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Jeff Dean
Works at Google
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Jeff Dean

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The Show and Tell work from the Google Brain team ( a bit more than a year ago, along with concurrent work on the same topic by Berkeley, Toronto, Microsoft Research, and others, was some of the most exciting ML work in recent years. The notion that image models could not only classify an image ("train"), but could actually generate plausible whole sentences about an image purely from the raw pixels ("_A blue and yellow train is traveling down the tracks_") is pretty remarkable. Today, Chris Shallue from our group has released an open source implementation of an improved version of this model in TensorFlow. Happy captioning!

Edit: fix typo
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Well done!
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Jeff Dean

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We're open sourcing a TensorFlow model for text summarization that achieves state of the art results on a summarization task. Nice work, Peter Liu!
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ch shen
is it fit for chinese?
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Jeff Dean

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Today we're releasing two new datasets to help accelerate robotics research. We hope the robotics research community develops and evaluates new and interesting algorithms and learning techniques using this data.
Today, we're releasing two large datasets for robotics research:

Grasping: A collection of 650k grasp attempts, data used in:

Push: A collection of 59k examples of pushing motions, data used in:

Both datasets contain RGB-D views of the arm, gripper and objects, along with actuation and position parameters. They were collected in a controlled environment using a wide collection of everyday objects, some of which were held out for evaluation. Enjoy!

Credits: +Sergey Levine, +Chelsea Finn and +Laura Downs.
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A bit of fowl play by a TensorFlow powered robot.
The future is now. (h/t +Pete Warden)
TensorFlow powered robot recognizes a fried chicken nugget, picks it up and serve on a dish. #dltfb · Embedded image. 4:54 AM - 22 Jun 2016. 26 Retweets22 Likes. Reply to @kazunori_279. Home · Sign up · Log in · Search · About. More like this; Less like this; Cancel. Not on Twitter?
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Okay, this was pretty funny. And for the record, a recurrent model would have been a better approach 😉.
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+Jeff Dean can we expect questions for sorting algorithms based on TF or TSP solution on interviews to Google Brain team? :) 
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Jeff Dean

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I’m looking forward to Google I/O, which starts tomorrow (Wed.). I'll be on a panel on Friday morning discussing ways in which we're using machine learning at Google, and where we see this evolving. Come ask questions at our panel at Google I/O! Friday 9am PT #io16
Google has deployed practical A.I. throughout its products for the last decade -- from Translate, to the Google app, to Photos, to Inbox. The teams continue to make fundamental breakthroughs in machine learning, publishing promising new results at an accelerating pace. Now TensorFlow and Cloud Machine Learning make it even easier for researchers and developers around the world to collaborate. So as we work together to drive machine learning forwa...
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+Rokesh Jankie - I'm not sure what your question is. Conv3D was added to TensorFlow two weeks ago, so - perhaps the answer is 'Yes!' :)
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Jeff Dean

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Kickoff for next year's Google Brain Residency program!

We are excited to announce that the Google Brain Residency Program application will re-open this coming Thursday, September 1st! Our first year's program launched in October last year and we’ve received an overwhelmingly positive response. We welcomed our first cohort of 27 Google Brain Residents in June, 2016 and we're excited about the impact they're already making with the research they are conducting!

In conjunction with the application re-opening, I would like to invite you to join me at a YouTube Live event where I will be discussing the Brain Residency Program as well as presenting an overview of some of the research work being done in the Google Brain team (

To attend this event, simply visit and tune in tomorrow (Thursday, September 1st) at 3pm PDT. The event will last about an hour and will be filmed live where you can not only watch but also post questions in real time via chat. We will have moderators online to help answer questions as they roll in during the event.

To learn more about the program, check out Applications for next year's program will officially open on September 1st, 2016 (tomorrow).

If you have any questions, please direct them to

I sincerely hope to see you all there!

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Would you mind making the video public?
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Jeff Dean

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I've been watching this work by my colleague +George Toderici​ and others for a little while now and they've been getting better and better compression rates for images using recurrent neural nets, to the point where they are now significantly better than JPEG compression. Very cool work!
When I joined Stanford's Compression and Classification Group in 1999, it became quickly evident to me that research in signal compression was really at an impasse: it was clear at the time that one would have to move towards more semantic interpretations of images and videos to make any significant gains in bandwidth, and in spite of standards already moving towards enabling these 'higher-level' coding methods, nobody really knew how to go about them.
Fast forward to today, I'm very excited to see deep nets make a significant dent into the problem, while enabling seamless, practical variable-rate coding, bit-per-bit progressive decoding, and with huge gains over JPEG to boot.
Abstract: This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once.
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What it your name, & live
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I and a bunch of others from the Google Brain team are doing a Reddit AMA on /r/MachineLearning to answer people's questions about AI and Machine Learning. The thread is posted now to allow people to post and vote on questions they want to see answered, and we'll be spending several hours this Thursday, 8/11 answering questions, starting at 10 AM Pacific Daylight Time.

(We have 279 comments posted to the thread, and it's the most up-voted post of all time on /r/MachineLearning, and we haven't even started answering questions. We'll have to see how many questions we can get through on Thursday!).
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Another time seeing jeff's message,so happy.

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+Christopher Olah and +Dario Amodei from the Google Brain team, along with their collaborators Jacob Steinhart (Stanford), Paul Christiano (Berkeley), John Schulman (Open AI), and Dan Mané (Google Brain) have put together a very nice paper that discusses some technical problems in building safe AI systems. Rather than much of the rhetoric that we've seen recently about fears around AI systems, this manuscript is nice in that it actually frames things in terms of real technical challenges and some research directions to explore in solving them.

I'm also excited to see this topic being addressed openly, in a collaboration across many different institutions.

Actual paper:
Google Research blog post: 
Open AI blog post:
Abstract: Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended ...
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Jeff Dean

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I gave a keynote talk at GCP NEXT a couple of months ago, and as part of that, I demoed this nice visual explorer that folks at Google built to demonstrate the Cloud Vision API. You get to fly through a point cloud of tens of thousands of images, annotated and clustered automatically by Google's Cloud Vision API. As of today, this demo is now released to the public, so you too can play with it (requires a relatively recent version of Chrome).

You can learn more about the Cloud Vision API, which is now in GA ("General Availability") at

Have fun!
CV Explorer shows you how to combine Cloud Vision API w/dimensionality reduction & cluster analysis so you can build a 3D catalog of an image repository
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+Jeff Dean Thanks, I could understand the classification based on the pixels as features, but I still couldn't image a ML method could output word labels based on numerical(pixels) input.
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Tensor Processing Units (TPUs)
I'm very excited that we can finally discuss this in public. Today at Google I/O +Sundar Pichai revealed the TPU (Tensor Processing Unit), a custom ASIC that Google has designed and built specifically for machine learning applications. We've had TPUs deployed in Google datacenters for more than a year, and they are an order of magnitude faster and more power efficient per operation than other computational solutions for the kinds of models we are deploying to improve our products. This computational speed allows us to use larger, more powerful machine learned models, expressed and seemlessly deployed using TensorFlow ( into our products, and to deliver the excellent results from those models in less time.

TPUs are used on every Google Search to power RankBrain (, they were a key secret ingredient in the recent AlphaGo match against Lee Sedol, they are used for speech and image recognition, and they are powering a growing list of other smart products and features.

+Norm Jouppi and the rest of the team that developed this ASIC did a fabulous job, and it's great to see it discussed in public!

Blog post:

Link to the part of the keynote where Sundar discusses TPUs:

WSJ article:

Edit: Added a link and some text.
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+Jeff Dean - really amazing. i am getting a crash course in machine learning (esp vision) at ... a tiny non-profit that seems to have gotten on the map of all sorts of wildlife conservation groups out there. if you ever want to throw a few cycles of tpu at us helping identify animals, just let me know. :) or, yknow... some engineers, and some algorithmic magic, and and and... the 2.5 of us on staff cant keep up with the 1+ species a week contacting us! :D

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Google Senior Fellow
I build large-scale computer systems.  I joined Google in 1999 and am currently a Google Fellow working in the Systems Infrastructure Group. While at Google, I have designed and implemented large portions of the company's advertising, crawling, indexing and query serving systems, along with various pieces of the distributed computing infrastructure that sits underneath most of Google's products. At various times, I've also worked on improving search quality, statistical machine translation, and various internal software development tools, and I've had significant involvement in the engineering hiring process.

Prior to joining Google, I was at DEC/Compaq's Western Research Laboratory, where I worked on profiling tools, microprocessor architecture, and information retrieval. Prior to graduate school, I worked at the World Health Organization's Global Programme on AIDS, developing software for statistical modeling and forecasting of the HIV/AIDS pandemic.

I earned a B.S. in computer science and economics (summa cum laude) from the University of Minnesota and received a Ph.D. and a M.S. in computer science from the University of Washington. I was elected to the National Academy of Engineering in 2009, which recognized my work on "the science and engineering of large-scale distributed computer systems."

  • University of Washington
    Computer Science
  • University of Minnesota
    Computer Science and Economics
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Jeffrey Dean
Google Senior Fellow
  • Google
    Google Senior Fellow, present
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