Sleepwalkers at CES



Secure I'd never been to Las Vegas before which is the difference between us. I've Been Vegas gets too many times. I could tell and didn't feel good to be in good hands with an old vegas handler. You one of the new things. Though for me was slots which I don't normally play play. I think subconsciously I was thinking about what Tristan Harris talked about in the first season of sleep walker former Google lower. Who told us that? Instagram is actually supposed to feel a lot like slot machine or the Tristan studied at the Stanford persuasion lab and told us about how casino architecture has influenced the development of highly addictive tape products like instagram. Interesting for me to actually see Vegas and the bright lights and the impossibility of escape firsthand not to mention the replicas of if the empire state building the canals of Venice Coliseum of Rome you know I was lucky enough to see the Seattle space needle for the first time. I didn't know that it was in Las Vegas. But doesn't we were there. We were there for C.. Es The consumer electronics show in this episode. Were actually going to talk about some of the coolest things we saw there. But we're going to focus focused more on the innovations that are at the intersection of technology and humanity rather than talk about you know infamous toilet. Paper dispensers run of the big reasons we went is because we you were invited by wave maker which is an agency part of WPP to do an interview on stage alive. PODCAST so to speak with Matt Monahan. The HATTON who is head of product at publishing and publishing is part of the Washington Post Orcas also an interesting case of a and action because they're forward thinking in terms of increasing the visibility of content through personalization. An optimizing everything from headlines to photo selection all using machine learning and those are things that really matter for journalists and readers. Yeah and this use of. Ai stands out to me because it provides a solution to real problem. How do you get eyeballs on the right content when there's just so much that said the issue of personalization does raise questions about what happens when machine thought to know US better than we know ourselves not to mention and what are the appropriate limits of how companies use AI and data about us? Yeah I can definitely streamline processes by detecting patterns that you know human beings cannot see or it can allow you to scale like tag hundreds of thousands of articles that again human beings just cannot do so greater efficiency is on one side of the spectrum and extremely attractive to people but on the other side. You have issues of taking humans out of the loop like the blackbox problem and authenticity in a world of deep fake so a question for businesses and users of technology is sort of when does Ai. Add to our experience experience and when does it maybe hold us back or take advantage of us for example from seeing news stories that we should see but maybe the algorithm doesn't think we want want to see it or that we won't click on it right in the old days. When everyone received a print newspaper on their doorstep? Everyone had the same front page in the same headlines Nowadays holidays when you log onto a news website or on social media everybody has a different version of the world and that is obviously positive for driving engagement but may not be so positive in terms of having conversations with the same facts about the same stories equally. We have to ask. Do we want articles where the headlines been written by Algorithm. ooh Do we prefer headlines written person. And that's something we talked about with Matt because all actually tested headline writing technology. Let's talk to Matt. Lucas says let's cut to the chase are really came out of a collaboration trying to better understand what actual journalists needed it. Can you talk a little bit more at the very beginning. You know we were just trying to solve problems for ourselves. Seven or eight years ago. We knew he had to make some pretty fundamental transformation to the post and to really prepare for the digital future. We didn't have the right tools to do it. And we couldn't really find the right tools on the market either. What we did was spent a lot of the journalist and the editor is trying to figure out what it was that make their lives easier? It's trying to figure out. How do you make journalists work better publish faster? What are the little things you can do? Inside of IT products make it easier easier for them to write stories or publish from there about four years ago when we started evolving into a commercial offering. Today we're running hundreds of websites around the world breath about twenty different countries. We're running companies like BP their internal communications as well as some of the marketing. We're running large broadcasters and all their live video and beauty and of course I was still running a lot of newspapers and news publishers. Like the post and many others around the world looking in publishing you know that. Ai Artificial intelligence in headlines MHM and there was a story in the Financial Times last year. We said forty percent of startups us. No whatsoever uh-huh so I bet it's probably higher so when we talk about using a Ohio when you talk about what we actually mean so it can span the range of technologies analogies from something like machine learning which is basically a way to use algorithms to take large sets of data in either uncover patterns in it or try to model away to predict a certain outcome. The two technologies like computer vision which you can use to look at images or video and extract information about them by recognizing patterns and trying to identify objects inside of them and so a lot of those technologies than when you put them together conform. Some really interesting workflows that you know in the past. You might have had us humans to do that. You can actually do much more simple automatically. was there a the titular business challenge or challenge the Washington Post that. You couldn't have sold if you hadn't been using AI. Any story that we right on Washington Post. We're mapping to a set of I two or three hundred topics maybe an example of one of those might be like congressional policy or narcotics crime. What you're trying to do is say if I look at all this content? I'm not just pulling specific words. I'm actually trying to figure out. What is this content about? What is the fundamental concept of this so you pick a set of articles? Let's say one hundred two thousand news articles in the case this example for the post and I see us. Humans of Micro Labor to do this training set and the goal is you're building an algorithm Based on a set of real data and so the humans are going there and saying this article. Yeah this is about congressional policy. Why because I know it is I read it? That's what it's about. This one's about narcotics crime time and this one's about soccer and so you train all these articles against that algorithm until finally the algorithm is basically sufficiently advanced to predict a new article that you put into it and determine it outcome with the same high probability of success that you're able to with human training now every time. A journalist Saves Saves publishes the story we're able to Parse over all the contents inside that story then we can predict the strength at which it's likely to belong to that topic. How do you create a better user experience in your case news experience for an individual consumer with the medicine? You can do a lot of interesting things we can figure out that. Hey this is something that they're interested in reading. Perhaps they'd like to read more in. It actually serves the signal into a recommendation Algorithms from your perspective where can businesses sort of harness the power of machine learning to really hone in on who their customer is and what that customer wants. We want to deliver more content to our readers leaders. Who Want to help them? Find more content that we've created. We have about nine hundred journalists at the Washington Post we write something like three or four hundred original stories today. So there's is a Lotta content there to get readers to all different content and to have them continue moving through your constant. You spent a lot of money to produce is really challenging. And so that's a great use case for personalization Shen but where you can make it really come alive is by having more sophisticated. Meditated more sophisticated information about that content. That's more likely to bring readers to it. And so that's where these machine learning remodels really come in handy. I think part of what's fun about this conversation is there's a lot of cases out there where average users you know. They imagine they see something like that. You see the boots on instagram. And you think Oh my God he's companies. Must you know indiscernible for magic right. There must be some crazy model out there doing this. And perhaps is there is but in a lot of ways you know. Your users aren't necessarily as aware of the advertising ecosystem data ecosystem and how these things tied together between platforms incites and I think as like industry professionals. We always kind of underestimate that fact and so the net effect is that users are completely surprised by this. I think you must be doing something completely on her to achieve it. When in fact you know it could be really simple data sharing and so the reason? I think that's important than when you do. Bill Technologies that actually utilize some these more sophisticated methods to build data sets. You have to be aware that your users you know first of all your users aren't going to necessarily anticipate the outcomes you can create and if you don't do a good job on the product side of making sure that you really think through the use case and how you're leveraging technology solve it you can generate unexpected outcomes. You know there was the example of the retailer who produced advertising flyers that were able to predict folks who are pregnant right. Even if some of those folks didn't necessarily know that themselves or hadn't shared it with with their family or their spouses.

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