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Sam Harris | #292 - How Much Does the Future Matter?
2022-08-14
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<p>Sam Harris speaks with William MacAskill about his new book, <a href="https://www.amazon.com/What-Owe-Future-William-MacAskill/dp/1541618629"><em>What We Owe the Future.</em></a> They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.</p>
<p><strong>William MacAskill </strong>is an Associate Professor in Philosophy and Research Fellow at the Global Priorities Institute, University of Oxford. He is one of the primary voices in a philanthropic movement known as “effective altruism” and the co-founder of three non-profits based on effective altruist principles: <a href="https://www.givingwhatwecan.org/">Giving What We Can</a>, <a href="https://80000hours.org/">80,000 Hours</a>, and the <a href="https://www.centreforeffectivealtruism.org/">Centre for Effective Altruism</a>. He is also the Director of the Forethought Foundation for Global Priorities Research and the author of <a href="https://www.amazon.com/gp/product/1592409660?ie=UTF8&amp;tag=wwwsamharris03-20&amp;camp=1789&amp;linkCode=xm2&amp;creativeASIN=1592409660"><em>Doing Good Better: Effective Altruism and a Radical New Way to Make a Difference</em></a>.</p>
<p>Website: <a href="http://t.co/Db3VE8qSyV?amp=1">williammacaskill.com</a></p>
<p>Twitter: <a href="https://twitter.com/willmacaskill?lang=en">@willmacaskill</a></p>
<p>Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the <a href="http://wakingup.com/">Waking Up</a> app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.</p>
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 #292 - How Much Does the Future Matter?

 A Conversation with William MacAskill

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 August 14, 2022

 To gain access to ALL full-length episodes, you'll need to subscribe. If you’re already subscribed and on the private RSS feed, the podcast logo should appear RED. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe .

 Sam Harris speaks with William MacAskill about his new book, What We Owe the Future. They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.

 William MacAskill is an Associate Professor in Philosophy and Research Fellow at the Global Priorities Institute, University of Oxford. He is one of the primary voices in a philanthropic movement known as “effective altruism” and the co-founder of three non-profits based on effective altruist principles: Giving What We Can , 80,000 Hours , and the Centre for Effective Altruism . He is also the Director of the Forethought Foundation for Global Priorities Research and the author of Doing Good Better: Effective Altruism and a Radical New Way to Make a Difference.

 Website: williammacaskill.com

 Twitter: @willmacaskill

 Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.

 If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe .

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To gain access to ALL full-length episodes, you'll need to subscribe. If you’re already subscribed and on the private RSS feed, the podcast logo should appear RED. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe. 
Sam Harris speaks with William MacAskill about his new book,  What We Owe the Future.  They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.
 William MacAskill is an Associate Professor in Philosophy and Research Fellow at the Global Priorities Institute, University of Oxford. He is one of the primary voices in a philanthropic movement known as “effective altruism” and the co-founder of three non-profits based on effective altruist principles: Giving What We Can, 80,000 Hours, and the Centre for Effective Altruism. He is also the Director of the Forethought Foundation for Global Priorities Research and the author of  Doing Good Better: Effective Altruism and a Radical New Way to Make a Difference .
Website: williammacaskill.com 
Twitter: @willmacaskill 
Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.
If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.
Sam Harris | #292 - How Much Does the Future Matter?
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#292 — How Much Does the Future Matter?
2022-08-14
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<div><p>Sam Harris speaks with William MacAskill about his new book, <a href="https://www.amazon.com/What-Owe-Future-William-MacAskill/dp/1541618629"> <em>What We Owe the Future.</em></a> They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.</p><p>If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at <a href="http://samharris.org/subscribe">samharris.org/subscribe</a>.</p><p>Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the <a href="http://wakingup.com/">Waking Up</a> app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.</p></div>
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<div><p>Sam Harris speaks with William MacAskill about his new book, <a href="https://www.amazon.com/What-Owe-Future-William-MacAskill/dp/1541618629"> <em>What We Owe the Future.</em></a> They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.</p><p>If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at <a href="http://samharris.org/subscribe">samharris.org/subscribe</a>.</p><p>Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the <a href="http://wakingup.com/">Waking Up</a> app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.</p></div>
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 Sam Harris speaks with William MacAskill about his new book, What We Owe the Future. They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.

 If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

 Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.

 … continue reading

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 #292 — How Much Does the Future Matter?

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 Sam Harris speaks with William MacAskill about his new book, What We Owe the Future. They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics.

 If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

 Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it.

 … continue reading

 394 episodes

 # Science # Philosophy # Intellectual Dark Web # Currentevents # Ethics # Neuroscience # Politics # Samharris # Waking Up with Sam Harris # Sam Harris # News # Society # Religion

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#292 — How Much Does the Future Matter?
 Sam Harris speaks with William MacAskill about his new book,  What We Owe the Future.  They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe. Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it. 
  
394 episodes
   
 Sam Harris speaks with William MacAskill about his new book,  What We Owe the Future.  They discuss the philosophy of effective altruism (EA), longtermism, existential risk, criticism of EA, problems with expected-value reasoning, doing good vs feeling good, why it's hard to care about future people, how the future gives meaning to the present, why this moment in history is unusual, the pace of economic and technological growth, bad political incentives, value lock-in, the well-being of conscious creatures as the foundation of ethics, the risk of unaligned AI, how bad we are at predicting technological change, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe. Learning how to train your mind is the single greatest investment you can make in life. That’s why Sam Harris created the Waking Up app. From rational mindfulness practice to lessons on some of life’s most important topics, join Sam as he demystifies the practice of meditation and explores the theory behind it. 
  
394 episodes
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#292 — How Much Does the Future Matter?
https://player.fm/series/making-sense-with-sam-harris/ep-292-how-much-does-the-future-matter
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Making Sense
2024-03-31
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<div><div><p>A New York Times New and Noteworthy Book</p><p>From the bestselling author of Waking Up and The End of Faith, an adaptation of his wildly popular, often controversial podcast</p><p>"Sam Harris is the most intellectually courageous man I know, unafraid to speak truths out in the open where others keep those very same thoughts buried, fearful of the modish thought police. With his literate intelligence and fluency with words, he brings out the best in his guests, including those with whom he disagrees." -- Richard Dawkins, author of The Selfish Gene</p><p>"Civilization rests on a series of successful conversations." --Sam Harris</p><p>Sam Harris--neuroscientist, philosopher, and bestselling author--has been exploring some of the most important questions about the human mind, society, and current events on his podcast, Making Sense. With over one million downloads per episode, these discussions have clearly hit a nerve, frequently walking a tightrope where either host or guest--and sometimes both--lose their footing, but always in search of a greater understanding of the world in which we live. For Harris, honest conversation, no matter how difficult or controversial, represents the only path to moral and intellectual progress.</p><p>This book includes a dozen of the best conversations from Making Sense, including talks with Daniel Kahneman, Timothy Snyder, Nick Bostrom, and Glenn Loury, on topics that range from the nature of consciousness and free will, to politics and extremism, to living ethically. Together they shine a light on what it means to "make sense" in the modern world.</p></div></div>||||I|||| Sign in
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 Making Sense : Conversations on Consciousness, Morality, and the Future of Humanity         
          
 Sam Harris          
 HarperCollins Publishers , 2020 - History - 444 pages         
          
 A New York Times New and Noteworthy Book         
          
 From the bestselling author of Waking Up and The End of Faith, an adaptation of his wildly popular, often controversial podcast        
          
 "Sam Harris is the most intellectually courageous man I know, unafraid to speak truths out in the open where others keep those very same thoughts buried, fearful of the modish thought police. With his literate intelligence and fluency with words, he brings out the best in his guests, including those with whom he disagrees." -- Richard Dawkins, author of The Selfish Gene    
          
 "Civilization rests on a series of successful conversations." --Sam Harris         
          
 Sam Harris--neuroscientist, philosopher, and bestselling author--has been exploring some of the most important questions about the human mind, society, and current events on his podcast, Making Sense. With over one million downloads per episode, these discussions have clearly hit a nerve, frequently walking a tightrope where either host or guest--and sometimes both--lose their footing, but always in search of a greater understanding of the world in which we live. For Harris, honest conversation, no matter how difficult or controversial, represents the only path to moral and intellectual progress.
          
 This book includes a dozen of the best conversations from Making Sense, including talks with Daniel Kahneman, Timothy Snyder, Nick Bostrom, and Glenn Loury, on topics that range from the nature of consciousness and free will, to politics and extremism, to living ethically. Together they shine a light on what it means to "make sense" in the modern world.    
          
 

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 Sam Harris  
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 Sam Harris  
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 Sam Harris  
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 View all »

 About the author (2020)

 Sam Harris is the author of the bestselling books The End of Faith, Letter to a Christian Nation, The Moral Landscape, Free Will, and Lying. The End of Faith won the 2005 PEN Award for Nonfiction and his work has been published in more than 20 languages. He has written for the New York Times, The Los Angeles Times, the Economist, the Times (London), the Boston Globe, the Atlantic, the Annals of Neurology, and elsewhere. He received a degree in philosophy from Stanford University and a Ph.D. in neuroscience from UCLA.

 Bibliographic information

 Title Making Sense: Conversations on Consciousness, Morality, and the Future of Humanity
 Author Sam Harris  
 Publisher HarperCollins Publishers, 2020 
 ISBN 0062857789, 9780062857781 
 Length 444 pages  
 Social Science › Anthropology › Cultural & Social 
  
 History / Social History 
 Subjects Language Arts & Disciplines / Rhetoric 
 Psychology / Social Psychology 
 Social Science / Anthropology / Cultural & Social 
 Social Science / Sociology / General 
 Social Science / Sociology / Social Theory 
      
 Export Citation BiBTeX EndNote RefMan 
 


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A New York Times New and Noteworthy Book From the bestselling author of Waking Up and The End of Faith, an adaptation of his wildly popular, often controversial podcast "Sam Harris is the most intellectually courageous man I know, unafraid to speak truths out in the open where others keep those very same thoughts buried, fearful of the modish thought police. With his literate intelligence and fluency with words, he brings out the best in his guests, including those with whom he disagrees." -- Richard Dawkins, author of The Selfish Gene "Civilization rests on a series of successful conversations." --Sam Harris Sam Harris--neuroscientist, philosopher, and bestselling author--has been exploring some of the most important questions about the human mind, society, and current events on his podcast, Making Sense. With over one million downloads per episode, these discussions have clearly hit a nerve, frequently walking a tightrope where either host or guest--and sometimes both--lose their footing, but always in search of a greater understanding of the world in which we live. For Harris, honest conversation, no matter how difficult or controversial, represents the only path to moral and intellectual progress. This book includes a dozen of the best conversations from Making Sense, including talks with Daniel Kahneman, Timothy Snyder, Nick Bostrom, and Glenn Loury, on topics that range from the nature of consciousness and free will, to politics and extremism, to living ethically. Together they shine a light on what it means to "make sense" in the modern world.
Making Sense
https://books.google.com/books/about/Making_Sense.html?id=Fp-uywEACAAJ
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<div><article><div><div><div><h2>Redesigning for a more fine-tuned and immersive listening.</h2><div><a href="https://medium.com/@sanyamjain18?source=post_page-----e0b9c8f22a2a--------------------------------"><div><p></p></div></a><a href="https://uxdesign.cc/?source=post_page-----e0b9c8f22a2a--------------------------------"><div><p></p></div></a></div></div><div><p>As an avid listener of podcasts, I have always felt that podcast listening experience lacks imagination as they are either tied within the music app or ‘dedicated’ podcast apps which are not very different.</p><p>In reality, listening to songs vs podcasts are two entirely different user experiences. Here, I attempt to provide a more <strong>fine-tuned</strong> and <strong>immersive</strong> podcast listening experience.</p></div></div><div><p>The history of podcasts dates back to 1999 with the development of the Real Simple Syndication Feed (RSS) and it’s been around since then in some form or another. In 2005, with the entry of Apple into the market increased the accessibility of podcasts all over the world. Ever since the listener numbers have witnessed a steady rise with 2019 being considered the breakout year for podcasting.</p><blockquote><p>The name podcasts are the combination of ‘iPod’ and ‘broadcast.’</p></blockquote><p>By podcasting, you can close off the distance between you and your target market. It ensures you get a closer relationship with your audience by providing informative, engaging, and frequently updated content. This project is an inspection of the roadblocks and thereby proposing a design solution for making podcast realize their <strong>potential as the future of radio.</strong></p><h2>Identifying the problem</h2><p>Historically we had “audio players” that played all audio formats be it clips, songs, or podcasts. For songs, over time the audio player has metamorphosed into a “music player” in apps like Apple Music, Spotify with features like synced lyrics, song insights, better queuing whereas the podcast ecosystem still relies on the “audio player” for playback.</p><p>My attempt is to change that and envision <strong>an explicit “Podcast Player”</strong>.</p><figure><figcaption>Any redesign starts with a problem with the existing product!</figcaption></figure><p>Let’s inspect the major problems with the existing system.</p><ol><li>Listening Experience —<br/>The length of a podcast episode can range anywhere from 5 min to several hours and the <strong>chances of losing attention</strong> are very high so when you get back to resuming an episode some sort of <strong>cues to remind the previous section</strong> would be helpful.</li><li>Monetization — <br/>Although the podcast market is rapidly growing it still suffers from the <strong>problem of being under-monetized</strong>, the rates for an advertisement plug in a podcast episode are much lower than other digital mediums.</li></ol><h2>Goal</h2><blockquote><p>To envision an explicit “Podcast Playing Experience”.</p></blockquote><h2>Scope &amp; Constraint</h2><p>The podcast market is witnessing serious growth. A product that can provide a tailored experience to the rookie, as well as veteran listeners, shall act as a catalyst to that growth.</p><p>Since many podcasters are independent creators a product that can help raise their revenues will effectively be a promoter of the format itself.</p><blockquote><p>2019 has been considered a breakout year for podcasting, according to Edison Research’s <a href="https://www.edisonresearch.com/infinite-dial-2019/">Infinite Dial</a> report.</p></blockquote><p>With a big problem comes big constraints, here the biggest constraint was the fact that people mostly listen to a podcast while engaged in some other activity and barely interact with the device or the app where it is being played. This meant we had to approach the problem where we don’t wholly require their attention.</p><figure><figcaption>Biggest constrain — Adding enough value to make the user pick up their phone. Img Src: <a href="https://www.edisonresearch.com/the-podcast-consumer-2019/">Edison Research</a></figcaption></figure></div><div><div><h2>Solving Problem 1 — <em>Improving the listening experience</em></h2><h2>1. Introducing contextual cards</h2><p><em>“Adding context to content”, </em>often being unaware of certain things being spoken you end up googling it, which makes you leave the app distracting from what’s being currently spoken. This is where visual cue cards come in.</p><h2>2. Defining the cards</h2><p>Taking this concept following categories of cards were created.</p><ul><li><strong>Information</strong>: Unfamiliar terms &amp; visuals can be described here with an option to add a weblink.</li><li><strong>Quote: </strong>Highlights an important piece of dialogue or advice given by the speaker/guest.</li><li><strong>Connect: </strong>Quickly connect with the guests.</li></ul></div><div><blockquote><p><strong>Note</strong>: I have chosen the design system of Google Podcast for present my idea.</p></blockquote><h2>3. Re-designing the Interface</h2><p>Existing podcast playing screens barely differentiate themselves from music playing screens. My approach was to integrate the cards within that space without compromising on the overall functionality.</p><figure></figure><p><strong>(I) Finding Space for the Cards<br/></strong>Typically cover art occupies the majority of the screens which makes sense for songs as they add personality+customisation to the content. This logic doesn’t apply to the podcast, because often the user is just listening to yet another episode of a show he is already invested in.</p><figure><figcaption>The <strong>cover art</strong> along with the episode+show name is <strong>placed on the top section of the screen</strong> occupying lesser space and the cards get prominently displayed onscreen.</figcaption></figure><p><strong>(II) Placing essential components<br/></strong>Here we divide the components into two groups with</p><ol><li>Pause, forward/rewind &amp; the progress bar <strong>placed on-screen</strong> for direct access</li><li>whereas additional options of the playback speed, sleep, info, playlist &amp; share the episode neatly <strong>open upon clicking the meatballs menu</strong>.</li></ol><figure></figure><p><em>Thought behind the change: <br/>The additional options don’t take up any new space on the screen as to not disturb the ‘immersive experience’, but once an option from the meatball menu tapped then it expands as now the user has committed to that choice and would not mind the visual disturbance caused. This small maneuver gives the user time to think.</em></p><h2>4. Defining Interactions</h2><h2>(I) Progress bar into a slider</h2><p>The pre-defined role of the progress bar included —<br/>Providing a visual sense of the time elapsed and skipping through an episode by dragging the knob.</p><p><em>What do we change?</em></p><ul><li>We <strong>attach pins </strong>in the shape of smaller knobs along the length of an episode.</li><li>These pins are linked to individual cards and upon overlap with the now playing<em> </em>knob<em> </em>get activated and display the linked card on the screen.</li><li>The two possible behaviors are:<br/>(i) knobs automatically get activated as the track progresses<br/>(ii) the user can intentionally drag to that location</li></ul><figure></figure><h2><strong>(II) Swipeable Cards</strong></h2><p>Simply swipe through the cards <strong>without affecting the playback</strong>, this allows a <strong>preview</strong> of the episode with additional options to</p><ul><li>view all cards in a stacked manner.</li><li>jump to a section of an episode by tapping on the timestamp on the top left corner of the card.</li><li>bookmark the card with the timestamp.</li><li>share the card with social media.</li><li>undo (gets activated when a sudden movement of cards is detected).</li></ul></div><div><figure></figure><figure><figcaption>Swipeable Cards + Anatomy of the Screen</figcaption></figure></div><div><h2>5. Feedback, research, and iteration</h2><h2>Feedback</h2><p>After going through the above process, it was time to seek feedback and test out the concept. For this purpose, I approached my mentor </p><p>, and he highlighted some important pain points —</p><ul><li>Who will create these cards?</li><li>How will you convince podcast hosts to package things in your format?</li><li>What can you do about the monetization of older podcast episodes?</li></ul><h2>Utilizing the Feedback</h2><p>I realized the missing aspect while developing the idea was in-depth research around podcasts in terms of market trends, interests of creators/platform as well as demographics of listeners. What followed was —</p><h2>Step 1: Reaching out to people and understanding their concerns.</h2><p>Speaking to casual and avid listeners of podcasts highlighted some useful things like</p><ul><li>The mode of listening i.e, active/passive listening greatly depends on the podcast they are listening to.</li><li>In some cases, listening is complemented by active note-taking.</li><li>It validated the hypothesis I made about the problem with podcasts.</li></ul></div><div><figure></figure><figure><figcaption>The struggle to find podcast listeners :P</figcaption></figure></div><div><h2>Step 2: Conducting in-depth online research.</h2><p>This was most useful and gave us our second major problem to solve, the issue of under monetization of this medium.</p><p><em>I talk about it in depth later in this post.</em></p><h2>Step 3: Competitive Research</h2><ul><li>I discovered apps like <a href="https://www.entale.co/">Entale</a> &amp; <a href="https://www.adorilabs.com/">Adori</a>, they have been trying ‘enhanced podcast’ for a while.</li><li>Studying their approach gave me ideas about making a better solution.</li></ul></div><div><figure></figure><figure></figure><figure><figcaption>Some clicks from the process. Doing it for the first time it was quite a mess.</figcaption></figure></div></div><div><h2>Solving Problem 2— Monetisation</h2><h2>1. Increasing Discoverability</h2><ul><li>The concept of cards is customized to add meaning and context for the podcast format, thereby increasing the chances of people sharing them.</li><li>Second, most podcasters with limited technical skills find creating digital assets for promotion a chore, so these cards which can be simply created using templates can later come in use as digital assets for promotion.</li></ul><figure><figcaption>As the data above suggests, the top two ways people find out about podcasts are social media &amp; word of mouth. This is where cards with the ability to seamlessly be shared across social platforms steps in.</figcaption></figure><figure><figcaption>Sharing card on an Instagram story by clicking the share button on the card</figcaption></figure><h2>2. Ad–Cards</h2><blockquote><p>Advertisement is the primary source of revenue for digital content.</p></blockquote><p>In addition to the 3 cards, <strong>Information, Quote &amp; Connect </strong>which are presently part of the concept a fourth card for placing advertisements is introduced, it’s advantages being;</p><ul><li>Adding context and a direct link to reach the advertiser, to backup the audio placement in the show.</li><li>Older content which is unmonetized can also be monetized.</li><li>It can act as an incentive to buy a premium subscription, for an ad-free experience.</li></ul></div><div><blockquote><p>Finally, I will end this by answering two essential questions.</p></blockquote><h2>Q. What’s in it for listeners?</h2><p>— Flexibility to listen &amp; added context to understand better.</p><h2>Q. What’s in it for podcasters?</h2><p><strong>— </strong>Discoverability &amp; growth in revenue for eventual self-sufficiency.</p><figure></figure></div><div><p><em>Hi, this is Sanyam, I think through problems to arrive at a solution, which is engaging and works. If you’d like to get in touch, send me an email at </em><a href="mailto:sanyamjain18@gmail.com"><em>sanyamjain18@gmail.com</em></a><em> or tweet to me </em><a href="https://twitter.com/sanyamjain97"><em>@sanyamjain97</em></a></p></div></div></article></div>||||I||||          Open in app

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                        The Future of Google Podcast | Case Study

                        Redesigning for a more fine-tuned and immersive listening.

                                                Sanyam Jain

                                          ·

                                          Follow

                                          Published in

                                              UX Collective

                                          ·
                                        8 min read
                                          ·
                                        May 26, 2020

                                        --

                                                    Listen

                                          Share

                      As an avid listener of podcasts, I have always felt that podcast listening experience lacks imagination as they are either tied within the music app or ‘dedicated’ podcast apps which are not very different.

                      In reality, listening to songs vs podcasts are two entirely different user experiences. Here, I attempt to provide a more fine-tuned and immersive podcast listening experience.

                      The history of podcasts dates back to 1999 with the development of the Real Simple Syndication Feed (RSS) and it’s been around since then in some form or another. In 2005, with the entry of Apple into the market increased the accessibility of podcasts all over the world. Ever since the listener numbers have witnessed a steady rise with 2019 being considered the breakout year for podcasting.

                      The name podcasts are the combination of ‘iPod’ and ‘broadcast.’

                      By podcasting, you can close off the distance between you and your target market. It ensures you get a closer relationship with your audience by providing informative, engaging, and frequently updated content. This project is an inspection of the roadblocks and thereby proposing a design solution for making podcast realize their potential as the future of radio.

                      Identifying the problem

                      Historically we had “audio players” that played all audio formats be it clips, songs, or podcasts. For songs, over time the audio player has metamorphosed into a “music player” in apps like Apple Music, Spotify with features like synced lyrics, song insights, better queuing whereas the podcast ecosystem still relies on the “audio player” for playback.

                      My attempt is to change that and envision an explicit “Podcast Player”.

                      Any redesign starts with a problem with the existing product!

                      Let’s inspect the major problems with the existing system.

                       1. Listening Experience —
                          The length of a podcast episode can range anywhere from 5 min to several hours and the chances of losing attention are very high so when you get back to resuming an episode some sort of cues to remind the previous section would be helpful.
                       2. Monetization —
                          Although the podcast market is rapidly growing it still suffers from the problem of being under-monetized, the rates for an advertisement plug in a podcast episode are much lower than other digital mediums.

                      Goal

                      To envision an explicit “Podcast Playing Experience”.

                      Scope & Constraint

                      The podcast market is witnessing serious growth. A product that can provide a tailored experience to the rookie, as well as veteran listeners, shall act as a catalyst to that growth.

                      Since many podcasters are independent creators a product that can help raise their revenues will effectively be a promoter of the format itself.

                      2019 has been considered a breakout year for podcasting, according to Edison Research’s Infinite Dial report.

                      With a big problem comes big constraints, here the biggest constraint was the fact that people mostly listen to a podcast while engaged in some other activity and barely interact with the device or the app where it is being played. This meant we had to approach the problem where we don’t wholly require their attention.

                      Biggest constrain — Adding enough value to make the user pick up their phone. Img Src: Edison Research

                      Solving Problem 1 — Improving the listening experience

                      1. Introducing contextual cards

                      “Adding context to content”, often being unaware of certain things being spoken you end up googling it, which makes you leave the app distracting from what’s being currently spoken. This is where visual cue cards come in.

                      2. Defining the cards

                      Taking this concept following categories of cards were created.

                        * Information: Unfamiliar terms & visuals can be described here with an option to add a weblink.
                        * Quote: Highlights an important piece of dialogue or advice given by the speaker/guest.
                        * Connect: Quickly connect with the guests.

                      Note: I have chosen the design system of Google Podcast for present my idea.

                      3. Re-designing the Interface

                      Existing podcast playing screens barely differentiate themselves from music playing screens. My approach was to integrate the cards within that space without compromising on the overall functionality.

                      (I) Finding Space for the Cards
                      Typically cover art occupies the majority of the screens which makes sense for songs as they add personality+customisation to the content. This logic doesn’t apply to the podcast, because often the user is just listening to yet another episode of a show he is already invested in.

                      The cover art along with the episode+show name is placed on the top section of the screen occupying lesser space and the cards get prominently displayed onscreen.

                      (II) Placing essential components
                      Here we divide the components into two groups with

                       1. Pause, forward/rewind & the progress bar placed on-screen for direct access
                       2. whereas additional options of the playback speed, sleep, info, playlist & share the episode neatly open upon clicking the meatballs menu.

                      Thought behind the change:
                      The additional options don’t take up any new space on the screen as to not disturb the ‘immersive experience’, but once an option from the meatball menu tapped then it expands as now the user has committed to that choice and would not mind the visual disturbance caused. This small maneuver gives the user time to think.

                      4. Defining Interactions

                      (I) Progress bar into a slider

                      The pre-defined role of the progress bar included —
                      Providing a visual sense of the time elapsed and skipping through an episode by dragging the knob.

                      What do we change?

                        * We attach pins in the shape of smaller knobs along the length of an episode.
                        * These pins are linked to individual cards and upon overlap with the now playing knob get activated and display the linked card on the screen.
                        * The two possible behaviors are:
                          (i) knobs automatically get activated as the track progresses
                          (ii) the user can intentionally drag to that location

                      (II) Swipeable Cards

                      Simply swipe through the cards without affecting the playback, this allows a preview of the episode with additional options to

                        * view all cards in a stacked manner.
                        * jump to a section of an episode by tapping on the timestamp on the top left corner of the card.
                        * bookmark the card with the timestamp.
                        * share the card with social media.
                        * undo (gets activated when a sudden movement of cards is detected).

                          Swipeable Cards + Anatomy of the Screen

                      5. Feedback, research, and iteration

                      Feedback

                      After going through the above process, it was time to seek feedback and test out the concept. For this purpose, I approached my mentor

                          Gyan Lakhwani
                      , and he highlighted some important pain points —
                        * Who will create these cards?
                        * How will you convince podcast hosts to package things in your format?
                        * What can you do about the monetization of older podcast episodes?

                      Utilizing the Feedback

                      I realized the missing aspect while developing the idea was in-depth research around podcasts in terms of market trends, interests of creators/platform as well as demographics of listeners. What followed was —

                      Step 1: Reaching out to people and understanding their concerns.

                      Speaking to casual and avid listeners of podcasts highlighted some useful things like

                        * The mode of listening i.e, active/passive listening greatly depends on the podcast they are listening to.
                        * In some cases, listening is complemented by active note-taking.
                        * It validated the hypothesis I made about the problem with podcasts.

                          The struggle to find podcast listeners :P

                      Step 2: Conducting in-depth online research.

                      This was most useful and gave us our second major problem to solve, the issue of under monetization of this medium.

                      I talk about it in depth later in this post.

                      Step 3: Competitive Research

                        * I discovered apps like Entale & Adori, they have been trying ‘enhanced podcast’ for a while.
                        * Studying their approach gave me ideas about making a better solution.

                          Some clicks from the process. Doing it for the first time it was quite a mess.

                      Solving Problem 2— Monetisation

                      1. Increasing Discoverability

                        * The concept of cards is customized to add meaning and context for the podcast format, thereby increasing the chances of people sharing them.
                        * Second, most podcasters with limited technical skills find creating digital assets for promotion a chore, so these cards which can be simply created using templates can later come in use as digital assets for promotion.

                      As the data above suggests, the top two ways people find out about podcasts are social media & word of mouth. This is where cards with the ability to seamlessly be shared across social platforms steps in.

                      Sharing card on an Instagram story by clicking the share button on the card

                      2. Ad–Cards

                      Advertisement is the primary source of revenue for digital content.

                      In addition to the 3 cards, Information, Quote & Connect which are presently part of the concept a fourth card for placing advertisements is introduced, it’s advantages being;

                        * Adding context and a direct link to reach the advertiser, to backup the audio placement in the show.
                        * Older content which is unmonetized can also be monetized.
                        * It can act as an incentive to buy a premium subscription, for an ad-free experience.

                      Finally, I will end this by answering two essential questions.

                      Q. What’s in it for listeners?

                      — Flexibility to listen & added context to understand better.

                      Q. What’s in it for podcasters?

                      — Discoverability & growth in revenue for eventual self-sufficiency.

                      Hi, this is Sanyam, I think through problems to arrive at a solution, which is engaging and works. If you’d like to get in touch, send me an email at sanyamjain18@gmail.com or tweet to me @sanyamjain97

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                        The Future of Google Podcast | Case Study
                        Redesigning for a more fine-tuned and immersive listening.
                                                Sanyam Jain
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                                          Published in
                                              UX Collective
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                                        8 min read
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                                        May 26, 2020
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                      As an avid listener of podcasts, I have always felt that podcast listening experience lacks imagination as they are either tied within the music app or ‘dedicated’ podcast apps which are not very different.
                      In reality, listening to songs vs podcasts are two entirely different user experiences. Here, I attempt to provide a more fine-tuned and immersive podcast listening experience.
                      The history of podcasts dates back to 1999 with the development of the Real Simple Syndication Feed (RSS) and it’s been around since then in some form or another. In 2005, with the entry of Apple into the market increased the accessibility of podcasts all over the world. Ever since the listener numbers have witnessed a steady rise with 2019 being considered the breakout year for podcasting.
                      The name podcasts are the combination of ‘iPod’ and ‘broadcast.’
                      By podcasting, you can close off the distance between you and your target market. It ensures you get a closer relationship with your audience by providing informative, engaging, and frequently updated content. This project is an inspection of the roadblocks and thereby proposing a design solution for making podcast realize their potential as the future of radio.
                      Identifying the problem
                      Historically we had “audio players” that played all audio formats be it clips, songs, or podcasts. For songs, over time the audio player has metamorphosed into a “music player” in apps like Apple Music, Spotify with features like synced lyrics, song insights, better queuing whereas the podcast ecosystem still relies on the “audio player” for playback.
                      My attempt is to change that and envision an explicit “Podcast Player”.
                      Any redesign starts with a problem with the existing product!
                      Let’s inspect the major problems with the existing system.
                       1. Listening Experience —
                          The length of a podcast episode can range anywhere from 5 min to several hours and the chances of losing attention are very high so when you get back to resuming an episode some sort of cues to remind the previous section would be helpful.
                       2. Monetization —
                          Although the podcast market is rapidly growing it still suffers from the problem of being under-monetized, the rates for an advertisement plug in a podcast episode are much lower than other digital mediums.
                      Goal
                      To envision an explicit “Podcast Playing Experience”.
                      Scope & Constraint
                      The podcast market is witnessing serious growth. A product that can provide a tailored experience to the rookie, as well as veteran listeners, shall act as a catalyst to that growth.
                      Since many podcasters are independent creators a product that can help raise their revenues will effectively be a promoter of the format itself.
                      2019 has been considered a breakout year for podcasting, according to Edison Research’s Infinite Dial report.
                      With a big problem comes big constraints, here the biggest constraint was the fact that people mostly listen to a podcast while engaged in some other activity and barely interact with the device or the app where it is being played. This meant we had to approach the problem where we don’t wholly require their attention.
                      Biggest constrain — Adding enough value to make the user pick up their phone. Img Src: Edison Research
                      Solving Problem 1 — Improving the listening experience
                      1. Introducing contextual cards
                      “Adding context to content”, often being unaware of certain things being spoken you end up googling it, which makes you leave the app distracting from what’s being currently spoken. This is where visual cue cards come in.
                      2. Defining the cards
                      Taking this concept following categories of cards were created.
                        * Information: Unfamiliar terms & visuals can be described here with an option to add a weblink.
                        * Quote: Highlights an important piece of dialogue or advice given by the speaker/guest.
                        * Connect: Quickly connect with the guests.
                      Note: I have chosen the design system of Google Podcast for present my idea.
                      3. Re-designing the Interface
                      Existing podcast playing screens barely differentiate themselves from music playing screens. My approach was to integrate the cards within that space without compromising on the overall functionality.
                      (I) Finding Space for the Cards
                      Typically cover art occupies the majority of the screens which makes sense for songs as they add personality+customisation to the content. This logic doesn’t apply to the podcast, because often the user is just listening to yet another episode of a show he is already invested in.
                      The cover art along with the episode+show name is placed on the top section of the screen occupying lesser space and the cards get prominently displayed onscreen.
                      (II) Placing essential components
                      Here we divide the components into two groups with
                       1. Pause, forward/rewind & the progress bar placed on-screen for direct access
                       2. whereas additional options of the playback speed, sleep, info, playlist & share the episode neatly open upon clicking the meatballs menu.
                      Thought behind the change:
                      The additional options don’t take up any new space on the screen as to not disturb the ‘immersive experience’, but once an option from the meatball menu tapped then it expands as now the user has committed to that choice and would not mind the visual disturbance caused. This small maneuver gives the user time to think.
                      4. Defining Interactions
                      (I) Progress bar into a slider
                      The pre-defined role of the progress bar included —
                      Providing a visual sense of the time elapsed and skipping through an episode by dragging the knob.
                      What do we change?
                        * We attach pins in the shape of smaller knobs along the length of an episode.
                        * These pins are linked to individual cards and upon overlap with the now playing knob get activated and display the linked card on the screen.
                        * The two possible behaviors are:
                          (i) knobs automatically get activated as the track progresses
                          (ii) the user can intentionally drag to that location
                      (II) Swipeable Cards
                      Simply swipe through the cards without affecting the playback, this allows a preview of the episode with additional options to
                        * view all cards in a stacked manner.
                        * jump to a section of an episode by tapping on the timestamp on the top left corner of the card.
                        * bookmark the card with the timestamp.
                        * share the card with social media.
                        * undo (gets activated when a sudden movement of cards is detected).
                          Swipeable Cards + Anatomy of the Screen
                      5. Feedback, research, and iteration
                      Feedback
                      After going through the above process, it was time to seek feedback and test out the concept. For this purpose, I approached my mentor
                          Gyan Lakhwani
                      , and he highlighted some important pain points —
                        * Who will create these cards?
                        * How will you convince podcast hosts to package things in your format?
                        * What can you do about the monetization of older podcast episodes?
                      Utilizing the Feedback
                      I realized the missing aspect while developing the idea was in-depth research around podcasts in terms of market trends, interests of creators/platform as well as demographics of listeners. What followed was —
                      Step 1: Reaching out to people and understanding their concerns.
                      Speaking to casual and avid listeners of podcasts highlighted some useful things like
                        * The mode of listening i.e, active/passive listening greatly depends on the podcast they are listening to.
                        * In some cases, listening is complemented by active note-taking.
                        * It validated the hypothesis I made about the problem with podcasts.
                          The struggle to find podcast listeners :P
                      Step 2: Conducting in-depth online research.
                      This was most useful and gave us our second major problem to solve, the issue of under monetization of this medium.
                      I talk about it in depth later in this post.
                      Step 3: Competitive Research
                        * I discovered apps like Entale & Adori, they have been trying ‘enhanced podcast’ for a while.
                        * Studying their approach gave me ideas about making a better solution.
                          Some clicks from the process. Doing it for the first time it was quite a mess.
                      Solving Problem 2— Monetisation
                      1. Increasing Discoverability
                        * The concept of cards is customized to add meaning and context for the podcast format, thereby increasing the chances of people sharing them.
                        * Second, most podcasters with limited technical skills find creating digital assets for promotion a chore, so these cards which can be simply created using templates can later come in use as digital assets for promotion.
                      As the data above suggests, the top two ways people find out about podcasts are social media & word of mouth. This is where cards with the ability to seamlessly be shared across social platforms steps in.
                      Sharing card on an Instagram story by clicking the share button on the card
                      2. Ad–Cards
                      Advertisement is the primary source of revenue for digital content.
                      In addition to the 3 cards, Information, Quote & Connect which are presently part of the concept a fourth card for placing advertisements is introduced, it’s advantages being;
                        * Adding context and a direct link to reach the advertiser, to backup the audio placement in the show.
                        * Older content which is unmonetized can also be monetized.
                        * It can act as an incentive to buy a premium subscription, for an ad-free experience.
                      Finally, I will end this by answering two essential questions.
                      Q. What’s in it for listeners?
                      — Flexibility to listen & added context to understand better.
                      Q. What’s in it for podcasters?
                      — Discoverability & growth in revenue for eventual self-sufficiency.
                      Hi, this is Sanyam, I think through problems to arrive at a solution, which is engaging and works. If you’d like to get in touch, send me an email at sanyamjain18@gmail.com or tweet to me @sanyamjain97
                  Podcast
                  Redesign
                  Product Design
                  Case Study
                  Google
                        --
                        --
                    Follow
                    Written by Sanyam Jain
                      290 Followers
                      · Writer for
                          UX Collective
                    Designing Experience @Swiggy
                    Follow
                    Help
                    Status
                    About
                    Careers
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                    Blog
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                    Text to speech
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The future of Google Podcast
https://uxdesign.cc/the-future-of-google-podcast-e0b9c8f22a2a
https://www.seroundtable.com/past-present-and-future-of-structured-data-with-google-search-33214.html
Podcast: Past, Present and Future Of Structured Data With Google Search
2022-04-08
Barry Schwartz
<div><div>
	 <div>
	<p>
	</p>
	</div>
	 
	 
	 
	 <div>
	 <p></p><p>Lizzi Sassman and Martin Splitt brought on a special Google guest on their Google search off the record <a href="https://search-off-the-record.libsyn.com/structured-data-whats-it-all-about">podcast</a> to discuss structured data. The guest is named Ryan Levering who has been with Google for over 11 years working on structured data.</p><h2>Structured Data Past At Google</h2><p>In short, Ryan Levering explained that when he first started working on the structured data project, he worked on that <a href="https://www.seroundtable.com/google-data-highlighter-16078.html">legacy data highlighter tool</a> in Search Console. But early on, Google seemed to try to move away from requiring us to highlight or markup our content and wanted to use machine learning to figure it all out, which Google's Gary Illyes said <a href="https://www.seroundtable.com/google-structured-data-schema-dependence-24693.html">back in 2017</a> but kind of retracted <a href="https://www.seroundtable.com/google-schema-structured-data-here-to-stay-25293.html">in 2018</a>. So Google poured a lot of effort into machine learning to figure it out. </p><h2>Structured Data Present At Google</h2><p>But over time, Ryan said, it was "much easier to just ask people to give us their data rather than to pull it off of the web pages." "It's surprisingly more accurate," he added. So they then moved more resources into building out structured data and support documents for site owners to use and hand over the data.</p><p>But machine learning is now thrown out the window. Ryan said they still use it a lot for (1) sites that do not use structured data where Google wants to still show rich results for those, (2) for mistakes or abuse, so Google can verify what really the page is saying compared to the structured data. So Ryan said it is a "multiple pronged approach" to using structured data and machine learning for understanding it all.</p><p>So that is how Google uses it all today but what about the future.</p><h2>Structured Data Future At Google</h2><p>The "medium term future," Ryan said they plan on using structured data "not just visual treatments but actually help with more understanding on the page." Google has mentioned this before, that <a href="https://www.seroundtable.com/google-structured-data-ranking-factor-25510.html">structured data can help Google understand the page</a> but it is not a ranking factor. I guess Google will be working more on that. Plus, medium term future" Ryan said Google wants figure out "how to use structured data more universally in a lot of our features rather than just like here and there, scattered around."</p><p>Long term, Google said how Google can use structured data with how Google "interprets it in general into our internal graph." Ryan said he "would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself." Basically figuring out a "cleaner way to do data transfer between data providers and Google." How does Google do this, he said maybe by working with the large CMS platforms so they can build it into their platforms directly.</p><p>Here is the podcast embed:</p><p>Here are parts of the transcript:</p><blockquote>Ryan Levering : So, my introduction, when I started at Google, we were working on extraction from web pages. So like doing it via ML. So we came in, and the first thing I worked on was the data highlighter product, which is externally. We were looking at web pages and pulling structured data from unstructured text, and my whole team was very into the actual ML aspects of it. So how do we extract data, which in academic circles is often called "wrapper induction"? So when you take the-- you build a wrapper that can pull the data out of a template. So reverse engineer the database. But after several years of working on it, there was another project that was side by side that was extracting structured data, which became the core of what we use now. <p>And I became convinced, after talking to people for a long period of time that, it was much easier �to just ask people to give us their data rather than to pull it off of the web pages. It's surprisingly more accurate. There's other problems that can happen because of that, but it's generally an easier thing to do. And it's a lot less work for us, and it's a lot better for the provider. So I came to it from ML and seeing structured data as the enemy at first. And then I was won over as a good mechanism.</p><p>So machine learning is-- I see as like multiple prongs in our approach for how we get stuff. We want to use machine learning for cases where either we don't have more information where it's not provided for us. But it's always going to be easier to just have the data shown to us, I think. So we will try-- I think it's like a multi-tiered approach, where you have machine learning for cases where we don't have that data specifically. But then providers always have the option of giving us data, which usually improves accuracy, which usually gives better benefit for the actual provider. So I always see them as working side by side in an ideal world.</p><p>Most of our features over time migrate to that approach where we ingest it. Maybe we start with one approach where we're just using ML. And then we eventually add markups so people have control. Or it's the opposite way around. And we start-- we bootstrap with markup in an eco-system approach where people are giving us data. And then we enhance coverage of the feature by adding ML long run. So, I see them as very compatible. But it's always good to empower people who are giving you data, to have control over that. So I think it's really important that structured data in general is part of the overall strategy so the people can actually have some control over the content that we show.</p><p>The primary challenge is that we then have to figure out a way to verify that the structured data is accurate. And sometimes this is from actual abuse. And sometimes this is just because there's a problem with synchronicity. Sometimes people generate structured data for their websites and it becomes out of sync with the actual stuff that's being shown visually. We see a lot of both. So there needs to be other mechanisms to figure out some balancing act where those things are enforced. So that's the cost of structured data, I guess, is that extra checking.</p><p>Lizzi Sassman: Yeah, speaking of the work that has been done, what about the work that's to come, the next couple of years for structured data? If you were to give us a peek into the future, what is next for structured data?</p><p>Ryan Levering: In the medium-term, I think we're... I mean we continue to flesh out the structured data usage in terms of adding more features and looking into more ways we can use it in cooler things that are not just visual treatments but actually help with more understanding on the page, I think. And figuring out how to use structured data more universally in a lot of our features rather than just like here and there, scattered around. I think that's what we're looking at in a medium-term. </p><p>Long-term, I think that it's going to play a really interesting role at interacting with the way that we interpret it in general into our internal graph. So I would like to see more machine learning, figuring out-- I would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself. So I think that's a much cleaner approach, particularly for some of our structured data ingestion paths. So figuring out a way to get around the actual visual representation and figuring out ways to link the structured data with the web page but not necessarily embed it on the web page. So I think there's a cleaner way to do data transfer between data providers and Google.</p><p>I think that it will make it easier for plug-ins and CMSs to create that information particularly. Because I feel like a lot of the eco-system has moved in that direction where people aren't implementing the structured data themselves but rather are using content creation tools. I think it's becoming more important that we have mechanisms to work directly with those content creation tools to ingest the data in a programmatic way in order to make it fresher and easier.</p></blockquote><p>Forum discussion at <a href="https://twitter.com/googlesearchc/status/1512097877711110161">Twitter</a>.</p>	 </div>
	 	</div>||||I|||| Home > Google News > Google SEO > Podcast: Past, Present and Future Of Structured Data With Google Search

 Podcast: Past, Present and Future Of Structured Data With Google Search

 Apr 8, 2022 • 7:51 am | (0) by Barry Schwartz | Filed Under Google Search Engine Optimization
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 Lizzi Sassman and Martin Splitt brought on a special Google guest on their Google search off the record podcast to discuss structured data. The guest is named Ryan Levering who has been with Google for over 11 years working on structured data.

 Structured Data Past At Google

 In short, Ryan Levering explained that when he first started working on the structured data project, he worked on that legacy data highlighter tool in Search Console. But early on, Google seemed to try to move away from requiring us to highlight or markup our content and wanted to use machine learning to figure it all out, which Google's Gary Illyes said back in 2017 but kind of retracted in 2018. So Google poured a lot of effort into machine learning to figure it out.

 Structured Data Present At Google

 But over time, Ryan said, it was "much easier to just ask people to give us their data rather than to pull it off of the web pages." "It's surprisingly more accurate," he added. So they then moved more resources into building out structured data and support documents for site owners to use and hand over the data.

 But machine learning is now thrown out the window. Ryan said they still use it a lot for (1) sites that do not use structured data where Google wants to still show rich results for those, (2) for mistakes or abuse, so Google can verify what really the page is saying compared to the structured data. So Ryan said it is a "multiple pronged approach" to using structured data and machine learning for understanding it all.

 So that is how Google uses it all today but what about the future.

 Structured Data Future At Google

 The "medium term future," Ryan said they plan on using structured data "not just visual treatments but actually help with more understanding on the page." Google has mentioned this before, that structured data can help Google understand the page but it is not a ranking factor. I guess Google will be working more on that. Plus, medium term future" Ryan said Google wants figure out "how to use structured data more universally in a lot of our features rather than just like here and there, scattered around."

 Long term, Google said how Google can use structured data with how Google "interprets it in general into our internal graph." Ryan said he "would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself." Basically figuring out a "cleaner way to do data transfer between data providers and Google." How does Google do this, he said maybe by working with the large CMS platforms so they can build it into their platforms directly.

 Here is the podcast embed:

 Here are parts of the transcript:

 Ryan Levering : So, my introduction, when I started at Google, we were working on extraction from web pages. So like doing it via ML. So we came in, and the first thing I worked on was the data highlighter product, which is externally. We were looking at web pages and pulling structured data from unstructured text, and my whole team was very into the actual ML aspects of it. So how do we extract data, which in academic circles is often called "wrapper induction"? So when you take the-- you build a wrapper that can pull the data out of a template. So reverse engineer the database. But after several years of working on it, there was another project that was side by side that was extracting structured data, which became the core of what we use now.

 And I became convinced, after talking to people for a long period of time that, it was much easier �to just ask people to give us their data rather than to pull it off of the web pages. It's surprisingly more accurate. There's other problems that can happen because of that, but it's generally an easier thing to do. And it's a lot less work for us, and it's a lot better for the provider. So I came to it from ML and seeing structured data as the enemy at first. And then I was won over as a good mechanism.

 So machine learning is-- I see as like multiple prongs in our approach for how we get stuff. We want to use machine learning for cases where either we don't have more information where it's not provided for us. But it's always going to be easier to just have the data shown to us, I think. So we will try-- I think it's like a multi-tiered approach, where you have machine learning for cases where we don't have that data specifically. But then providers always have the option of giving us data, which usually improves accuracy, which usually gives better benefit for the actual provider. So I always see them as working side by side in an ideal world.

 Most of our features over time migrate to that approach where we ingest it. Maybe we start with one approach where we're just using ML. And then we eventually add markups so people have control. Or it's the opposite way around. And we start-- we bootstrap with markup in an eco-system approach where people are giving us data. And then we enhance coverage of the feature by adding ML long run. So, I see them as very compatible. But it's always good to empower people who are giving you data, to have control over that. So I think it's really important that structured data in general is part of the overall strategy so the people can actually have some control over the content that we show.

 The primary challenge is that we then have to figure out a way to verify that the structured data is accurate. And sometimes this is from actual abuse. And sometimes this is just because there's a problem with synchronicity. Sometimes people generate structured data for their websites and it becomes out of sync with the actual stuff that's being shown visually. We see a lot of both. So there needs to be other mechanisms to figure out some balancing act where those things are enforced. So that's the cost of structured data, I guess, is that extra checking.

 Lizzi Sassman: Yeah, speaking of the work that has been done, what about the work that's to come, the next couple of years for structured data? If you were to give us a peek into the future, what is next for structured data?

 Ryan Levering: In the medium-term, I think we're... I mean we continue to flesh out the structured data usage in terms of adding more features and looking into more ways we can use it in cooler things that are not just visual treatments but actually help with more understanding on the page, I think. And figuring out how to use structured data more universally in a lot of our features rather than just like here and there, scattered around. I think that's what we're looking at in a medium-term.

 Long-term, I think that it's going to play a really interesting role at interacting with the way that we interpret it in general into our internal graph. So I would like to see more machine learning, figuring out-- I would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself. So I think that's a much cleaner approach, particularly for some of our structured data ingestion paths. So figuring out a way to get around the actual visual representation and figuring out ways to link the structured data with the web page but not necessarily embed it on the web page. So I think there's a cleaner way to do data transfer between data providers and Google.

 I think that it will make it easier for plug-ins and CMSs to create that information particularly. Because I feel like a lot of the eco-system has moved in that direction where people aren't implementing the structured data themselves but rather are using content creation tools. I think it's becoming more important that we have mechanisms to work directly with those content creation tools to ingest the data in a programmatic way in order to make it fresher and easier.

 Forum discussion at Twitter.

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Lizzi Sassman and Martin Splitt brought on a special Google guest on their Google search off the record podcast to discuss structured data. The guest is named Ryan Levering who has been with Google for over 11 years working on structured data. Structured Data Past At Google In short, Ryan Levering explained that when he first started working on the structured data project, he worked on that legacy data highlighter tool in Search Console. But early on, Google seemed to try to move away from requiring us to highlight or markup our content and wanted to use machine learning to figure it all out, which Google's Gary Illyes said back in 2017 but kind of retracted in 2018. So Google poured a lot of effort into machine learning to figure it out.  Structured Data Present At Google But over time, Ryan said, it was "much easier to just ask people to give us their data rather than to pull it off of the web pages." "It's surprisingly more accurate," he added. So they then moved more resources into building out structured data and support documents for site owners to use and hand over the data. But machine learning is now thrown out the window. Ryan said they still use it a lot for (1) sites that do not use structured data where Google wants to still show rich results for those, (2) for mistakes or abuse, so Google can verify what really the page is saying compared to the structured data. So Ryan said it is a "multiple pronged approach" to using structured data and machine learning for understanding it all. So that is how Google uses it all today but what about the future. Structured Data Future At Google The "medium term future," Ryan said they plan on using structured data "not just visual treatments but actually help with more understanding on the page." Google has mentioned this before, that structured data can help Google understand the page but it is not a ranking factor. I guess Google will be working more on that. Plus, medium term future" Ryan said Google wants figure out "how to use structured data more universally in a lot of our features rather than just like here and there, scattered around." Long term, Google said how Google can use structured data with how Google "interprets it in general into our internal graph." Ryan said he "would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself." Basically figuring out a "cleaner way to do data transfer between data providers and Google." How does Google do this, he said maybe by working with the large CMS platforms so they can build it into their platforms directly. Here is the podcast embed: Here are parts of the transcript: Ryan Levering : So, my introduction, when I started at Google, we were working on extraction from web pages. So like doing it via ML. So we came in, and the first thing I worked on was the data highlighter product, which is externally. We were looking at web pages and pulling structured data from unstructured text, and my whole team was very into the actual ML aspects of it. So how do we extract data, which in academic circles is often called "wrapper induction"? So when you take the-- you build a wrapper that can pull the data out of a template. So reverse engineer the database. But after several years of working on it, there was another project that was side by side that was extracting structured data, which became the core of what we use now. And I became convinced, after talking to people for a long period of time that, it was much easier �to just ask people to give us their data rather than to pull it off of the web pages. It's surprisingly more accurate. There's other problems that can happen because of that, but it's generally an easier thing to do. And it's a lot less work for us, and it's a lot better for the provider. So I came to it from ML and seeing structured data as the enemy at first. And then I was won over as a good mechanism. So machine learning is-- I see as like multiple prongs in our approach for how we get stuff. We want to use machine learning for cases where either we don't have more information where it's not provided for us. But it's always going to be easier to just have the data shown to us, I think. So we will try-- I think it's like a multi-tiered approach, where you have machine learning for cases where we don't have that data specifically. But then providers always have the option of giving us data, which usually improves accuracy, which usually gives better benefit for the actual provider. So I always see them as working side by side in an ideal world. Most of our features over time migrate to that approach where we ingest it. Maybe we start with one approach where we're just using ML. And then we eventually add markups so people have control. Or it's the opposite way around. And we start-- we bootstrap with markup in an eco-system approach where people are giving us data. And then we enhance coverage of the feature by adding ML long run. So, I see them as very compatible. But it's always good to empower people who are giving you data, to have control over that. So I think it's really important that structured data in general is part of the overall strategy so the people can actually have some control over the content that we show. The primary challenge is that we then have to figure out a way to verify that the structured data is accurate. And sometimes this is from actual abuse. And sometimes this is just because there's a problem with synchronicity. Sometimes people generate structured data for their websites and it becomes out of sync with the actual stuff that's being shown visually. We see a lot of both. So there needs to be other mechanisms to figure out some balancing act where those things are enforced. So that's the cost of structured data, I guess, is that extra checking. Lizzi Sassman: Yeah, speaking of the work that has been done, what about the work that's to come, the next couple of years for structured data? If you were to give us a peek into the future, what is next for structured data? Ryan Levering: In the medium-term, I think we're... I mean we continue to flesh out the structured data usage in terms of adding more features and looking into more ways we can use it in cooler things that are not just visual treatments but actually help with more understanding on the page, I think. And figuring out how to use structured data more universally in a lot of our features rather than just like here and there, scattered around. I think that's what we're looking at in a medium-term.  Long-term, I think that it's going to play a really interesting role at interacting with the way that we interpret it in general into our internal graph. So I would like to see more machine learning, figuring out-- I would like to move to where we are adjusting more and more data through structured data-specific channels rather than necessarily conveying all of our information on the web page itself. So I think that's a much cleaner approach, particularly for some of our structured data ingestion paths. So figuring out a way to get around the actual visual representation and figuring out ways to link the structured data with the web page but not necessarily embed it on the web page. So I think there's a cleaner way to do data transfer between data providers and Google. I think that it will make it easier for plug-ins and CMSs to create that information particularly. Because I feel like a lot of the eco-system has moved in that direction where people aren't implementing the structured data themselves but rather are using content creation tools. I think it's becoming more important that we have mechanisms to work directly with those content creation tools to ingest the data in a programmatic way in order to make it fresher and easier.  Forum discussion at Twitter.
Podcast: Past, Present and Future Of Structured Data With Google Search
https://www.seroundtable.com/past-present-and-future-of-structured-data-with-google-search-33214.html