So by now we have a nice list of factors that we should be able to evaluate. Please look at the depth that's required in a verb like evaluate. You need to make a judgment about these things. So therefore you can present a lot of information but you also need to evaluate and judge which of these factors are really important when you are considering a chemical synthesis process. And in our last one we look at some of these broader issues around environmental, social and economic issues. And in order to do that I thought we'd set this last one in the context of sulfuric acid production. So our final case study is around the production of sulfuric acid. Now sulfuric acid is a really important substance. It's one of those substances that we've used a number of times as a catalyst as it is a really good dehydrating agent. So if we think about things like our esterification process, if we also think about the conversion between ethanol to ethane or ethane to ethanol, these both can use sulfuric acid as a catalyst for the reactions. And so and beyond that of course sulfuric acid has a huge range of applications. The process for producing sulfuric acid in an industrial scale or on an industrial scale is quite complex and has a number of different steps associated with it. The first thing we need to do is we need to extract sulfur from the ground. Usually that's tied up in deposits of metallic ores or it can be found underground and removed in a process known as the fresh process. I'm not going to go into detail on that. There is a bit of information. If you want to have a look at it, it's quite an interesting little process of how we get something like sulfur up from underground and there's a few really important environmental impacts associated with that. Not the least of which is that the sulfur will naturally undergo some of these reactions as it oxidizes in the presence of oxygen. And so we get a few of these interesting little things that are contributors to acid rain in the atmosphere as equally as they are a desired product of sulfuric acid production in an industrial context. The next part is called the contact process and the main reason it's called the contact process is because we use a catalyst in order for the reactants to be in contact with the catalyst to speed up the rate of the reaction and also to control that reaction. Because this is an equilibrium, all of the things that we've previously talked about in terms of temperature and pressure and concentration all affect our yield and also are part of our reaction conditions that we can discuss in terms of sulfuric acid production. Once again, you would need a little more background if you wanted to go into this in detail, but even just being given an equation like this should tell you something about the effect of temperature on a delta H negative. So therefore exothermic process, the effect of pressure where we have two and two plus one is three moles of gases and also how we may or may not be able to remove these. The third step is to turn the sulfur trioxide, the SO3, into sulfuric acid. Now this can be a very dangerous process and so often there's a step in between the conversion of sulfur trioxide and the production of sulfuric acid and that produces another little product called oleum.


I just wanted to ask a generic question. Proton mail is awesome. Thank you. Do you believe Bitcoin can survive government intervention and or regulation like Google's, like let's say, the big countries inside? I have never seen global collaboration between countries on regulating anything.   So maybe if we let it happen, it can cause them to have a currency problem before it causes us to have a currency problem. And then we win. And I'm not joking about that, because I'm pretty sure that would be the kind of calculation that you would see in a lot of countries that are thinking about whether Bitcoin should be regulated. And so they regulate it. So what? What are they going to do? Threaten people with death if they use Bitcoin? They already do that in some countries. And guess what people will do? They'll use Bitcoin, and then when the policeman comes, they'll bribe the policeman in Bitcoin. And up the chain it goes until the head of the state is stuffing a wallet full of Bitcoin. The bottom line is that in countries where the rule of law matters, money is a form of speech, freedom of association, freedom of expression, freedom of speech protects political expression through currency. In the United States, we put that into a United States Supreme Court decision on the Citizens United that said you can't regulate campaign contributions because money is speech. So in countries where the rule of law matters, you pick that fight, head on with Bitcoin, and we're going to take it all the way to the Supreme Court. Do you know how many lawyers per day Mark Andreessen can hire? And the problem is that they may lose. And losing on a decision that says Bitcoin is protected speech is much worse than the status quo, which is kind of gray. So that's not going to happen.  Bitcoin is evil, really. You say it's evil. I'd better look into this, because every time you say something is evil, it's because it's good for me and bad for you, and I don't like you. This is happening in Venezuela right now.  Don't pay attention to our 550% inflation rate. Please leave your money in the currency of choice and go with us to the bottom as hostages of our insane policies. And what are some obviously technically literate, a very tiny percentage of the population in Venezuela are probably now thinking, really, I should look into this.


This quick tip came to me when I had stopped during a bike ride to take a photo with my phone. I opened up the camera app and somehow, instead of clicking on the shutter button, I slid my finger from the shutter button to the right towards where the front rear camera button is on my iPhone. The app immediately started a video recording. If you release your finger from the rear front camera or from the right there, it will continue to record until you click the shutter button again to stop it.  He says, I like this much better as it is a one step process to record a video rather than having to select video and then press the shutter button. That's how you lock a video. The way that you record a quick video is just hold down on the camera shutter button while you are recording.


Hi everyone, Joe for Jaspy's casebreaks.com coming at you with a cereal box of 2020 2021 mosaic basketball Number 55 two of those big immaculate random number block spots being given away That's at the end of this break the first dice roll B for the box itself big. Thanks to this group for making it happen Next one's already loaded up and big things this group here as well the group of teams Let's roll it randomize it names teams two and a five seven times each one two three four five six and seven two and a five seven times for the teams one two three four five six and seven and final time What's my favorite cereal my towers ass? That's a good question. Um, and here's the first half of the list right here the second half of this right here well if I'm going with a favorite like Super sugary kid cereal I'm a big fan of the of the frosted flakes They're great Cinnamon toast crunch is good. You got to eat those fast though Once the ones they get once once they sit in the milk too long they get soggy unless you like that But I don't you get soggy a little too fast and then it's just garbage after that I do like a cocoa Krispies as well. There's the final print out Right Kellogg's brand Tony the tiger frosted flakes that those are not the off-brand stuff my tower I don't do off-brand breakfast cereals nowadays, it's It's Cheerios nice Lamello ball national pride card here excellent And then that parallels exclusive to this set to Charlotte Stephen Pete with the Hornets All right, so nothing to the little mellow is pretty nice nothing too crazy there though. Let's uh Let's gather everybody's names here and It's just the top two there me a lot of sad faces here top two after five and a five ten times one two three four five six seven eight nine and 10th and final time Five and a five ten the hard way a lot of sad faces, but I appreciate you trying. There is another opportunity if you miss out And Daniel very close. No cigar unless your name's on top. You've seen that happen before But for now three through 30 Thank you for giving this shot. We'll get him next time top two happy times for there you go Daniels in and Huang is in as well nice There you go, so congrats to the both of you We'll see you in that random number block randomizer. Maybe later tonight. Jasper's case race calm. Bye. Bye


Many thanks to the gardeners in this literary tradition. Jack, Byron, Judy, Sarah, Catherine, Aggy too, throw her in the mix. People who have inspired me over the years. The poem I'm about to read is going to appear in my first book coming out in a few weeks called Falling into Flowers. But the poem has a little history of its own. It first was published in a card and married to this iconic photo by Dan Nicoletta, the 20th anniversary of Harvey Milk's memorial. And I'm going to just tell you some exciting news as a preface to me reading the poem. I spoke with Dan and he told me that I could tell you that he has his first book of iconic photographs of San Francisco coming out in the spring. It's going to be called LGBT San Francisco. And his distributor says, Dan started his career as a young photographer working at Harvey Milk's camera store in the heart of the LGBT Mecca and has religiously documented the journey of the LGBT civil rights movement over the last 40 years. The photographs are powerful, they're intimate, and they totally capture this groundbreaking time. This is the first book devoted to Dan's incredible body of work. Yay Dan, look for it. My poem is called Postcard from the Castro and it has an epigraph from Harvey Milk. Two days after I was elected, I got a phone call and the voice was quite young. It was from Altoona, Pennsylvania. And the person said, thanks. If a bullet should enter my brain, let that bullet destroy every closet door. You've got to give them hope, Harvey Milk. And I wrote this poem after a mural was placed on his old, above his old Castro camera store. Harvey Milk is smiling out a window in the Castro again in a new portrait on the side of his old building. Half circle smile, head bowed, looking out through a sliver of sun onto the sidewalk of his old neighborhood. He seems to know something that we don't. Or is his smile just the start of a happy laugh remembering that this gay mecca is named Eureka Valley? Maybe he's simply admiring the beautiful boys holding hands with such nonchalance as they sashay past him, heading toward, does your mother know, on 18th? He has the radiance of someone who sees through hate. From this window on a wall, rainbow flag across his chest, rainbows fluttering from lampposts at every corner. The van Gogh, golds and blues of the mural hang like a load star above his once open to all refuge.  This hippie community center where I once sat in his old dentist chair, installed like a work of beat art in the middle of his shop floor. I knew him when I did not know who I was. Looking back on the long journey from then to now, his holy disarming comedy silenced by a deadly serious fear spawned loathing.  I have come here on a pilgrimage from the hate. I'm alive. I'm in love. I'm in hope. I'm out. His image shines like a moon over Castro Street again, but I cannot meet the gaze of his earth cast eyes. If I could, I would say thanks Harvey, having a wonderful time. Wish you were here.


Thank you. Thank you. Welcome again to this session. Thank you for making space in your lunch schedule for the locals It should be fairly early, but for the others. Maybe I'll be starting to feel hungry So my name is Gabriele Bunker again I work at MathWorks the makers of the MATLA programming language. I've been there for about almost 12 years I have a worldwide role as a product manager for our DSP and audio processing products And this session is going to be about that it's going to be about the application of deep learning techniques to signal processing and audio processing problems or audio speech and acoustics and I mean if you if you how many of you have an experience of signal processing of some sort in the past or something to do with audio Kind of a rough show of hands Okay, no many but if you have it and if you're in my position over the past years You see you must have seen a bit kind of a big trends where? Traditional techniques have been replaced by a lot of new techniques based on machine learning and deep learning Starting from the most popular example like the voice assistants To something that we try to see we tend to see less such as things like mood classification systems in in cold centers This bigger verifications bigger density verifications in your phone banking Systems and for example for public services radio Microphones to complement the actions of vision and radio system in in self-driving cars and and a lot more you know if you look at simple things like Software for for for computer audio, you know the typical things that I use to produce content the content on TV cinema Audio you find that even that community that used to be pre anchored on traditional signal processing method Not only has been transitioning to using new methods based on deep learning But also make a big deal of it in their technical marketing content You know they make statements where they call out deep learning techniques to make themselves visible and to differentiate a bit because You know that that that's also a market. It's very competitive So these are some examples audio now mix over on the top left They have software that they're easily allows you to extract voice from mixed premixed content They're based in France a cruise on Agapraising Greece and another example They build audio restoration software to take away noise or reverberation again from from this content And this is I so dope a much bigger company not still not so keen you know Verlach companies, but they have a several Several several products that do tough things like take away the the friction noise of microphones like these From recording these are all things that were not possible a long time ago Even if you look five ten years ago, or if they were possible the quality was very low And this new techniques have completely revolutionized the way these things work these days So there's a there's a fundamental question like like us to answer during the session and it's something like this What does it take to develop an effective real world machine learning system for audio and speech application? And when I say real world, I mean something that works for real something that you can sell okay, and So I want to do it by By using the example that's part closer to to our day life because you know Even even the ones that are more traditional in the way they use electronics have started to use voice to communicate Maybe it's your phone maybe two other devices, right? And so and I want to be specific I want to use the example of trigger word detection, which is a pretty important component in those systems And let's let's agree on what that is. I just recorded myself here So trigger word detections are those algorithms that allow Phones you know every voice-enabled device to continuously listen for anything that happens around all the time 24-7 and To wake up the system when they to hit a keyword or a key phrase Okay, there are kind of I like to think about those as the embedded gateway to your cloud-based voice assistance because they Have to take place on the device Without that your phone cannot listen continuously stream audio to the cloud That also means that everybody who wants to develop a voice interface needs to build their own Well, you can leverage Alexa if you want if you build your own device But this you need to implement so either you buy it and they tend to be very expensive or you develop it in the house So what what what best to investigate how something works and how difficult they try to make it So what you're seeing right now is it's a prototype of a trigger word detector Okay, and it's built entirely my love Trying to wake up when it hears the word yes I'd like my system to wake up whenever someone says yes Obviously not when they speak any other random word. That's not yes So this is now working as a prototype It's it's an audio plug-in running inside a kind of a very very standard audio recording and Editing software as a plug-in And so what I'd like to do during the session is to really show you how you put together something like that from the grounds up Okay, and and so if you ask that question about this application What could be the answer if you asked me that question? Well, rest assured that plus if you don't ask me but you ask the majority of people that are involved with deep learning You'll get this as an answer. There's a deep new owner. That's the first thing you might get even on luck here And get this kind of response back which perhaps might make sense to some of you to to to you know show hands Who is able to take some useful information from this second type of answer? Well, that's good. If you have some experiences make some sense. Otherwise just some blurb. So this is this is fair It's definitely not wrong But what I'd like you to take away in the next half hour So is that there's a lot more to it? Okay, if you asked me I would probably I mentioned at least this a lot of data a good dose of signal processing expertise and tools build around speech And all the application and you might wonder why so right if it's the neural network what you need to do What do you know those things? Well, let's find out and at the end of the talk Let's reevaluate what's more important in the balance and what counts and where you can really make the difference in these application so This is really Important to me because what where lies the important is it really has an influence into Understanding where the investment go where the roles go should you hire a signal processing person or a computer scientist and also You know, there's a lot of people complain about if things are changing my work perhaps is losing relevance So let's find out if you this is taken from an independent research that that I came across a few months ago And it's describing the amount of investment, you know in terms of time and hours That goes into developing the actual models So that the networks like finding out about possible architecture training and so forth and working on the data to train that network And if you look at research somebody doing a PhD, this is the picture very little effort on data Everybody uses the same data and all goes in exploring the architecture of the system But if you go to the industry and I'm not just referring to audio and speech here But in general people developing product Sold as powered by AI with deep learning inside you will find out that a lot of investment there are Driven on data and that's the requirements for shipping something that works, okay So what does that mean that I'll represent that it using another picture as that that we that we tend to use So if you think about the development of a deep learning system as a workflow There's definitely a stage in that workflow where you need to design the network and train it Okay, but there's a lot happening earlier and after so the first thing is to think after what do I do with that? I need to implement it somewhere for the kind of problem that we are having in hand here I need to make it run on a phone for example, but then it's it's it's how do I train it? And there's a lot going on inside. Are you so here to Schematize there's a first stage where the data that needs that it's used to train the network needs to be processed in some Way to make it digestible Understandable by the network and there's a lot to know about a collecting the data and notating it augmenting it and so forth So I'll try to cover I you know here and there Diverse stages of this process So let's see. This is this is Jordan which I'll cover that so I'll start You know by taking the elephant of the room out of the equation Just discuss what you need to do about designing the network choosing what network to to use and and how to go about it And then I'll I'll talk a bit about the data that you need to start with and in particular annotation or labeling I'll talk about synthesizing or augmenting data and about extraction feature for different data. Okay, and then we'll wrap up Right, so let's start from the basics How do you? What's the idea about training a network go? Most of you might know but let's recap it A network is all kind of a deep learning system or a new on that Who is a system that just like any type of machine learning learns from data and let's produce an An answer from data, so it's fed with some input some expected outputs and after you know Adjusting its behavior based on the accuracy or not yours its response It's optimized in order to then produce an Optimized model a train model that can then once new data is available Predict the right kind of output from that data that it hasn't seen before the first phase We call it training the second we might call it prediction of inference, you know, they're pretty much equivalent terms So in when you When you think about networks One type of ways to think about them these days especially it's about thinking about layers So this I'll use very few snaps of code during the presentation It will be my lab code in this particular case. You can find the network that I use in that system that you saw it Don't don't lose time reading about the details But take away that it's a vector of layers, right? And if you know what layers you you need if you know how to how to call them in my lab you can go ahead and and look it up and form a vector, okay, and Why why layers well kind of that's a way of representing these connections of neurons They originally they were biologically inspired and a convenient way of Building networks was I was about thinking about stacks of these neurons and connect them together And by doing that you come up with a number of parameters that you need to optimized Using training to have the right answer answer given an input, okay? These days layers might appear in literature in different ways because the way they work the math behind them evolved it evolved you know the way you optimize them evolved and No, you know this is this various ways of representing same things like this slides represents a typical ways of representing convolutional neural networks For example, and this other it's a kind of it's a common way of representing LSTM is a long-short-term memory layers, which are the ones that were used in the example that are That we use these are sometimes, you know, this belongs to a larger class of recursive neural networks, okay? So let's let's let's get back to our example. How do I choose what kind of layers to use? Well, I think chances are if you're developing a product that's that's a that's a problem That's been discussed and examined in literature. This is just a snap of example taken from from Google I'm not endorsing any of these in particular But it's just to give you give the idea if you if you were after an example Especially even in this area and computer vision not not even to mention the literature abounds with with with example of networks That could work well in that case So the general consensus is just grab the first that you got on your eyes provided You're able to implement it and then you've got margins for improving it in all terms, okay? so what you can see here in this case is speech applications and a lot of mentions to LSTM layers between three and four layers the kind of things that we're using right so the kind of thing that we had there Obviously, you know using language right away when you're new to the problem Maybe a bit nasty. You don't know what those functions are. You don't know how to find them So and other approaches in general There are apps or their drug and drops that may be useful as an entry level where you just have a look at catalog of Layers available in that framework you drug and drop them into a canvas connect them and have your model, okay? Similarly when you design the network, it's important to see once you've designed it How many parameters are there that you'll need to optimize when you do training? How complex is your model the more complex the model the more data you will need and you see that in this case You know, maybe people when you deep learning tend to see those couple of years of LSTM and think oh come on This is not deep back in the 80s when I first started in your network We had networks with two internal layers one in this case Look at the the thousands of weights that you're you're bound to have to to optimize in this case so Now so you had your network Optimizing these days means definitely not writing in the equations for grading the sense and finding yourself But it's about using a framework whatever it is In this whatever framework will give you a way to specify in the network or to specify some parameters You want to use to adjust the way the network trains or optimizes the value of the weights And then once you have the network design and the training options You just go ahead and call a comment like this one train network that takes the architecture the specifications inputs and expected outputs and goes ahead probably using some some Hardware like a GPU and optimizes the network and and and you'll see something like that This is a common type of representation of the level optimization of the system that represents the accuracy not over time But over over training cycle Okay, and what you're definitely happy when those lines goes up into the right because it it it means that you Coming close to a hundred percent accuracy that means that the network predicts the right responses for every input And at the end you've got your train network. Okay, like say look at this example This is telling us that by using a single GPU for training It took five minutes to train the system and we kind of got an accuracy of 90 percent It means nine times over ten the network gave the right the right answer for this problem And this is right the plot that you would see if you train that that network that I'm presenting here Right, so validation accuracy. Let me use this as an important point to segue Two lines here a blue and a dash dot the line Okay, these are telling us two different bits of information the blue Tell us the accuracy on the data that we're actually using to optimize The weights of the network. Okay, that is data that is going actually into the computations There's not a bit of data that's not going on the computation But it's it's always kept handy as a means of checking It's data that's not used to optimize the weights But it's used to check how the weights are doing that's data that the network has never seen before It's not useful training and so that tells us Usually how the network behaves for a real problem most likely that validation data will be data That's very close to the data that the final system will be seeing okay data That's realistic that takes into account the defects of any microphone that we'll be using the acoustic responses of the room That's the data we've done that's telling us something how are how is our network behaving for a final problem that we're giving it Okay, what's the training set is something else? So let's use this distinction. Let's keep it in mind because they will accompany us through quite a few parts of this talk And let's move on and use this as a segue to to think about how you you go about we go about annotating data Because we we talk about data and labels, but you know data Aren't born with labels either you take data with labels from somebody else Or you'll need to record data and put a label onto it And so labeling data these days has a different meaning to me for training and and validation data And so I want to just give you some some bits of information is due to like you think about it Back in the days I mean if you're in between your 30 and 40s now, and you were in school in your 20s If you study any deep learning in school, then you will be told the following you've got this much data Okay, that's all you got and you to train a system with that a machine learning system So making sure that you chop your data in three parts The largest part you use it for training We said to actually optimize the weights of the network and then you got other two parts The second part you'll use for validation the one that we've seen the black line. Okay, and then there will be another part used to test Why another part if the validation was already telling us that the network if not what how the network was behaving well, because if the network is not behaving well We're likely to change something in the network like some options in the training for example or some other parameters So although that data doesn't come directly into the optimization We're using it to inform us into choices So we are likely to adjust the parameters so the network believes well on the data So once we're happy at the stage where we need to Verification the network was well on data that it really hasn't had any impact in in in how it's behaving And that's the role of the test data right and at the time so these two used during training this afterwards at the time You know 60% 20% 20% 70 15 15. This was the kind of ballpark Okay, but they you know, this is largely a recipe of kind of a textbook recipe And that was but work well when you know the size of the data set where we think kilobytes and megabytes The traditional size but now with modern systems so many weights and so much data needed to train them the picture is changing Data is exploding in amount But if you shrink it down to the same proportion you find out that the training part of the data will be over 90% will be 98% typically something like that or close 95 and you just a tiny bit of the data for validation and training Hold on. I said tiny in proportion This will be probably a lot more than you used to have in your old machine learning problem But in comparison with with with a whole budget of data. This will be tiny. Okay, so What does that mean is that the kind of things that you can do on your data actively in terms of investment? Very a lot. It's very difficult to intervene on the training data because that's huge who you are Where are the resources to go and work and correct the training data? But you have a lot more margin to work and correct the validation data. Okay, so let's start from it Let's give you some examples in terms of what you can do So what's a good type of of? Validation data with some labels for our problem. Remember our problem is to wake up whenever there's word. Yes So this is an example Let me play it first. It's a recording first You said yes, then no, then you said yes, then no, then yes, then no driving me crazy All right, I like the signal because there is there is a voice there's a speech there's recording But there's a lot more there's some washing machine noise in the background. There's a little reverberation Let's assume that this is a realistic Recorded signal it's something good. It's representative of the actual working environment of my future product so I like that the other thing that I like is that kind of The kind of mask let's call it that highlights only those the errors of the recording where I have the word Yes, and I feed that the red as a label to the network because I need the network to wake up whenever there's a transition from From zero to one. Okay, and and and let's listen just to those portions to make sure that this is well labeled Yes, yes, yes Okay, so no extra no less it's kind of sounds sounds about right how do we achieve this? Okay, so this to me is a good validation data sample Because it's it's got a realistic recording and it's also actually labeled so realistic recording It's okay. You are developing the system. You might have a prototype, you know what microphones you're using You can use it and go where you need to record the data raw data is easy to record. What about labeling? Okay, so various approaches How to label well in general is use an intelligent system trained to carry out a similar task with proven accuracy, right? You are developing a system doesn't mean that it doesn't exist a system that does it better. Maybe it's a cloud system Or maybe it's a human, you know, that's the first choice That's still how many people label data the people who sell you the data most most times have a warehouses of work You know just remote workers that label the data. How do they you work? Well, they use apps that are made to label data This is just an example of an app that's recently been Shipped in my lab. It's called audio labeler Testing a prototype of a wake-up word detector. So you're gonna wake up when it hears the word. Yes Okay, encounter. Yes, stop Go back zoom in and making sure that you label it as something that you're looking for it Can be yes can be truly can be up but you select only the error where this yes You validate it you click down into the label area type whatever you want that corresponds to that Adjust the the section and then you move on right? So that's the first way to do that. It's kind of It's it tends to be honors It's important to have it because sometimes you need to go in and correct the labels But it's it's it's a bit, you know You can't bet on really delivering a full label data set with with a with a manual approach So then the second option is to use an automated system like an intelligent system So a pre-trained machine learning model. What does that look like? So for example, you know for it for speech This this various services available out there like Google speech used in this case from that same app But they're similar from Amazon IBM You name it and and so the ability to just call out to a system like that That's not exactly labeling data according to your needs in this case It's it's labeling whatever all the words, but it's giving you the time intervals for all the words So you can label all your collections your files just by sending them to the way and getting them back You can see that every word is is highlighted in isolation and then because you have some programming experience It doesn't things don't need to be exactly like you need them You can post-process it by writing some code in this case those labels in terms of the value and the time intervals can be exported into my app So you can just you have them here represents as a table So that there you go, you can select only the rows where there's yes Having only the intervals where there's yes and and wherever you encounter. Yes you know start from zeros where I'm gonna yes put a one and Pretty easily using a combination of intelligent services and a language that you can control you can get to a situation like this where Yes, yes, yes, yes. Oh, yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes Yes Yes, I know I didn't completely plan that this will be so funny once once once played back in public But it's the idea is that we understood, you know playing it back only selected error It's a just a good way to check that this is well-validated. Okay, so this is the general the general rules Okay, this is this is a good way to to go about labeling Validation data what to say about training. Well, you know for training unless normally you you tend to accumulate Data sets multiple data sets that are already labeled So the idea is usually still to use a programmatic approach to use the information that you have But revert it into the forum that you that you need I want you know We had just limited bandwidth in terms of time I won't go too deep but I use training just as again as a segue Because the problem with training data is not only that the labels need to be accurate But training data inherently won't be specific to your problem even in the way it's being recorded Okay, so There's a new problem about training Which is that the data is different from the one that your system will digest in the end. Okay, it will be data typically Let me go directly to the slide record it at first. You said yes and no then you said yes at a desk, right?  Microphones at what point in time by what person in what mood you don't know you have this data You need to make some use of it and let's assume that the labels you're in a good place That's labeled data and you're able to convert them into the format that you need like in this case a mask over time How do you make sure that you can make your training data a little bit more similar to your validation? So when you train your network, you just you know Randomly expected that magically your network behaves well on your validation data, but you can help it Well, the only way to do it is to Take your data and try to bring it closer to the validation data make it more real Okay, this is an example of more realistic Okay, the kind of thing we had before so reverberation and noise background take all the variations that you That you want. How do you do that? Well, some people call this synthesis some others call it Augmentation a log augmentation because synthesis disposes you don't have anything and then you come up with your invent stuff Augmentation is more suitable to audio speech and acoustics because you normally you want to start with something You don't just synthesize data at random, but then you have a way to augment it So let's take a look at the possible things you can do You've got One if you've got this two Three okay, this recorded signal, you know a very muffled very fast reach but also very dry in reverberation and a lot of Electrical noise so one of the things you can do well at for example one Two odd reverberation. How do you add reverberation? Well, you can measure the behavior of acoustical systems and you can combine them together. You can have many One you can record noise. That's relevant to your situations of use And if you want to avoid applying these effects on background noise that's specific to that recording So for example reverberating the electrical noise will be a pretty nasty thing to do because it wouldn't happen in reality Well, you can use one clean up algorithms to either, you know that you read yourself available in the MATLAB tool boxes Or any other system or you can use external API MATLAB these days for example you can consume easily audio plugins and You know one of those guys at the beginning that did Audio restoration tools, I was able to easily use their plugins to clean up the audio in preparation for processing like reverberation That's the other things that I've done specifically for speech often. So for example, yes This is the original yes, and this is you can do time stretching that means the same pitch taking more or less time To play it's a bit tricky to implement, but it's a common standard effect You know, it's it's made to account the fact when people speak faster or slower Okay, starting from a single sample similar thing pitch shifting. Yes Okay, accounting where people are just more relaxed or or or more depressed or happier or in a hurry or the opposite Okay, things like time stretching sound doesn't doesn't change But it allows you to make the network more responsible to different positions based on how it scans the signal It's clear that There's some domain expertise required, you know things like pitch shifting and you can do it in different ways You know, there's a brutal way of doing pitch shifting and the non-bruel way, you know, this is the original yes again Yes, this is a pretty good way. Yes. This is a bad way. So the last two Yes, yes, okay same pitch strictly the same pitch But in some way in the first way to the right is the same person talking with lower pitch With the same shape of the vocal tract in the other case It's the equivalent of a of a giant replica of the same version with a big vocal tract So that the that is trivial. That's not trivial Okay, so you need to be an inch to do things the right way or you're just messing about and train your network on rubbish stuff okay, so We covered that let's take a look at the last topic which is creating it but for the networks There's a there's some misconceptions around deep learning, which is you have a network and you just need to feed it raw data Because it will be so intelligent and so self-sufficient They will make sense of your data regardless of how many samples per second it is how complex and how abstract, right? That's a misconception that what you call feeding raw data to a network has a name and it's called end-to-end learning and It wouldn't be right to say that it's nothing to do with deep learning It's got something to do with it, but it's not deep learning historically deep learning in some with some types of signal has been able to produce End-to-end learning it doesn't happen so often with with audience speech the only example that I've got in Mind that works well doing end-to-end learning is Google's wave net no more. That's not what people do Okay, everybody regardless of the type of layers that the network is made of will do some kind of first pre-processing of the data So that means that you have various near a large data sets composed of data samples and labels For convolutional networks typically because they are there they were born in first place to work with images You transfer you transform the the the audio or the signal into some form of spectrums That means frequency over time that will be slices with frequency transform This is also called short time free transform and you'll feel that to the network That's a possible way of of pre-processing the data The other way is that there are Specific features there are some that are very useful speech or other domains That basically takes a measurement of estimation of the data that we know makes sense Typically, there's an invariance problem behind extracting features in that you want to have the same features When you apply some modification on the data that don't change its it's some substance So there's a lot of literature literature on those so that's that's the way to go And and again deep learning doesn't mean end to end learning so I For the few who have some some single-person experience this will make some sense or go quick I'll just give you an example of typical things that are used and in this particular example They represent different stages of the say of one end goal, but you can feed any of these stages So for example Starting from the the initial signals over a time the first thing you do is to you buffered or window it You slice it and then you do the same thing on all the slices and you stack it together the first thing you can do It's a spectrogram or or a simple a short time for a transform. Those are FFTs Start one next to the other Another kind of more refined step that you can take is to wait that FFT the FFTs is is an artificial construction It doesn't reflect how the human ear sounds and how voice have evolved to be well perceived by the ear So there's this perceptually accurate versions of spectrogram that that take various names For example a male spectrogram these are perceptually scales trying for his representation and again from this another very useful thing that's computer this is this physical MFCC male frequency capsule coefficients These I just meant to put this light so there were some some some key words that might resonate with with some of you right so Now remember this slide at the beginning. We're talking about the two phases training and and and infer it's not prediction How do things look like when you put them all together? How do you do training? So you will have a collection of recordings with the right labels Okay, and we've just seen that we use typically some kind of feature extraction to transform or Reduce the amount of information in some way that has the effect Let's let's talk bluntly to Allowing you to use you know less heavy networks. So you don't need So they're trained in a reasonably In a reasonable amount of time or resources right even Google will Advise against using that wave net for any kind of practical problem because the training times are so completely out of scale To compare to the usage time it takes a day to train it for one minute It's ridiculous. So not even mention embedded system, right? so Then then you use the features along with the labels to train the network Okay You'll feed the features and and the features over time to the network and then you that's how the network will be will be Optimized because we saw that almost always we also need to Pay augmentation that means that the feature will be expected on the augmented data, right? So what where you end up there may be larger or smaller than the original data depending on on what you do But this is the general workflow okay for training So there's a lot going on things look a lot easier for inference, right? Once you you don't need to augment the network is already trained I forgot to date the cars those are meant to be field weights adopted And the idea is that you just take the input signal you extract the features out of it You feed it to the network and you expect to find back at the end the right type of inference, okay? When you put it into code this looks quite simple. So there's a this is a sample it's like code So there's obviously more code here But you can see the bit of code to extract MFTCs and repartition them in a way that digestible One line of code to have a network that's pre-trained and let it have predict on output signal And then in this case some some you know some logic to insert The trigger sound stuff like that is I was saying that this is a lot more There's this you know, there's parameters in terms of how you accumulate the data what latency etc But you know, that's not going to the details there And so so by doing that I just I think I covered all these aspects There's the buttons contact, which is how do we how do we end up building that kind of application that demo that you saw the beginning and that was That was done in this way. So well We did cover all of this pretty much at least not all not all but but you get good sampling with this Documentation labeling transformation physical extraction etc. But what about the last slice there? How do you take a bit of code that you used as an investigation process? The research and development and and change it into a product or at least a prototype that can be tested in real world And so this is just a kind of a few slides just to take with a curiosity for for the demo you so If you start it with that piece of code Imagine that that and all that sits around saved to to a file to a mutlify in this case This is one of the many ways that MATLAB can transform a piece of MATLAB code into COC plus plus There are other avenues as well in this particular way It already allows you to take that code and transform it into a plug-in So what was running underneath that plug-in worse was compiled C++ including all the network So something like that. This was this was the the source C++ in a juice project in case this you're familiar with that and that That basically was what was running in there So so it was running pretty pretty well in real time not too many issues and these networks can also be scaled very easily Instead of having 150 Units per layer get at 50 and it worked just about fine. Okay, so What's the the answer to that question? It's summarized form. What do I need to develop such a system? Obviously a simple and proven deep network recipe is One part of the answer which was the equivalent of the deep of the deep network. That's fine But I think that the bulk of the answer to me was you need a lot of awareness and expertise In terms of how things work, which means that all the competencies is the skills the tools the expertise That signal processing engineers in this case have accumulated over years are still valuable And they're still valuable even to train deep networks And this is what happens in in in companies that do these things for real not just for research But everything that you find in your in your in your phone You don't even know who's developing it Because I'm lucky your full manufacturer don't want you to know but what they will be doing is this kind of things Okay, and the bottom line to me is deep learning system can only be as good as the data you should train them There's no way to get away from it Right and that should be the end for me


Good morning, Radiant Church. It's so good to be here.  And I want to tell you what God did in my life almost 14 years ago, which I'm still stunned about. Because 14 years ago, I was a gay man living in Hollywood. And I was an atheist. And then I had this very, very unexpected encounter with God and my entire life changed in a split second. But let's go back to the beginning. And then after I talk about this, I want to address some kind of core questions surrounding this issue of LGBTQ, et cetera. But back when I was very young, fifth or sixth grade, I started to realize that I was attracted to the same sex, which is a very disorienting thing to happen. And back in the day, back in the 80s, it was very, very much. I grew up in Dallas, Texas. I was the youngest of eight kids in a very large family. And according to my family, according to the culture, according to my classmates, homosexuality was very, very, very taboo, very forbidden. So it was a strange experience to have this feeling. And so I had to keep this kind of deep, dark secret inside. But on the outside, I was very social. I was very popular in school. And I dated girls in seventh and eighth grade. I went study with girls in high school. I went to an all-boys high school, but I had three serious girlfriends in high school, all the while having this kind of internal thing going on where I was attracted to the same sex. So it was kind of this schizophrenia. It's like you have to live these two different lives. And things really changed for me in high school. I went to an all-boys high school, as I said. I ended up meeting someone when I was a junior and he was a sophomore who was going through the same thing I was. And one night, we came out to each other. And then the flood gates just opened. Because once that happened, we started exploring gay culture in Dallas.  I was 15. He was 14. I'm not sure how we got into these bars. But we were very much welcomed in to these bars.  And I was doing super well in school with my grades. So my parents were kind of oblivious to what was going on. Plus, I was the youngest of eight. So they were just kind of like tired by the time they got to me. But then I remember we went to this club one night. And it was called the Stark Club. And it was designed by the famous French designer, Philippe Stark. And it was a really beautiful club. And it was partly owned by Stevie Nicks, Grace Jones. And I remember walking into the Stark Club. And it was a whole mix of people. It was straight people, gay people, drag queens, and transsexual people. It was just like this whole kind of mix of misfits, of artists and misfits. And I remember walking in with my best friend. And I was just like, wow, these are my people. These people get who I am or what I'm going through. And I just started going to the Stark Club every night, every weekend night. And that was a big turning point for me. Because I felt very alienated from kind of my family and from God, because I felt very different from them. And the same thing happened. I went away to college. And I ended up becoming best friends with someone who was going through the same thing. We came out to each other. It was a very dramatic moment.  I just thought, oh, this is what I'm feeling now. And eventually, after college, I'll get married to a woman, have a family. And so it was never my identity, really, until after college in Tokyo, of all places. It became my full-blown identity. And the way that happened was after college, I'd gotten accepted into law school and dental school. Don't ask why, but there was really weird things. But in my best friend in college, he was going through the same thing. He was going to go to law school. But we were like, let's just move to Tokyo for a year and figure out what we really want to do with our lives. Like, that's a place to go to figure that out. So we moved to Tokyo. And we had an amazing time there. I lived there for a year. And while I kind of like eight months into us being there, his friend from Texas came to visit us. This guy, we'll call him Adam. And Adam stayed with us for five days in our tiny shoebox apartment in the middle of Tokyo. And by the end of his stay, Adam and I had fallen in love. And that was the first time I had experienced that. It was the first time. And then we immediately got into a relationship for two years. And that was my first boyfriend. It was the first time I experienced that. And I was just like, whoa, this is amazing.  And I just came out to everyone. I told all my friends. I told my family. Actually, my sister had written me a letter while I was in Tokyo asking if I was gay because she had her suspicions. And I wrote her this long missive back and said, blah, blah, blah.  And I explained to her all the aspects of it. And I said, PS, please don't tell mom and dad. I'll tell them when I get home. But of course, she gets the letter and immediately runs to my parents and tells them, which I was kind of happy about. Because by the time I got back to Dallas at the end of that year, my whole family already knew. So she kind of did all the heavy lifting for me. I didn't have to have that awkward moment of mom, dad, I have to tell you something. So they already knew when I got back. And their reaction to me was so lovely. And my parents are, they're in heaven right now. But my parents were very strong believers. My siblings are all very strong believers. And they all believe that homosexual behavior is a sin. I knew that I was completely aware of that. But when I got back, the first night I got back, I walked into the kitchen and my mother was sitting at the kitchen table. And she started crying. And I knew why she was crying. And I was like, mom, what's wrong? And she said, well, I heard you're homosexual. And this was in 1992. And those were scary days, because AIDS was a real thing. And it was a death sentence. And I was terrified. And my mother, that was a big concern of hers. She was really afraid of that as well. And I just tried to allay her fears. And I was like, mom, don't worry. This is just who I am. It's not a big deal. And then so that was kind of the last. And I'll get to her later. But that was kind of the last we talked of it, really. And then the next day, my dad came up to me. I pulled up in the driveway. And he pulled up behind me. And he got out of the car. And he came up to me. He said, hey, Beck, I heard you're homosexual. And did I do anything wrong as a father? And he listed three kind of things that he did. And I was like, no, dad, it's not your fault. This is just who I am. And it actually turns out it probably was his fault. And distant father, overbearing mother, it's a recipe for disaster.  I was whatever. I love him. He's his heaven. Who cares? So then I decided to. And my dad was so sweet about it. I mean, he was so nice and loving. Even over all the years, he was just so sweet to me. And then I decided to forgo grad schools. And I decided to move to Los Angeles to pursue writing and acting, which I do not recommend. And so when I got to LA, like I told my dad, two weeks before I was leaving for LA, I told my dad, my dad was a lawyer. And I said, I'm not going to law school. I'm going to move to LA. And he just was kind of like, he was baffled by it. He was like, what? And two weeks later, I loaded up my truck and moved to Beverly. And some of you know that reference. And so when I got to LA, I already had this built-in amazing group of friends who were all in the business, writers, actors, directors, producers. And they were all my friends of mine from the East Coast, from these Ivy League schools that had migrated West to Hollywood to make it in Hollywood. And it's funny, because that group of friends runs Hollywood now. They run the town. So if you ever watch Netflix or all that content, a lot of that stuff is from my old friends. And so when I got into this group, we all wanted the same things.  It was a whole mix, again, of people. And they were smart. They were funny. They were super ambitious. And we all wanted three things in this world. We all wanted to make it big in Hollywood, which they were doing. And it was crazy. I was watching my friends were becoming kind of unknown people to movie stars overnight. Like many driver was a close friend. And she was like living in my friend's apartment. And she had done kind of one movie with Chris O'Donnell. And then I think that's his name. And then she got the part in Goodwill Hunting with Matt Damon and became a movie star suddenly. And this was happening to all of my friends. Like a friend of mine directed a movie. And we were talking about, you know, we were at this diner in LA called Swingers.  And then it became this giant phenomenon. And Marishka Hargitay was a good, I was her quote unquote best gay. And I drove her to her audition for Law and Order Special Victims Unit. And she got the part. So I think she owes me some royalties for that. Because she's been doing it for 23 years now, which is crazy. And so I was just watching my friends this happened. And it was in real time. And it was crazy to see. And so we wanted to make it big in Hollywood. We wanted to find true love. I cycled through five serious, serious boyfriends over the years, live-in boyfriends. It was almost like I was married and divorced five times. They were very intense. And the third thing we wanted was to have extraordinary experiences. And we were doing that in spades. Because everyone was in the business, we were always invited to movie premieres every Thursday night. And to the Oscars, the Emmys, the Golden Globes, the Grammys, all the after parties, the Governors Ball after the Oscars, the Vanity Fair party, the HBO parties. And so I was always kind of in this world and always at these parties. And I met everyone, knew everyone, was friends with all these people. And one night, Wintz was invited to Prince's house where he performed a concert in his backyard for three hours. And just these things were constantly happening. I would hang out at Paris Hilton's house and hang out at Drew Barrymore's house and swim in her pool every summer. She had the best pool in LA, best diving board. And so I would have dinner with Tom, Hanks, and Meryl Streep. And so I was just surrounded by these shiny objects.  And so God, my whole friend group, not one time did we ever mention the word God. It was just assumed that God didn't exist and that the Bible was an ancient myth like any other ancient myth. We didn't even have to say it. And so this went on for many, many years, from 1993 to 2009. And starting in kind of 2008, 2009, the law of diminishing returns started to set in. And I just kind of was like, is that all there is to a fire? Is that all there is? And I just wanted to, I mean, obviously, everyone wants to know the meaning of life. And I really, I mean, I wanted to know the meaning of life. But again, God wasn't an option. So I looked for meaning in several things, not only relationships, for sure, but in art and in the theater. So I was obsessed. Art was my religion. Whenever I was in New York or London or Paris, I would spend every day at museums. And I loved contemporary art. I loved conceptual art. And I remember I was at the Pompidou Center in Paris. And this is how crazy I am. But there was an exhibition by Franz Klein. And the exhibition was three empty galleries with white painted walls. That was the exhibition. And I was like, this is amazing. I almost fainted when I saw it. I just was like, this is incredible. Negative space. And so art was my thing. And then whenever I would go to New York or London, I would, again, every night I would go to the theater to serious plays, not musicals. I wasn't into musicals. But I would go to serious plays by really, really serious playwrights, Tom Stoppard, Eugene O'Neill, Harold Pinter, Tony Kushner, who wrote Angels in America, which is a six-hour gay Fantasia with political themes is the subtitle of his play. And I saw both parts. I saw the first part of Tony Kushner's play in London and in the West End. And I saw the second part on Broadway. And I was just like, oh, this is brilliant. But I would always go to these plays thinking, like, oh, these guys are so smart. They're going to have some kind of access to the truth. And I'm going to understand the meaning of life. But I would always leave the theater frustrated because it would get so close to the truth, but then it would just evaporate. And I was like, ah, like what? And so I was just constantly in this kind of state of frustration. And it all came to a head in March of 2009 when I was at Paris Fashion Week. And I used to go to fashion weeks in New York and Paris all the time. And in that year, I was there. And I went to a bunch of the shows, the runway shows. And most of the shows have after parties. And I was at this one after party in the middle of Paris at this legendary club called Regine. And everyone from the fashion world was at this party. Kanye West was there. Just all the every designer was there. Stella McCartney was there. And I just remember everyone was dancing and drinking champagne. I was sitting on kind of this platform like this. I was sitting with Rachel Zoe and Roger, her husband. Rachel Zoe was a fashion girl who was on Bravo. And I was just like, I was sitting with them. And I stood up and I was drinking my champagne. And I looked out over the crowd. And everyone's dancing and having the best time. And I just felt this suddenly this overwhelming sense of, I can't do this anymore. This stuff has worked for me since high school. All this kind of shiny stuff has worked. And it sustained me. But it's not doing it for me anymore. I can't keep going to parties and dinner parties and award shows. This is not going to do it. And I was in a panic about my future. I'm like, what am I going to do for the rest of my life? How am I going to sustain myself? Am I going to be put out to pasture in Palm Springs like all other older gay men? Is that my fate, my future? And so I get back to LA a few days later. And at that point in my life, I was a production designer in the fashion world. So I did shoots for Vogue and Harvest of Bazaar. And I worked with actresses and pop stars and movie stars and on all these shoots. And I did ad campaigns for L'Oreal and for Gap and Nike and all these brands. And so I got back to LA. And I got really busy with work again. And when you're in that production world, it just takes all of your everything. It takes all of your time and energy. So I kind of forgot about that night in Paris. Cut to six months later, I'm at a coffee shop in Silver Lake called Intelligencia. And every weekend, my best friend who was also gay, he and I had the same kind of routine. We would go to brunch in Venice at our favorite little cafe, this beautiful cafe. Then we would drive across town to Beverly Hills or West Hollywood and go shopping, which is gay church, brunch and shopping. And then we would go to Intelligencia and have coffee and hang out all day. Because it was a very popular place. And people would come and go. We knew a lot of people there. So we would hang out. And that particular day, I think it was a Sunday, we're just having our normal conversation, talking about guys, whatever. And we suddenly, we look over and there's a table next to us with five young people at the table. There were five physical Bibles on the table. And we were shocked. I had never seen a Bible in public in LA in my life. Never. And I had never met a Christian in LA. So my friend loved to sort of engage in controversial conversation. And so he kept urging me to talk to them. I was like, no, I don't want to talk to them. And finally, he kept prodding me. And finally, I turned to them. It's like a Christian's fantasy come true.  What's the gospel? That's literally what I said. It was so easy for them. So we got into this really long conversation for about an hour or two. And it was really interesting. And I said, look, what do you guys believe? I don't even remember what. Like, tell me about your faith. And we talked for a long time. And then I got to the $64,000 question. And I said, well, what? They told me they were evangelical Christians. And I said, well, what does your evangelical church in Hollywood believe about homosexuality? And they said, well, we believe it's a sin. And it was just so kind of a matter of fact and frank. And I wasn't surprised. I wasn't surprised by their response because I expected that. I was surprised by my response because a year before that, 10 years before that, I would have been like, you guys are insane and I'm leaving now. But because of that night in Paris six months before, I was open to hearing something different. And I had this flash of this moment of like, what if God does exist?   And what if I've built my entire life on the false foundation and I don't know it? I mean, that is a possibility. So they invited me to their church the following Sunday. I said, look, I don't know. Just give me the address and I'll think about it this week. And because it was a big deal.  Evangelical Christians were the enemy like according to my friend group and according to, you know, everyone in LA basically. And so evangelicals were the enemy. So I was like going into enemy camp and the enemies camp and so I had a week to think about it and I was kind of going back and forth, back and forth.  Like I had never been to an evangelical church. I didn't know what it was like and had no idea what to expect. And God in my car drove to meets in a high school auditorium on Sunset Boulevard.  I forgot that existed. Ooh, weird. But then I was like, no, actually it's nice. It's beautiful. And then I sat on the fourth row by myself. I don't know where the people were who invited me there, but I sat by myself. The pastor comes out after the worship time and he after the worship music and he starts preaching on Romans chapter seven. He had been going through the book of Romans for two years and expository. And so he was preaching on Romans seven. I don't know why that's funny. And he, as he was, he preaches his sermons are an hour long and as he's preaching, strange things start to happen to me. Every word that he's saying, every sentence that he's saying is resonating as truth in my mind and my heart and I don't know why. And I'm literally sitting on the edge of my seat, riveted to the sermon. I don't want him to stop talking. Even after an hour, I was like, just keep talking. Like, what are you saying? And it was the first time in my life that I had heard and understood the gospel. And so after the sermon, he said, there's people on the side of the auditorium. If you want prayer for anything, they can pray for you. And then he left and there was another 30 minutes of worship music. And so I had this moment again. I was like, do I go to the side to get prayed for? If I do, the people that invited me here are probably watching me. It could be humiliating. And I was like, whatever, I'm going over. So I walked over to this guy, the stranger, and I said, I don't know what I believe, but I'm here. And he laid hands on me when that was still legal in California and he prayed for me. And he, and I just was like, the prayer was so powerful. I was like, how does this random straight dude care about me so much? And then I thanked him. I walked back to my seat. I sat down and everyone else was standing and worshiping for another 25 minutes.  And as soon as I sat down, the Holy Spirit just went. And I was like, whoa. And in that moment, God, it was like a road to Damascus moment. It was so intense and God revealed himself to me in that moment. In my mind, God said, I'm God. Jesus is my son. Heaven is real.  The Bible's true. Welcome to my kingdom. And I was like, oh my God. Yeah, praise God. And I just started sobbing and sobbing uncontrollably for the next 25 minutes. I was doubled over and I was crying over two things. I was crying over the conviction of sin and over the joy of meeting the king of the universe, Jesus cries. And it was like the curtains had parted and I could finally see the meaning of life. Like I knew, finally I knew where I came from, what I'm doing here and where I'm going. And it was the most amazing moment of my life. And then it happened again. I got in my, after the service, I got in my car. I don't even know how I made it home because I was such a wreck. And I got into bed to take a nap because I was just so overwhelmed by it all. And when I got into bed, God was like, it was like Moses when he's in the cleft of the rock and God passes by with his glory. God's like, let me show you some more of my glory. And I just felt like this wave of God's presence. And I burst into tears and I jumped out of bed in the middle of my bedroom. I said, God, you have my whole life. I'm yours. I'm done. And that was September 20th, 2009. And I knew in that moment, yes. And I knew in that moment, I mean, it was so clear. God had so much grace on me.  I knew that it was not my identity anymore. I knew that dating guys was not a part of my future, but I didn't care because I just met Jesus. And I'm like, good riddance to that old life. I'm going with this guy. Like he's amazing. And I think of Paul when he says in front, one of my favorite verses is just in terms of this is when Paul says, I count everything as rubbish as loss because of the surpassing worth of knowing Christ Jesus is my Lord. And I've lost a lot of kind of friends. I lost my career when my book came out in 2019. But it doesn't matter. Like I have Christ. So everything else doesn't matter. And so I want to turn to in the last remaining time, I want to turn to some of the questions surrounding this issue that I get a lot. And then tonight we'll get into more of it. But one of the questions that of course is always asked is, but aren't you born gay? How can it be wrong if you're born gay? Well, first of all, just because Lady Gaga sings that song doesn't mean it's true. She's not a scientist. But there's three theories about why someone, there's three dominant theories why someone develops same sex attraction. There's a genetic predisposition. There's a hormonal and utero theory. So when you're in your mother's uterus, you get too much progesterone or not enough testosterone, whatever. And the third one is nurture, is your environment. Like distant father, mother, that kind of dynamic. And the point of it is it's a mood point. Because of the fall, because of sin, we are broken. And not only are we born and sin, we're conceived and sin. So even our genetic coding is corrupted by sin. That's why there's birth defects.  Like we're our genetic coding is corrupted and we're born and sin and we need to be born again. So, and every human being, we're all, everyone in here, we're all born with sinful innate impulses. But that doesn't mean we act on those impulses.   Now there's a couple of categories I want to talk about. It's because this specific sin is in my book, I talk about how it's same but different. It's the same, it's different from other sins in a certain kind of way because it is such a powerful identity in our culture. And when you experience these feelings, it does feel, I have to say it does feel like it is who you are, it feels that way. So that's why there's gay pride parades but not greed pride parades or tax collector pride parades or adultery pride parades. So this is a different, it's a difficult one, this one. And by the way, my heart goes out to anyone who struggles with this because I know it's difficult. And but there's a couple of categories.    It's gone from a sin to a sacrament.  And without repentance, there's no salvation. And I'll just read, I just wanna read a couple of verses from 1st John. He says, no one who abides in cries keeps on sinning. No one who keeps on sinning has either seen him or known him. Whoever makes a practice of sinning, notice this is kind of a continuous unrepentant kind of practice of sinning. Whoever makes a practice of sinning is of the devil for the devil has been sinning from the beginning. And the last verse I'll read is, no one born of God makes a practice of sinning for God's seed abides at him and he cannot keep on sinning because he has been born of God. And that's what's so dangerous about this idea that of this identity of being gays is because you will never repent of it. Whereas, you know, if you're committing adultery or whatever, if you're stealing or murdering, you're gonna repent of that, right?   So why would I hang on to that old man? Why would I hang on to that moniker and carry it around like a binky? Like it doesn't make any sense to do that. And you're also blocking the Holy Spirit from sanctifying that part of you. And so that's the really dangerous idea of the calling, that group of people who call themselves gay Christians.  There's no such thing as a sexual minority. There's only sexual sin in the Bible. There's not one verse about being a sexual minority in the Bible. So I just find that baffling to, because when I got saved that day, I mean, again, God's grace was so powerful and amazing, but I wanted to run as far as possible from that identity, from that life. I never, that was, it's like, I don't ever wanna go back to Egypt. I was in bondage in Egypt. I don't wanna go back to Egypt. I thought I was sexually liberated for all those years. I was in bondage and God freed me of that. Why would I wanna hang on to any aspect of that? So that is, that's not, but the good news. By the way, I mean, the good news is for those who are struggling with this or those who are in this life and you may be here, you may be listening, the good news is when Paul in 1 Corinthians 6, he lays out this vice list, you know, maybe I'll just read it quickly. Paul says, or do you not know that the unrighteous will not inherit the kingdom of God?  And here's the beautiful gospel. But such were some of you, but you were washed, you were sanctified, you were justified in the name of the Lord Jesus Christ by the spirit of our God. That's the beauty of the gospel. And the last thing I'll say, I think, oh, there's music. The last thing I'll say is people often ask me, Beckett, isn't it unfair that you don't get to be with someone, you don't get to be with a man for the rest of your life? Like you have to be single and celibate or single and chaste. And I literally, honestly, I've not once in my life have I felt cheated out of anything. And not once have I felt like my life is unfair. In fact, I feel the opposite. I feel like the luckiest guy in the world. Obviously I don't believe in luck, but you know what I mean, I feel so lucky that I feel so grateful that God adopted me into his kingdom and gave me everlasting life. I mean, that's kind of a big deal, right?  What's unfair is that Jesus had to be beaten, tortured and crucified for my sin. That's unfair. My life is not at fair at all. So I, and Paul, I mean, Paul was single. Jesus was single. Do you think Paul thought his life was unfair? All Paul cared about was running around the Mediterranean planting churches, spreading the gospel. And he was beaten, stoned, shipwrecked. And no, he didn't, he all he cared about was the gospel. And so I'm all in here and we can get into some more questions tonight, but I'll just end in this prayer. Father, thank you so much for this time. Thank you for your amazing mercy and grace on us, Lord, for your salvation. God, thank you for rescuing us out of darkness and bringing us into your marvelous light. I pray for anyone here who is struggling with this sin, who is struggling with this issue in their lives or a family member who has a family member who's struggling with this. God, I pray that you would pour out your mercy and your grace on these people, Lord, and that you would help them. God, we need the power of your Holy Spirit to deny our self, to deny our desires and take up our cross and follow you. So I just ask for anyone who's struggling with this, God, that you would help them by your spirit, by the power of your spirit. And Lord, I just thank you so much for this church, I thank you for what you're doing in this church, God. And we just love you, Lord, we bless you. And I just thank you so much, God, in Jesus' name, amen.