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  • so welcome to talk on the future of Java.

  • Script is Aye, aye.

  • My name is quick was broken.

  • Uh, my name is asked him.

  • Hussein, you can find me on Twitter as jaw ache.

  • Not Joe Ash.

  • A jaw ache.

  • Um, I blogged about angular and draw the strips on my website code craft dot TV.

  • I'm also a cloud advocate, Microsoft.

  • So if you don't know who we are, we are three open source advocates working on the azure cloud platform.

  • If you go to our get a link there, you'll find a final.

  • That's basically there's mean that's up there.

  • Before we focus on all different technologies on areas.

  • My focus is Java script and on weather.

  • Actually, I've gone into leadership mouse.

  • My focus is people.

  • But you're gonna find out more about us going to that website on just Ah, yeah, Chase people up.

  • I also work with a charity called Pile Internship, and what they do is they help find Palestinians internships in high tech Israeli startups.

  • I last year I I meant toward about 10 of them really hard work mentoring 10 people by men to 10 of them.

  • And this year I'm very, very proud to say that 17 off the interns all being mentally by one cloud advocate for Microsoft.

  • So if any of you work with or for at, ah, startup or or high tech company in Israel and you have a space on opening for him to give me a shout, I'll connect you with the program director.

  • I'll get the ball rolling.

  • Anybody here?

  • Uh, anybody here know about you, Demi?

  • Anybody hear my students on you, dummy?

  • Well, hi.

  • Thank you.

  • Thank you.

  • Pays the mortgage.

  • Um on You did me like this I get over a year ago now the added in automatic subtitling.

  • So they correctly sites up.

  • Spell my name correctly.

  • My name is awesome on DA This talk should be very asked him.

  • I also co organized a meet up group in London.

  • Aye, aye.

  • Java script at London's I co organized with my friend Ellen.

  • 1/2 off.

  • We started it early last year.

  • It's being running very successful.

  • Think about 1718 100 members now.

  • We run some pretty regular sessions all the way through last year.

  • And then what happened was at the end of these sessions, people would come up to us and they kind of give us a link to some.

  • Hey, look at this Java script.

  • Aye aye, Power Jobs kept up after occasion.

  • Isn't it cool?

  • We thought, You know, it's fantastic.

  • It's really cool.

  • Let's put them all together in a website.

  • Everybody else confined them.

  • That's what we did is we made a website called a Js rocks, and to go to a A.

  • I just rocks.

  • What you'll find is a whole collection off a I powered JavaScript applications.

  • Now that Java script and then the Web, which means you can play around them but also means you can usually view source and see how it's working.

  • But the very least, we'll come up with or have a link to the source code or an article or a video explaining how it was made of meat.

  • The idea is to inspire you with an application, and then, if you like it, you can dig into and learn a little bit more and more.

  • That's that's enough.

  • In my journey into a machine, learning and jobs got bean bean, That process, I recommend.

  • Do not yourself on this talk today what we're gonna do is we're gonna go through three off the applications, which you can find on a IGs, rock them and explained how they work on.

  • Then hopefully through that whole process you're gonna learn throughout the course of today for the course of this lecture talk, you know, learn a little bit more in a little bit more, little bit more about what's possible and achievable with a I machine learning in jobs.

  • Get right now.

  • I know it's been a couple talks already in this conference on these topics.

  • Um, I suppose my talk for a bit more higher level give you a bit more of a broader viewpoint of what's possible.

  • Let's start off with the first application.

  • It's called the Emoji Fire.

  • It's not trademarked.

  • It's just the merger fire.

  • So it made sense.

  • Um, this is actually my application.

  • I brought this application.

  • It's gonna change everything.

  • Yeah.

  • So what's it do given image?

  • It will detect any faces in the image.

  • It will then detect the emotion in the face.

  • It will.

  • Then any guesses it will notify it.

  • That's right.

  • Well detailed.

  • Grab the appropriate emoji stick on the face and post it right back at you.

  • Yeah.

  • Elon Musk has got nothing on me.

  • Yeah, that.

  • Yeah.

  • I am open for investment here.

  • Singapore started hospital.

  • You could use them a different way, so you can use that as a slack applications.

  • In fact, if you go to the modified dot com and click there after slack, but it'll add it to your own slack work spaces.

  • Right now, this that works.

  • So you basically find them a major?

  • This is my son.

  • Yeah.

  • Slash Modify.

  • Put the pasty image in.

  • Yeah, I see appropriate emerge.

  • It can handle multiple people.

  • So you got that one in together that they're, Well, I I are answers An age old question that has been plaguing people for Diego.

  • That's the Mona Lisa.

  • Smile.

  • Bigger.

  • So the motive fire does quite a lot is a whole lot of things that it's doing by saying the most impressive thing that it's doing is it's How the hell is it calculating emotion, Emotion, right?

  • It's kind of kind of complex things.

  • A lot of us can't really tell other people's emotion, but how's the computer figuring that out, right?

  • I've got good news for you is really easy is incredibly easy, setting up two steps, just two steps.

  • First, that detecting the facial features.

  • We know how to do this.

  • Now we've examined enough faces.

  • We know the different points that every single space has.

  • And so basically there's different algorithms you can get.

  • Some of them are very arithmetic and some of them a little bit more.

  • Ah, machine learning Basic essentials made.

  • There's lots of different albums you can use was given image will extract you those spatial features And every single face has won best features Has 70 step one Step two is really, really easy.

  • You just use a neural networks that said just two steps extract the features and then just train and used.

  • And your network lets me for on Germans explain you just just for this guy in the front.

  • I'll explain in your network just for just for him over there and nobody else knows what they are.

  • Now a neural network is has a basis in biology.

  • Okay, this is a new run.

  • You should have at least one of these.

  • Um maybe some of you.

  • I have a few less.

  • Then you did yesterday, but it has some dendrites going in a body, a body on backs on going out.

  • If enough electricity flows in through the dendrite, the body fires.

  • It's electricity out through an accident.

  • That's it.

  • So it does.

  • If you stick enough of these together, you get a brain.

  • I can think, right.

  • That's it that's in your network.

  • That's awful.

  • That's a brain, I should say.

  • Um, if I wanted to code this up in Java scripts, you probably kind of try and give it a go in a long time.

  • So I've kind of studied the computer science basics right now, but I still remember some of it.

  • Quick note.

  • Ah, create some edges.

  • Ah, graph theory.

  • No, I'm talking about EJ is going in and EJ going back out on.

  • Then what?

  • Your features?

  • One of the things you're you're pumping into it would you would you was the inputs.

  • And what's the output?

  • Let's say this neural network is very one.

  • You're on your network.

  • You're gonna pump in the day of the year 23 and maybe the temperature on that day.

  • That's the imports.

  • Then for each of the edges, you basically create random number.

  • You initialize the random number.

  • Math.

  • But round maybe that got initialized minus 0.5 Mary.

  • That's got 2.1.

  • You multiply them together, you add them up when you pump him food on activation function.

  • Okay, whatever that function pumps out, that's electricity going back out.

  • That's how you would simulate that in code, I said, just to see a little bit more about how that might work.

  • Boom.

  • Just this.

  • This is it.

  • Input features and multiply them by the weights, some of them together.

  • Whatever that number is, you passed into the activation function.

  • Whatever that pumps out, you pump out.

  • That's just a neuron.

  • What are you doing when I when I teach, if some of you coming to my workshop let it run at some point, the workshop will just be like it's just multiplication.

  • Yes, just multiplication.

  • Like a large part of it, right?

  • That's what That's what it's all about, right?

  • I'm basically there's loads of activation functions out there.

  • This is a very, very simple one.

  • Anything below zero.

  • Is there anything above zero is one.

  • There's loads of all the ones you can use.

  • Um, basically, you just grab a bunch of those neurons together.

  • You got a network?

  • It was a simple feet forward.

  • Neural network, huh?

  • It's raining.

  • Um, that's what you do.

  • You you connect them altogether.

  • But remember, you initialize all those edges with random numbers, right?

  • Well, then, for this canyon that we need a training set.

  • Let's say we've got a whole bunch of images, but we've already gathered.

  • We've already figured out where all the facial features are in this image.

  • Okay, this data sets out there that they're available with all this stuff, right?

  • Feature.

  • Really Figure out what the facial features are off this image.

  • I'm actually, we also know the emotion in it as well.

  • We pump those features in maybe the features of kind of the X Y coordinates of each pupil and other things.

  • We humped those in.

  • We multiply all across.

  • That gives us a number at the end, right?

  • We we though what does three mean?

  • We've decided, actually that in this neural network, zero maybe is unhappy and 10 is happy, right?

  • Someone has a human being, is looked at image and they said that So we know it's wrong.

  • Of course it's wrong.

  • You initialized a bunch of random numbers and multiply them together.

  • It's not gonna It's not finish right to start it off its course.

  • It's wrong, right?

  • But what we need to do, isn't it just those numbers based off of how wrong it is that we we know?

  • Because we know this data that it's off by five.

  • This is the magic.

  • Use an algorithm, use a technique called back propagation.

  • Okay, given how wrong the network is, how do you tweak those numbers?

  • The next time you run it, it's more right.

  • Okay, that's back propagation.

  • And that's what you do.

  • That's when it tweaks it.

  • Perhaps you keep him running it with your date and you keep on running with data until he gets less wrong.

  • Less wrong restaurant, less wrong until eventually what happens is given a image, and it correctly predicts that is eight.

  • Okay, that process is training a neural network.

  • All this during this tweaking those weights.

  • So the next time you go through, it becomes better and better and better.

  • Okay, That's how you could do it.

  • There are actually well known data sets out there that you can get the freely available, which have ah whole bunch of images 30,000, 30,100 Army loads of images.

  • Each image has bean.

  • Human being is gone through and determine what emotion is.

  • And you can build and training your network in order to detect emotion all so you could do okay.

  • Well, what's that?

  • What's been happening over the last two years?

  • One of the lots of things happen in the space of the last year.

  • What's really been happening in the last two years?

  • Isa commoditization off the service is especially the very, very commonly used models love different companies, Microsoft included, have been making them available via AP eyes.

  • And that's what I used.

  • That's what I use for this service.

  • You should call the face AP how we can get for Maja.

  • Um, I was very, very, very easy to use.

  • You basically just host on image, too, that you are l.

  • And it gives you some data back at the backhand.

  • It's using something exactly like I've showed you.

  • It's been trained in a much, much bigger data set over much, much, much longer.

  • Time to get a whole bunch of information for each image in the face image in the face in the image look, it gives you a face rectangle, the face, actual boots with emotion.

  • A whole lot more is all of the strips, all out.

  • Anger, contempt, disgust, happiness.

  • You just sadness and surprise between zero and worn.

  • Do you see your bed 300 or bad?

  • Yes, yeah, you cannot be hundreds unhappy.

  • I'm really sorry about that.

  • It's actually impossible.

  • I've tried.

  • I can't be 99 percent happy.

  • That's basically I used in the motor fire, right?

  • That's how I built it, that I detected emotion inside There.

  • There's the summarize.

  • No one that works.

  • It's incredibly powerful.

  • They're incredibly powerful, actually.

  • Conceptually, the really simple to understand, I explained, You're really basic one in about five five minutes.

  • Something like that, right?

  • Basic of other coming lesson learned here might