I/O '17 Guide – Machine Learning



hi Timothy Jordan your friendly developer app against year at Google i/o 2017 I'm in the machine learning area and we're going to check out a lot of really cool stuff a reminder before I get to it if you'd like to see any of this footage or any of the other sandbox tours that I've been doing go to geo / io flash guide first off I'd like to check out the cloud TPU in person Wow so much heat sink look like a skyscrapers it's like a miniature city down there seriously one of the best Tron moments I've had in years this is among the may see most amazing things I've ever seen in my life a magnet everybody this is magnet you may know him from well being magnets and all sorts of things hi magnet hi how are you I'm doing super well so you've been hanging out with all this really cool machine learning stuff all day long yeah that's right we've been here all day doing different machine learning stuff I'd like to check out some of it and just play with it let's try a I do let's first so what is it so you can actually play on these things and then the network generates something back yeah okay so it's kind of like the machine playing music with you yeah it's kind of constructing music based on what you play it's trying to create something similar but still different okay I want to try it out that's a lot of fun it feels a little like that scene in close encounters with the Third Kind yeah absolutely all right so there's one other thing that I would like to check out while we're here and that's the candy sorter yes it's an amazing thing it consists of so many different machine learning technologies in one single demo so we check it out yeah for sure as promised we're going to check out the candy sorter what is the candy sorter umask well Dave's here to tell us what that is okay great so what we're going to show here is how you can infuse machine learning into your apps without actually being a data scientist and what we're going to do it is through candy and through labels now we've what we've done is we've gone through we've trained a model up in the cloud that for four basically we've taken an existing model and we trained it for candy so we put labels and next to candies and we've got a little camera here thus taking pictures of the candy and we put labels that haven't been associated with the candy we sent it up to the Google cloud machine learning engine and we've trained the model using Inception v3 model and it using transfer learning and so what we're going to show now is now that the model is in training is actually the serving of the model so I'm going to take candy here and just throw it out in front of the camera and this is just any random candy that we we have trained at this model for so I like I like gamma which is plenty of gum out there and you'll want to make sure there's a little bit of spacing between you know again when we train this this image it has been it has been a well trained model than it's been modified with these images and ink labels you can even write your own labels which is awesome so now it's trained now the fun part of this is actually the serving the prediction so you're going to speak into this mic you're going to hit this little mic button here you're going to speak into this mic and ask for some kind of candy and it's going to make a call to our API it's going to understand the text it's going to do a speech API so speech to text API and then it's going to understand the intent of what you're asking for using our natural language processing API and then it's going to make a prediction based upon the model that's sitting out in the cloud and the best part is hopefully cross your fingers it's going to give you it's going to pick make the prediction pick it and then give it to you awesome okay all right so click on that and speak into the mic may I have some gum so it understood what you said may I have some gum now it's going through natural language processing it's identifying the noun the noun there and gum so now it's trying to it will then match based upon the model that's been modified come on come on come on and it is picking chewy gum up and there the camera identified extra long lasting watermelon gum now the cameras go the and over and there's your gum curate learning in and out so I'm I get to keep this right yeah actually I've got like seven boxes back there please take everything Dave thanks to my eye thank you that's pretty great alright so that is the machine learning area I hope you've enjoyed these experiences as much as I had it's really cool to see machine learning up close and personal see how it can be used in real life through these demos and I hope it inspires you to do some cool stuff with tensorflow and with cloud see you later

24 thoughts on “I/O '17 Guide – Machine Learning”

  1. Machine produces the music in reverse direction of input. for example, i am entering some alphabets which are representing the symbol's of music like — a , b , c, dd so output will — dd, c, b, a

  2. Will the film "candy classification system" who invented, that invention is very interesting, can tell me a little more about this invention?
    請問影片中「糖果分類系統」是誰發明,那個發明很有趣,可以告訴我多一點有關於這種發明嗎?

  3. ok.. to move some candies a foot u gotta spend millions of dollars and ground breaking research !!!

    it make sense tho

  4. wait the TPU thing only has 180 TFLOPS? that's only 1.5x the performance of a SINGLE V100, which Nvidia is selling in pairs of 8 in a 4u server 😀

  5. the learning robot arm is Dobot Magician. Great job! we also have some good visual identity applications on it.

  6. Please remove the shadow on the top of the screen for future videos. It makes me want to touch the screen to make it go away.

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