Cannes Round-Up 2: Full Episode

Hey Alexa, play Machine Yearning.

Here's a sample of Machine Learning by Theoretix.

No, Machine Yearning podcast.

You're new to this, aren't you?

Yeah, that's true.

M-A-C-H-I-N-E-Y. M-A-C-H-I-N-E-Y. E-S-R. AI, AI, AI, AI, AI, AI, AI.

It's Machine Learning from Assist. It's a podcast where we think and dream about the future of AI, the talking internet, and how we're reshaping our culture. This week we're back with more conversations from the 2018 Cannes Lions International Festival of Creativity. We figured a place where over 10,000 leaders and innovators in technology, advertising, and brand communication were gathered would be a fertile ground for guests for the pod.

Machine learning.

We hit pay dirt. From our spot in the floating home of LivePerson, we were challenged, inspired, and learned a lot. Once again, all of us at Machine Yearning and Assist owe Rob Locascio and his team at LivePerson a huge thanks. LivePerson took great care of us and are a true friend of the pod. Starting things off, Shane sits down with Daniel Hodges. Daniel's a pathfinder for brands, industries, and individuals who are weaving their way through this disruptive time. He thinks like an analyst but talks to you like a trusted friend. His view spans a wide range of technology verticals, and his time in the field gives him a perspective we think you'll find particularly valuable. We loved our time with Daniel, and we think you will too. Enjoy.

Daniel, how are you?

I am well, and how are you?

I'm doing great, man. How many years have you been coming to Cannes?

Well, I've been coming since 2007 when I was with Nokia and we sponsored the event.

2007. What was happening in 2007?

You know, just the emergence of this new platform called mobile and smartphones.

What was the smartphone at the time? Because that was pre-iPhone launch, wasn't it? Or is that right when the iPhone launched?

No, it was pre-iPhone launch. It was the Nokia N95, which was like a box. So, uh, and it was very popular. It was our most popular handset in the world.

Yeah, what happened to Nokia?

You know, I think that the, the Nokia phone was a really good phone functionally, and the Apple was an inferior phone except for one thing, which was the user interface. And it was the user interface that sort of won. And I think Nokia was a little bit in denial when it came out, and by the time they got it together, it was too late.

What are you doing at Cannes this year? What brings you here?

You know, I come to Cannes every year to to be exposed to the various, you know, creative leaders and technologies and the randomness of Cannes. So it's a place for me to get caught up and up to date on sort of what's going on in the marketplace. So I've taken clients here before, but this year I'm here really just trying to get to the bottom of what's going on in the market.

And so you said user interface, and that was what really defined the difference between the mobile device, right?

Right.

And now, is the user interface gonna be voice? Does it feel like it did in 2007?

You know, it's interesting because there is currently a voice war on, and Amazon has about 70% of the voice marketplace right now, and others are trying to catch up. So I think that Amazon, with all this data, and I mean, it's very possible that at some point Amazon will be sending you two boxes. One box will be what they think you need, and another box will be an empty box to return what you don't need.

That's interesting. My co-founder Robert said anticipation versus automation. And he said whoever nails anticipation, is that kind of what you're talking about? Like you're going to anticipate what I need so then when I go to Amazon, it actually is showing up at my door at the same time?

Yes. And I think you asked about the iPhone. What the iPhone did was the UI/UX eliminated friction. And it eliminated clicks. And so what Amazon has done with voice is they've eliminated friction. And voice is, you know, it's a 200,000-year-old sort of phenomenon out there. And so what they've been able to do is, you know, use voice and use their massive database and artificial intelligence to figure out what you might be interested in.

And what are the specific areas of voice that are really interesting to you?

What's interesting is that the dialogue for voice, the car is the next big commerce platform with 90 minutes in a car. I mean, that's a big deal.

Can you say, okay, so the car is the next commerce platform. Say more. What do you mean by that?

Well, I mean that when you look at some of the cars like the Byton, which is the Chinese car, you'll actually see the dashboard is sort of an interactive user interface LED screen. Which is 8 feet long and 18 inches tall. And so you have the complete ability to order things, to look at things, to interact in your car for shopping, for banking, for almost anything. And that's 5 years away, but, you know, 5 years can go pretty quickly.

Do we need a self-driving car to be able to be that distracted in our car though?

This sounds dangerous as hell.

Yes, you will need a— this is all part of the self-driving car phenomenon. But yeah, and that's part and parcel. So one innovation begets yet another innovation. So the self-driving car will create a whole new marketplace for commerce.

But let's talk about today when there's not a self-driving car. Is it going to be— so if I'm a brand and I want to think about being in the car, are all the cars just going to install Google Assistant, Alexa, Siri, and then I'm the driver and I decide which one I want to use? So I should just be on those platforms or do I have to build for a car?

Interesting. That's a question that will be answered in the marketplace in the next few years. So I don't necessarily think there's an answer right, right this second.

Do you think that the assistants and the Alexas, et cetera, do you think they just are more specialized in their own thing? So for commerce it makes sense, but for information, if Google Assistant's better, would I almost like I go to different URLs for websites? I go to amazon.com for commerce. I go to google.com for information. I go to, Nike.com to look at shoes.

Yeah.

Do you believe you're actually going to just talk to different assistants or voice agents for different things and they're going to be all in your car?

That's probably more like it because right now when you are looking for, let's say you're looking for a yacht in the south of France, you may probably start with Google and then you actually may go to Amazon at some point. So I think it can flow from one area to the next.

Interesting.

Yeah.

What's changed in the last 18 to 24 months in the narrative of AI?

It's gotten to the point where it's become in the consciousness of people. I mean, AI is not anything new. It's been around since 1500 BC. So it's not new, but it's the, what I attribute it to is removing friction. When you can remove friction from a marketplace, you get acceleration. And that's the story of Nokia. I mean, Nokia went from being a 45% market share to being out of business in 6 years because they missed that element of friction.

Tell us what you do.

So I work with boards of directors and CEOs and CMOs, and I help them navigate all the change. So all the technological change that's going on and consumer behavior change, because I feel one of the big challenges is that the— I believe the brain has been rewired over the past 10 years. And it's when you've got people looking at their phones for 300 times a day, it basically rewires the brain in causing attention spans to go from 13 to 6 seconds. And if you're a marketer today and you're using a 30-second commercial, good luck, because you only have about 6 seconds of attention span these days. So it's kind of a way to answer your question.

And rewiring my brain, can you say, like, do you believe that that is if it happened faster than ever before? Is it in the last 10 years because of mobile? Is it through just the really the computing age? Our brains are always, I think, being rewired, but is it just happening faster? Is that what you're saying?

Well, it's happening faster and it's happening at scale because, you know, you've got billions of people with smartphones and If you go back 11 years ago when Apple sold about 300,000 phones in 2007, it wasn't there. But now the price of smartphones has come down and anyone can do that. In fact, governments like Dubai are basically running all of government on a smartphone. So, you know, a good analogy is think about when you ordered a taxi cab. About, let's say, 5 or 10 years ago, you call up a number and you'd wait maybe for about 5 or 10 minutes for someone to get back to you when a cab would be available. But now you order Uber, and if you don't get a response within 20 seconds, you're upset. And if the car can't be there for 5 minutes, you're even more upset. So what happened? And I think what happens is the behavior that's happening with Uber, that transcends itself to other services. So people say, well, I can get a car in 5 minutes, why can't I get a dress in 5 minutes? And why can't I get a yacht in the South of France in 5 minutes? Why is it taking so long? So it's not necessarily something that is as different as ordering a cab can actually impact shopping because it's a consumer expectation that if I can get a car in 5 minutes, then why can't I get a suit in 5 minutes?

Do we understand the implications of 3, 4 billion people's attention span getting cut in half. What's gonna happen with that?

You know, in human history, there are points where we sort of go ahead, using a Star Trek analogy, at like warp speed. And so we're in one of these warp speed moments in human history, and it's hard to fathom.

What are you waiting to see happen? For brands, customers, technology? Like, what are you waiting to see?

Well, it's interesting because in the US, uh, we had a presidential election, and, um, you know, politics aside, one thing that we've seen with, with Donald Trump is that he has been able to get simple, direct messages and garnered, you know, billions of dollars of media attention. And so I think a lot of people will look at the the marketing lessons of a Donald Trump and say, how is he being so impactful? And I think one of the reasons why he's being so impactful is he's a marketer who understands that attention spans are what they are, and he's communicating in very succinct bullet points, and he's very consistent. So his messages are designed for the world today, but he's also consistent. So he's Make America Great, and P&G was Ring Around the Collar. So it's only complicated if you're in the world working in the way it used to work 5 or 10 years ago. It still works because newspapers still work, although they're greatly diminished. But those that are being successful are understanding all these different things. It's about storytelling, context, community.

If I was the CEO of a large retail brand today, where would you tell me to go look?

I look at what Amazon is doing because Amazon at one of the retail conferences talked about the fact that they have a store and not everything's working out for them because it's hard to understand if you're reaching for the milk, is that your hand or is that my hand? And it's hard to understand, is that milk there or is that yogurt? A lot of, you know, a good example in the 20th century was Thomas Edison. Thomas Edison, he tried thousands of different experiments. And so to be innovative, you've gotta be willing to fail. And because in failure you learn what works.

Is that something new about the AI space? Is that failure is a part of the entire thing?

It should be because, you know, not everything's gonna work.

So how do you cope with that as a CEO? If you're leading a big retail organization and the internet has always, you know, you tested it and the webpage worked, but in this space Siri doesn't even work all the time. So your AI is not going to work all the time either. How do I even, how do I even think about that?

Well, there are a couple of ways. Um, you do experiments and, um, you learn, but also there are other companies that are doing this. So for example, sort of a different analogy, but if you look at supply chain, if you want to know, um, like the best supply chain in the world, you would talk to Coca-Cola because, um, or if you want to know, um, sort of the best practices in facial recognition, you talk to the casinos. So some of these technologies have— there are industries where things are very advanced and we don't have to spend 10 years of learning to do this. We can actually just go to some organization that's actually doing something like it and it may not be exactly our use case, but learn.

Thank you very much.

You're welcome.

Daniel Hodges on today's episode from the Live Programming person yacht at the Cannes Lions.

Machine learning.

Up next, we have Demetra Sealy. Demetra is an Australian actor, and before she moved to New York, she worked with the Australian Broadcasting Company. As you'll soon hear, Demetra has had a front row seat to some amazing AI developments, specifically in the ways AI can be enabling new possibilities through mind-body software collaboration. We loved discovering Demetra at Cannes because machine learning is all about the ways we navigate a new world where identity and technology and commerce and culture have to live together in ways we can't even fully imagine yet. Got an idea for a guest you'd like to hear on Machine Yearning? Let us know. DM us @Assist on Twitter. And now, Demetra Seely.

How are you?

I'm good. I'm quite honored to have been asked to be part of your podcast.

Becoming Superhuman. What is it?

I had some stuff to do with a project called Becoming Superhuman, which is a documentary part of the ABC Catalyst series, which in Australia is a television series that each week shows developments in science and developments in other areas of technology, etc. And the project was with a doctor called Dr. Jordan Nguyen. And he is an Australian electrical engineer who, with his father and a team of many invisible people who never get any credit, created a wheelchair that you can control with your mind. And he works heavily with children especially that have cerebral palsy. So he, for this documentary Becoming Superhuman, adapted the technology that he sort of created there into creating this child whose name was Riley, who has pretty severe cerebral palsy, dream of driving a car, which at 13 is illegal, but also probably not something he's gonna get to do. So, spoiler, they figured it out, otherwise it wouldn't be much of a documentary. And they helped create this technology that allowed him to more or less drive a car with his mind.

How far? Where was he driving?

So they rented out this big park and he actually did a little obstacle course around. It wasn't just straight driving. He did turns, he did all sorts of really interesting things.

And for accessibility, I mean, it could change everything.

If it could change millions of people's lives who previously can't leave their chair, what else can you do?

Can— if you can drive a car, can you use your brain to turn on a light or turn on a TV or turn on a microwave or turn up my heat.

Actually, they do this in the documentary. They play with that kind of experiment, and they turned their warehouse studio into a replica of Riley's home, and then they, they actually made him a version in his house. Because when they sort of spoke to him, he expressed his frustration that he can't do anything. Like, if he wants to turn on the TV, he's got to wait till someone comes in the room than try and hope that they understand that he wants to watch the television. And so the goal was to make him more self-sufficient, so he can now turn on lights in his home, he can turn on the television, just basic things that we absolutely take for granted.

This is incredible. It's funny, you think of Alexa and Google Assistant and Siri and all these things that you have to actually talk out loud to. Hearing this, maybe the future is just thinking it and it happens and you don't even have to talk to Alexa.

Well, in one of the seminars they talked about Elon Musk is trying to develop a brain lace which literally surgically is put over your brain and you can from your eyeball search the web. So you just think it and it does it. And that's technology that's not far off. AI is the future, I suppose. Like, who really knows what else the future has to offer.

Who knows?

But if you're sitting here in one year and we're doing this again, what's your number one wish to have happened?

I think that in one year what I'd like to have seen with the world is that gender, race, all that kind of stuff doesn't matter. And I think out of people being more diverse in the workplace, et cetera, we're gonna create things that different minds and cultures being brought together will create amazing, brilliant new technologies that we cannot do if everyone in the boardroom is and looks like you. If you're thinking with pretty much the same brain, what are you gonna come up with? You need people who challenge you. You need people who are different to you. You need people who have absolutely completely other side of the world, different experiences to you to create new innovation.

Thank you for doing this.

Pleasure. Absolute pleasure.

Machine learning.

Dimitra Seely in the second of a couple machine learning episodes that we're doing from the Cannes Lions. Thanks to LivePerson for helping to make these episodes possible.

Machine learning.

Next week, we're going to be speaking with Christina Malin, and it's an episode you're going to want to note and be prepared to share. Malin is an advocate for the disabled, and she'll open your eyes and mind to the possibilities of AI, machine-human integration, and embracing empathy in business. Now we have visual artist Mario Klingemann. Mario has been taking neural networks and turning them into his artist toolkit. You absolutely must go check out his work at quasimondo.com. Mario is the perfect guest to wrap this episode of Machine Yearning. These interviews have all taken place at the Cannes Lions International Festival of Creativity. It would be easy in a business and technology-based podcast like Machine Yearning to skip over the role artists and the radical imagination plays in shaping AI, machine learning, and the talking internet.

Mario Klingemann, Everyone says the word neural network, but I don't believe anyone explains it well.

And you're very—

you're like an expert machine learning person. And so can you please explain it to me as if like I was just turning 6 years old in kindergarten and I was like, Mom, I love neural networks. Like, but she's like, what is a neural network? What is a neural network?

I'm not a computer scientist. Okay. But the way I understand it. So actually, let me give you a metaphor. Please imagine a laundry machine. The one where you put in all your jeans and socks and everything, and then you wash it and then you pull it out. And that is your data, actually. And maybe— well, I don't do the laundry that often, but usually things are kind of intertwined and twisted and something. But imagine every piece that is in there is data, and the jeans is the dogs, and whatever you pull over is the cats, and the socks are, I don't know, mobile phones, and you— now the machine tries to tell them apart. And what the neural network does is kind of find the best way to untwist this ball of laundry. So in the end, it lays them all out nicely in a certain plane, and then it can say just, okay, on the left is, is my jeans, then next to it is the pullover. So, but in the end, it's like you untwisting the laundry by a mathematical operation. And so, but the data is kind of hanging together. The data is the leg of your jeans, which should stay together. You don't want to rip it apart. And so yeah, it's kind of a weird rotation transformation. That's all that happens inside of these neural networks in a way, a way to untwist or twist the data So in the end, the model can just do very simple kind of cuts and say everything that's left of this line is category A and everything that's to the right is category B. And yeah, so twisting and turning and—

That's amazing.

Not sure if that helped.

No, helped me a ton.

Okay.

What is the most inspiring thing that you're excited about?

I actually, I don't like the word inspired. That's, uh, why don't you start there?

I think it's a great, great answer. Why don't you like the word inspired?

Ah, because it's kind of being misused. So I, I get that a lot, that people say I was inspired by something, but in the end it's just more like, oh, I saw this and I copied it. And inspiration in my eyes is something that You see something and then you get a different idea. For me, the most important is to try to find inspiration outside the domain that you are in or that where you're trying to something to solve.

If inspiring is copying, then you're inspired about things that you've never seen before, then how do you find things you've never seen?

Well, you have to give serendipity a chance. You go to places where things are Well, where you never know what to expect. So personally, I love going to flea markets, old bookstores, thrift stores, where you never know what you might get.

So for all of us to be more creative, it's really manufacturing randomness and serendipity with a curious look on it and making sure you're not looking at the same things every day.

Yes, try to burst your filter bubble. Try to, like, for example, I actively try now to follow people that are doing things that I have no idea about and/or are totally like in a different culture, even like people that I disagree with, but just to not kind of get stale into constantly getting confirmation. And because yes, there's often some, like even if it's a horrible person, they might have something that even out of context taken triggers some idea in me. But if I always just get confirmation in my beliefs, then usually that— well, I need challenge.

What area do you love the most?

Uh, well, I love creating visuals, so I'm, I'm very— I would say I'm a very visual person. So everything from images, videos.

Specifically about images, what about it do you think is so interesting in the machine learning world as it relates to images?

Well, images are kind of a complex thing, right? Because an image can— well, there's the cliché saying an image can say more than 1,000 words. And that's true because there's a lot of information contained in an image. So an image can evoke emotions in you. It can— you can learn something from an image. An image can be interesting or not interesting, which is question I tried to actually— it's a core of my interest.

Well, how do you know what is interesting as a question to find interesting?

So there's something about images that cannot put a word on because it totally depends on what you have seen before. But it's definitely this thing like something you have not seen before or that in your understanding of the world does not make sense or does not fit together. This is— these are things that usually come to your attention because it It might be dangerous, it might be something you want to eat. So the problem was for a long time, this kind of complex information, like how do you analyze an image and figure out if there's something in it that is interesting, was not— you could not tackle using the classic techniques. But since neural networks are kind of more modeled after our visual perception system, they behave in a similar way. And suddenly you can actually turn an image into a bunch of very few numbers and then you can start mathematically comparing them.

If I'm an artist or someone making something, is it possible for me to know what people are interested in now based on all the data and, and understand if what I just made is different enough to be interesting?

Well, you can measure if what you do now is different enough, is different to what is done around you, that is possible. If it's interesting, I would say you lack the information about that. So you would have to make some tests soon. So there are no universal models. That's the other thing. So usually you have to cater, train these models for the question you want to ask. That's the tricky thing.

It always comes down to asking the right question. Yeah.

And especially, for example, in neural networks, you also have kind of the the separation sometimes between content and style. So there's this, like in a chemical process, you can kind of distill something like certain elements that make this up.

What is the difference between content and style?

Style is everything that is concerned with superficial layers like color and texture and like this type of brushstrokes. And content is actually the semantic meaning, saying like, oh, I see a dog there, I see a house. And neural networks are able to in some way tell them apart. And then it allows you to do things with that, like style transfer.

Is there style in writing? Because like commas or breaks or word structure or sentence structure.

Choice of vocabulary, absolutely. So adverbs. You can statistically, like you take the work of an unknown author and map it, like analyze it by length of sentences. Lengths of words, kind of size of vocabulary, and can make a map. And then you distinguish prose from poetry and certain authors. So you get these clusters. And so in some way, I believe, yes, you could do style transfer. I mean, you know, the technique of style transfer. So you take two images and you separate them into style and content, and then you exchange them, and then you can have something like a portrait look like a painting by Van Gogh. I believe you can do the same with text. So you could take the Bible and a detective story and then make a new Bible, have the detective story read like it's written like the Bible and the opposite way. And well, I haven't seen that done yet, but I believe it will be possible. Or maybe somebody has done it and just missed it. Or with sound, sound, the same thing. So you can say something and but you will actually that's happening already. So then you can sound like somebody else. Because again, like, your voice is kind of the words you say, is the content or kind of the low frequency, the structure. But then your high frequency and harmonics, that is what makes up your voice personality, and you can interchange that.

So when I saw Google Duplex, then basically what they innovated on with John Legend's voice, or it sounding like someone, is that they now have a machine that can copy style.

Is that what happened?

Yeah, yeah. So there, this is just happening right now. So you can start singing like Aretha Franklin or so, maybe, or whoever you like. So this is coming, definitely. Yeah, it would be interesting for pop music.

I mean, for sure.

Thank you very much.

Hey, was a pleasure. Absolutely.

All right, thanks for listening, and thanks to the folks at LivePerson. Our episodes from Cannes wouldn't have been the same without their tremendous support. Get in touch on Twitter @Assist. DMs are open. We're super interested to hear who you think should appear on the podcast. Please subscribe so you never miss an episode and share this with someone who cares about how we make sense of these changing times. Machine learning by Assist. Is made by Paul Chufo and Michael Elsesser for Limina House. Have a great day!