for the Deep Learning course set up now: https://discussions.udacity.com/c/standalone-courses/deep-learning We also ported over any relevant threads from the Machine Learning forum. Hope you're enjoying the course! 
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Edge.org's annual question 'What do you consider the most interesting recent [scientific] news? What makes it important?' is an excellent read, as usual. Progress in deep learning has a strong showing (even DeepDream!), behind Crispr, the Pluto flyby, and the studies showing that the majority of psychology studies are not reproducible.

A few things stood out for me:
- If you're not in awe and a tiny bit terrified by recent advances in gene editing, you're not paying attention.
- Every year, a few authors invariably try to show how smart they are by going meta: 'what is news?' 'are news important?' I find that plain irritating. If you have nothing of substance to contribute, just don't.
- Alexander Wissner-Gross' answer that datasets are the primary bottleneck to progress in ML/AI advances, as opposed to algorithms, really resonates with me. I have seen enough algorithmic work invalidated by 'just add more data' over the years, that I am seriously questioning how much collective time we devote to improving modeling as opposed to data curation. When I look at the next big challenges in machine learning, I see few that have publicly available datasets comparable in size and quality to e.g. object recognition or speech.
Subscribe to Edge. ×. You can subscribe to Edge and receive e-mail versions of EdgeEditions as they are published on the web. Fill out the form, below, with your name and e-mail address and your subscription will be automatically processed. Email address *. Your name *. Country * ...
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I only discovered edge.org yesterday: fantastic read indeed. 
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My entry: 'Zuckerberg's yacht, Sir.'
+Doug Beeferman
Information about The New Yorker, including press information, social media, and a link to contributors' biographies.
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Big news for AI research. Congrats to +Ilya Sutskever and crew!
OpenAI is a non-profit artificial intelligence group.
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Human-level concept learning through probabilistic program induction
http://www.sciencemag.org/content/350/6266/1332.short
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We have just come out with a huge update (almost complete rewrite) to our previous MultiBox object-detection paper

The models are not the latest, but we have new numbers and updated method with our new convolutional multi-scale multibox:

http://arxiv.org/abs/1412.1441
Abstract: Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation ...
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Currently
San Francisco, CA
Previously
Public - 5 months ago
reviewed 5 months ago
Very good food. Bring a heavy jacket in the Winter months, this place is freezing and the waiters all wear one.
Public - a year ago
reviewed a year ago
A mean hot chocolate.
Public - a year ago
reviewed a year ago
By far the best meal I've had in Trouville. Don't be in a rush, we were the first in the Restaurant that day and were in there for 1 hour 45 minutes.
Public - 2 years ago
reviewed 2 years ago
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An avalanche of failures in the peak Holiday season. OpenTable booking broken, you have to go on site. Slow service. Inexperienced, confused looking staff. Tepid potato leek soup, much too salty to eat. Overcooked pasta and fries drowning in salt as well. This might have been a great restaurant in the past, but it's seemingly poorly managed.
Public - a year ago
reviewed a year ago
Fantastic food. Ever surprising menu. (edit: no longer the Incanto I remember)
Public - 2 years ago
reviewed 2 years ago
Indifferent, borderline hostile service. Appetizing menu but poor execution.
Public - 2 years ago
reviewed 2 years ago