Bryan is Chief Technology Officer at Skyscanner, responsible for enabling the 500+ strong team of software engineers, data scientists, product managers, and designers to earn the trust of hundreds of millions of travelers around the world by delivering a simple, elegant product experience.
Bryan has previously held leadership roles at Amazon Web Services, Skype, and Microsoft.
[00:00:01] So, a picture is worth a thousand words. It's an expression we've all heard probably a thousand times. And I just want to take a minute to get everybody to get their head around that and start to think about something. I'm going to share something quite quickly here.
[00:00:16] So first if you see this word, you might look at it, I assume most people can read it, you understand it. You might think about something. you might think, "that's what happens when the sun goes down." But there's not much beyond that. And then if you compare and look at something like this. And even in the same amount of time it will conjure different feelings. You start to think about what's going on. You start to think about "where do I get to to go enjoy a sunset like that?" Relative to the beautiful summer weather we're having here in Edinburgh today. You think about the last time I was on a beach, the next time I'm gonna =be on the beach, the next time I'm going to get to a holiday, the next time I'm going to get to relax. All of that gets conveyed in just the same amount of time — far more than what you can get in words. We read at about 300 words a minute, so a thousand words is about three and a half minutes versus three seconds.
[00:01:07] So I really just want to talk a little bit about the power of images and what does this mean as this evolves from an artificial intelligence and machine learning perspective, and really provoke a couple of different ideas and thoughts about what we can do and how we need to think about that. So just a couple of more images. Just think about the density of information that we consume when we see these. We look at this we might recognize the sport, might recognize the event. This is Muhammad Ali knocking out Joe Frazier. You might recognize who the person is. You can imagine this might take place in a stadium. You can see the emotion and the relief and the victory and the intensity just by looking at a photo. Now imagine trying to write a news article about this: you would have to spend far more time for somebody to consume that than what they can get in a picture. Look at something like this: it's a relatively famous picture of Albert Einstein. But you might see something: it looks fun, you might recognize who it is, you might start to think about what does Einstein represent in terms of science? The amount of information that we can, not just process and synthesize, but what we recall, the connections that we make in our brains. Images are incredibly, incredibly dense and we can process them incredibly quickly. So what does that mean when we start thinking about what happens when computers start to see as well or better than we do as humans? What happens when machines are able to recognize any and extract all of that same context and yet be able to soak up all of the related contests at the same or greater speed?
[00:02:33] Well, one thing that happens - cameras start to replace keyboards. Anybody who's ever tried to search for something on your phone; you're trying to search for something really, really specific or find it. And you're sitting there tapping, tapping, tapping — it takes a while. It'd be a lot easier if you could just take a picture of it. So I want to share maybe a couple of examples of where where we actually see cameras starting to replace keyboards in things that we may do every day.
[00:02:56] So this is the Amazon app. You can go into the app rather than typing in what you're looking for, you can just take a quick picture, it will recognise it and pull it up and you can order that just like that. So you just tap that. You don't even have to type anything. Interesting thing, this feature has actually been in the app for about three or four years at this point. Not highly celebrated, and it's something that they continue to work on and continue to tune. You'll get something like Pinterest. Pinterest released an application earlier this year called Lens. Pinterest Lens can look at a picture, specifically food in this case, recognize what it is, recognize where you may want to use it, organize recipes that you can make. These are all different pins. You can click onto these and you actually get various recipes of what you may want to do with with strawberries. Interestingly enough by the way, anybody who watches the Silicon Valley TV show? So they had an episode about something called Shazam for food and then Pinterest released this about three days later. They just happened to be conveniently working on it. Great YouTube clip, by the way, if you just look up Shazam for food on Silicon Valley.
[00:03:57] So those are kind of neat tricks you can do in apps on your phone. But what does this really start to mean when you see somebody taking this to the extreme? When you see people pushing the leading edge of what's actually possible. And so this is a quote from William Gibson which I just love which is "The future is already here - it's just not evenly distributed." So I wanted to share a video of what actually is possible today. This is going to be a video from Amazon. Some of you may be familiar with this concept, some not. But I thought I'd share this quick video and we can kind of get a sense for really what is possible and what is this world that we're beginning to emerge into.
[00:04:35] "Four years ago we started to wonder: what would shopping look like if you could walk into a store, grab what you want and just go? What if we could weave the most advanced machine learning, computer vision and AI into the very fabric of a store so you never have to wait in line? No lines. No checkouts. No registers. Welcome to Amazon Go. Use the Amazon Go app to enter, then put away your phone and start shopping. It's really that simple. Take whatever you like. Anything you pick up is automatically added to your virtual card. If you change your mind about that cupcake. Just put it back. Our technology will update your virtual card automatically. So how does it work? We used computer vision, deep learning algorithms and sensor fusion much like you'd find in self-driving cars. We call it 'just walk out technology.' Once you've got everything you want, you can just go. When you leave, our just walk out technology adds up your virtual card and charges your Amazon account. Your receipt is sent straight to the app and you can keep going. No lines, no check out. No, seriously."
[00:06:15] It's pretty cool, right? You know, when you look at what's actually possible today... by the way, this store is open in Seattle, it's open to all of Amazon's employees right now and will open to the public, so it's only limited to about 400,000 people that can use it. Having talked to a number of my friends that have used it and shop there, it really does feel like magic the first time they do it. And I think that's what's really special about some of the times in computing that we're walking into, is we're starting to get these things that really astonish us, things that we may have thought about or read about from a science fiction perspective just even a few years ago actually show up and show up in the real world and become available. So when you get something like this and you start to think "wow these technologies are actually out there." The other thing that's happening is the distribution cycle of these innovations is rapidly accelerating. So if we talk about the new normal for research, you have a number of companies around this world putting in billions and billions of dollars to create all of this leading-edge modern science fiction magic in technology.
[00:07:16] So here's an article from Microsoft. This got published last October. And they achieved true parity for voice processing to text at the same level of human recognition. Same speed, same level of background noise, same level of accuracy, and there's all kinds of math models behind this. What I find is actually really interesting is this thing right here. If you read that little word: they used a piece of software that they custom developped and that is just open source, out on GitHub. Anybody in the world can get access to this technology. And this is the pattern that you see from every single company. You see companies like Google, Facebook, Microsoft, Amazon, companies in China - China and the US are two of the leading locations for AI work - companies like Baidu, Tencent, you see all of these innovations coming out there. And this is really what's happening in research today. The world's leading-edge research is available for free and for everyone. And the speed that these innovations will come to the rest of the world and the ubiquity that will begin to show up in all of the various applications and services that we use everyday is unlike anything that we've seen up until now.
[00:08:25] I want share another example with you. Now this is from a research team, I forget which university, but here in Europe. And if you look at the bottom left that's your control case staring dead straight. You look at the one on the upper left, and it's somebody just talking and saying some stuff. Now what's fascinating about this is that through computer vision they're able to extract the mouth motions and project them on to the person on the left, and that's what you see on the right hand side. In real time, zero loss, totally believable.
[00:08:56] Now what happens when people start to think about what else could you do with this technology? If you could take somebody's picture and put somebody else's mouth movements in, someone else's words on it? Well you get this. This comes out of a research group at the University of Washington. They put together a great YouTube clip if anyone wants to go check it out. Just search for 'Barack Obama fake speech'. It's like a two or three minute speech. And if you close your eyes it sounds like a Barack Obama speech. It has the right cadence, the right pitch, the right words, all the vocal inflections, all the pauses, everything is there. But these are the types of things he's saying: "You might recognize me, but this is completely fake." And then we start to think about well what happens when everybody has access to this technology? What happens when everybody can figure out what's both optimistic and potentially pessimistic or manipulative things that they can do with it? So it really starts to provoke a question for all of us. Who can we trust? How do we know if something really becomes real when the availability and ubiquity of technology can show us things... when no longer can we believe things that we see with our own eyes.
[00:10:02] Another interesting quote from Eric Schmidt, from Google. It was pretty interesting to me about this, this is from 2011, facial recognition is the only technology that he claims Google had built and then decided not to continue. This is also interesting, this is from a company that commercialized some various deep learning and machine computer vision technology. And you can see there they put together this comparison chart to tell you why their thing is great. I was pointing out the facial recognition piece because there will be more than zero companies that exploit all of these both, for positive and negative, and there are definitely positives to facial recognition. This is an article from a couple of weeks ago about the UK making an arrest. You have interesting yet perhaps questionable techniques. This is about an app called Find Face that exists in Russia. Basically it works against the Russian Facebook, but you can take a picture of anybody you see on the street, take a screencap of anybody you see in a YouTube video, anybody you see online, and it will locate with 70, 80, 90 percent accuracy that person's profile on VK, which is the Russian equivalent of Facebook. Kind of creepy. You see the headline there questioning what does this really mean for anonymity. You can get to start doing things like machine learning for forecasting and predictions. We can now do facial recognition and put two and two together and you see China now talking about using facial recognition and AI to actually predict what crimes are going to happen before they happen and in some cases take preventative measures. It's a fair question, where do you draw the line? Should we draw the line? How do we do so? The more time is spent thinking about this, this is kind of how I started to feel.
[00:11:43] I wonder if we're all just screwed. Right? Over time will there just be sinister actors that do every bad possible thing with every available piece of technology? No matter what happens, somebody will do something bad with it. And the more I start to think about it - I don't wanna get too philosophical - but I think broadly we have to believe in the positivity of people and of humans. Most people want to do the right thing. And I'll tell you where I really got some some inspiration around this. So there was a talk a couple of weeks ago, and I'll share a video in a second, at a UN forum on 'What does AI mean going forward?' And they started to think about 5 years, 10 years, 20 years, and they talked about a term that really really stuck with me. And so the way I can translate that back as an engineer is I thought about, well, engineers, we often look to build things. We often ask, "Can we do it? Can we put that together? Can we make that work? Can we put somebody else's words into Barack Obama's mouth in the right time and in the right pitch?" But there's a balance here which is about ethics. And ethicists really ask "Should we do that?" So what do you think about that? Because I think there's some some inspiring and optimistic activities happening, because this is not just a conversation happening in engineering. You actually see this conversation permeating virtually every industry, every vertical you see business leaders talking about this all around the world. This is the CEO of Audi and I just want to share this. Just a small one minute clip of a talk that he gave for the UN called "AI For Good." Again, there's a YouTube clip on this about 15 minutes, it's really worth checking out the entire talk, but I thought this particular section was really really impactful.
[00:13:22] "When we let people try out our research car check, we often see that minute after minute people gain confidence and trust in piloted driving. Seeing is believing. However, ethical concerns exist and we take them seriously. The best known examples of these ethical questions is a dangerous traffic situation where an accident is unavoidable. Imagine a situation where the autonomous car has got three choices. Either it is left and harms an elderly lady or is right and it hits a pregnant woman or it drives straight into an obstacle and thus harms the passenger. In such a situation human beings like you and me have no time for thoughtful decisions. We simply react. But interestingly, we expect the autonomous car to make always the right decision. And quite understandably people are emotionally touched when thinking of such a scenario.".
[00:14:42] So it's an interesting question. Imagine you were writing software for any random problem on the web. Might say somebody provides an input value and you want to do the right thing and you write some if statement or some case statement. OK. If left, go left, if right, go right. If somebody sends in "banana" instead of left to right, you go, "I dunno, throw an exception." If you're writing code for a car, if you're writing code for facial recognition at immigration, at customs, at visa processing, for the police — what's the exception that you throw?
[00:15:16] How do we think about the impact of the code that we write and how does it actually start to impact the real world as these technologies in the real world become more and more fused? So this really got me thinking from a technology perspective, from an engineering perspective. What should we do? And I don't know if this is an exhaustive list but I find there's a couple things that guide me. And something that's really been rattling around my head for a while is this notion of Ethical Engineering. All of us here all have access to influence and create technology products that are going to be used by others. What are the types of things that we can be doing to ensure that we're doing everything we can to use technology for the better? That we can avoid accidentally creating a negative impact or intentionally creating a negative impact for people? How do we think about user trust and that it takes forever to earn trust? Just think about your friends. I'm sure everybody has that friend who is a great friend for a number of years, and that one time that they broke your trust, that one time they screwed you, that one time they went behind your back, you don't forget that. That level of trust is effectively never repaired. And the way that our users use our product, whether the users are other people in our company, consumers, enterprises, that level of trust is similar. How do you ensure that you never disrupt, that you never break, that trust? How do you think about making sure people's personal information is sufficiently protected? How do you make sure that when people give you personal information, they know what they're doing as opposed to taking it?
[00:16:46] If you think of, you know, the advertising tech on the Web today, it¡s built on a lot of opt-out principles rather than opt-in. Is that the right thing? To be honest, I don't know. These are questions that rattle around for me. As more and more powerful technology becomes available, what do we do from an engineering perspective? How do we influence this from a technology perspective? And I come back to those questions of can we and should we. And this is sort of where I came to. I think from an engineering perspective we need to evolve from strictly looking at whether we can solve the problem, whether something can be done, and look at it equally weighted. Should we be solving our problem? Is that the right way to do it? Are we doing it in a way that urges trust from users? That earns credibility? That protects their information? That ensures that we're using technology - and as it further and further infuses into our lives - that we're using it for a benefit and to take things forward? And we're not advocating to create harm. With that, I'll wrap it up. Thank you very much.