51 episodes

The FYI - For Your Innovation Podcast offers an intellectual discussion on recent developments across disruptive innovation—driven by research, news, controversies, companies, and technological breakthroughs. Hosted by ARK Invest analyst James Wang, ARK and guests provide a unique perspective on how to best understand disruptive innovation.

FYI - For Your Innovation ARK Invest

    • Investing

The FYI - For Your Innovation Podcast offers an intellectual discussion on recent developments across disruptive innovation—driven by research, news, controversies, companies, and technological breakthroughs. Hosted by ARK Invest analyst James Wang, ARK and guests provide a unique perspective on how to best understand disruptive innovation.

    Introduction to ARK’s Big Ideas 2020

    Introduction to ARK’s Big Ideas 2020

    Join James Wang as he interviews Cathie Wood (CEO and CIO at ARK Invest) on our recently published Big Ideas 2020 report. On this podcast Cathie will share her perspective on some of the biggest breakthroughs ARK believes should not be missed in the coming year.
    What is ARK’s Big Ideas Report?
    This annual research report highlights the latest developments in innovation and offers some of our most provocative research conclusions for the coming year. ARK seeks to gain a deeper understanding of the convergence, market potential, and long-term impact of disruptive innovation by researching a global universe that spans sectors, industries, and markets. Today, we seem to be witnessing an acceleration in new technological breakthroughs. To enlighten investors on the impact of these breakthroughs and the opportunities they will create, we began publishing Big Ideas in 2017. This year’s version focuses on:

    Deep Learning — From Vision to Language
    Streaming Media — The Primary Technology Behind Content Distribution
    Electric Vehicles — Faster Adoption Than Most Think
    Automation — Increased Productivity and More Jobs
    3D Printing — An Underestimated Technology
    Autonomous Ridehailing — The Future of Transportation
    Aerial Drones — A Cost Saver and Potential Life Saver
    Next Generation DNA Sequencing — The Transformation of Oncology
    Biotech R&D Efficiency — The Convergence of Technologies in Healthcare
    Digital Wallets — The Transformation of Banking
    Bitcoin — An Evolution of Monetary Systems

     
    Tweetables:
    “The big surprise to me was that the biggest opportunity out there, in terms of drones, is food delivery.”— @CathieDWood
    “The convergence of DNA sequencing, AI, and Gene editing will reduce the price of Biotech R&D considerably, bringing margins potentially back to highs like we have seen in the 80s.”— @CathieDWood

    • 22 min
    Best of 2019: FYI Podcast with Elon Musk, George Church, and more

    Best of 2019: FYI Podcast with Elon Musk, George Church, and more

    As we wrap up 2019, we would like to thank everyone who listened to the FYI — For Your Innovation podcast. We received incredibly positive feedback for our podcast show and had some truly amazing guests in the past year, ranging from world class geneticists to founders and CEOs. In this final episode of 2019, we put together a “greatest hits compilation” of our five most popular episodes.
     
    1. On the Road to Full Autonomy with Elon Musk (EP11)
    Elon Musk talks about how his engineering background drives his decision making for Tesla and why he is so confident that Tesla will achieve full autonomy. On this podcast: Elon Musk, Tasha Keeney, Cathie Wood. (Listen to the Full Episode)
     
    2. Immunotherapy and the Race to Cure Cancer with Charles Graeber (EP21)
    For decades cancer was something that was treated rather than cured. Author of the book The Breakthrough: Immunotherapy and the Race to Cure Cancer, Charles Graeber unpacks the history of immunotherapy, why it remained on the fringes for so long, and why cancer might become a manageable disease. On this podcast: Charles Graeber and James Wang (Listen to the Full Episode)
     
    3. The Genomic Revolution with Prof. Dr. George Church (EP26)
    Professor George Church is one of the pioneers of modern genetics. We dive into the stage of genomics, next generation oncology, the security and regulation of genetic information, gene editing, and the increasing speed of the genomic revolution. On this podcast: George Church, Manisha Samy, Simon Barnett, and James Wang. (Listen to the Full Episode)
     
    4. Wright’s Law—Understanding Technology Cost Curves with Brett Winton (EP07)
    Brett Winton explains how Wright’s Law makes simple and robust predictions about technology cost declines, and why it’s at the center of ARK’s research. On this podcast: Brett Winton and James Wang (Listen to the Full Episode)
     
    5. Cerebras’ Wafer Scale Engine AI Chip with CEO Andrew Feldman (EP37)
    Andrew Feldman, co-founder and CEO of Cerebras, joins us to discuss the Wafer Scale Engine, or WSE, an AI chip that is 50 times larger than the largest chips produced by Nvidia and Intel. On this podcast: Andrew Feldman and James Wang (Listen to the Full Episode)
     
    We will be back in 2020 with new episodes and more exciting topics, because investing in innovation starts with understanding it. Until then, stay innovative!
     

    • 40 min
    The Beginning of Every Deep Learning Exercise With Manu Sharma and Brian Rieger

    The Beginning of Every Deep Learning Exercise With Manu Sharma and Brian Rieger

    In today’s episode, we welcome Manu Sharma and Brian Rieger from Labelbox, a private company which we believe is leading training data solution for machine learning. We have had many conversations on this show about artificial intelligence from a hardware and algorithm perspective, but data is just as important. All production AI systems are based on supervised learning, which requires large quantities of data to be labeled so that the algorithms can understand and compartmentalize it. In other words, data without labels can’t be used by most AI algorithms.
    While large internet companies like Google and Facebook have built custom tools in-house to help label and sort through their large troves of data, most enterprises have very few options. Labelbox aims to fill this gap by providing a scalable and easy-to-use tool to help companies convert their raw data into labeled data fit for machine learning algorithms. Today on the show, Manu and Brian get into the history of Labelbox, as well as the services it provides to its clients and the machine learning community. We talk about the tiers and iterations of Databox, its pricing structures, the various industries it supports, and what makes it stand out against its competition. We also cover some fascinating ground around human-in-the-loop systems, how a machine learning startup would train its AI and the difference between software 1.0 and 2.0. In our conversation, we also speak about Labelbox in relation to computer vision, drone technology, and labor ethics. Join us to get a taste of the many ways data and AI will continue to penetrate life and industry well into the foreseeable future.
     
    Key Points From This Episode:

    How Manu and Brian became friends through building an optimization system for airfoils.
    Manu’s experience working with data insights where he realized the need for data labeling.
    The connection between the rise of different machine learning algorithms and data labeling.
    Three labeling problems Labelbox solves in areas of tools, distribution, and management.
    Labelbox’s two formats: on-premises and cloud-based.
    Pricing structures for Labelbox which are tiered and correspond to the decisions it makes.
    Different industries that utilize computer vision which benefit from Labelbox.
    How Labelbox helped KeepTruckin build a dashcam data capturing system.
    Human-in-the-loop workflows
    How Labelbox believes it can take accuracy from 90% to 100%.
    Key differentiators of Labelbox regarding software, human support, and data management.
    The story of Expensify and how an AI-powered app trains its AI using humans and data.
    Data processing as the key differentiator between software 1.0 and 2.0.
    Whether the rise of transfer learning is detrimental to Labelbox as a business.
    The relevance of Labelbox and machine learning to modern drone technology and uses.
    Ethical considerations around human data labeling work conditions.
    The plus side of human data labeling: skills development and accessibility.
    Some of the third world English speaking regions where human data labeling is burgeoning.
    Situations where human data labeling falls under a mixture of experts and outsourced labor.

     
    Tweetables:
    “We’re seeing a massive adoption of deep learning technology across every industry where cameras or the human eye are involved in making decisions.” — @Riegerb
    “Labelbox is the only software platform that a customer would ever need in order to build, create, and manage the training of datasets for operating a machine learning pipeline.” @manuaero
    “What’s really fascinating is that in software 2.0, the way the machines learn is through a form of labeled data, and these labels are essentially decisions.” @manuaero

    • 53 min
    The Power of Deep Learning with Bryan Catanzaro from NVIDIA

    The Power of Deep Learning with Bryan Catanzaro from NVIDIA

    We are joined by Bryan Catanzaro, Vice President of Applied Deep Learning Research at NVIDIA. In his early career he built some of the original deep learning libraries and worked at Baidu in the specific field of deep speech. In 2016 he returned to NVIDIA and has been there since, exploring the ever-evolving field of deep learning at one of the industry leaders. We discuss conversational AI and the newest advancements in the field, Bryan’s thoughts on NVIDIA’s competition and what the market looks like currently. Bryan also weighs in on how far we are from a more general form of artificial intelligence and how far we can get by just scaling today’s technologies. We also cover autonomous driving, related software, hardware and frameworks and the impact of cloud computing on the field. For this informative chat be sure to listen in to the For Your Innovation podcast!
    Key Points From This Episode:

    An overview of how deep learning has changed over the last two decades.
    NVIDIA’s realizations around deep learning and the impact it would make.
    The deep learning team at NVIDIA and how it fits into the company as a whole.
    Connections to other companies and internal work within the NVIDIA sphere.
    Exciting projects at the company; conversational AI and graphics rendering!
    The data bottleneck and the hurdles to teaching machines to properly understand language.
    Scaling in large transformer networks, training and inference models for learning.
    Single and multi-node problems; creating solutions for different types of customers.
    The software and frameworks in the market right now and who is leading the race.
    The hardware side of machine learning and why Bryan emphasizes universal compatibility.
    NVIDIA’s autonomous driving efforts and what they are ultimately aiming for.
    How close are we to some form of general artificial intelligence?

     
    Tweetables: 
    “NVIDIA is also as you might remember, a very densely connected flat organization. Teams work together across org chart boundaries every day.” — @ctnzr
    “We are not just training models of the past we are inventing models of the future.” — @ctnzr

    • 48 min
    Streaming Wars with Nick Grous

    Streaming Wars with Nick Grous

    Isn’t everyone talking about streaming wars? This may seem like a new phenomenon, but it all started in 2007 when Netflix introduced streaming for the first time. With Disney Plus and Apple TV gaining traction, we are exploring what the future of streaming might look like and how it could shape the media landscape. On this episode Nick Grous, analyst at ARK Invest, shares his broad perspective and insightful knowledge on the current streaming wars taking place across the globe.
    On day one of their launch in November 2019, Disney Plus received over 10 million subscribers from just a handful of countries. Nick explains how the growing competition, like Disney Plus and Apple TV, impacts Netflix – the market leader in the streaming space. We explore some of the history behind streaming and why it took other companies over 12 years to start competing with Netflix. Lastly, we discuss how companies are expanding their business models and products in order to win a piece of the pie. Take a listen to this episode if you want to know who ARK believes might be best positioned in these streaming wars.
    Key Points from This Episode:

    How Netflix revolutionized streaming back in 2007.
    Who we believe are the main streaming competitors today.
    Find out how the launch of Disney Plus and Apple TV are faring in comparison to Netflix.
    Discover why it took the industry 12 years to respond seriously to the domination of Netflix.
    Why we Nick believes legacy companies today are responding with mergers and acquisitions.
    Find out what Netflix has done to scale more rapidly and globally than its competitors.
    Stepping into the ad-supported market versus Subscription Video on Demand (SVOD).
    Why live sport and live news still makes cable TV an attractive option for consumers.
    New strategies companies are implementing into their business models beyond streaming.
    Predictions for the future of the streaming space: Is Disney Plus going to win the war?

     
    Tweetables:
    “Before, Netflix was the undisputed king. Now, they have true competitors.” — @GrousARK
    “There is not going to be a single winner in this streaming war. There will be many winners.” — @GrousARK
    “This is also a way for companies to bring new users into an ecosystem beyond just streaming.” — @GrousARK

    • 21 min
    HD Maps for an Autonomous Future with Ro Gupta

    HD Maps for an Autonomous Future with Ro Gupta

    We are joined by Ro Gupta, the CEO of Carmera to discuss his contribution to an autonomous future. Carmera is a private company that makes real time HD maps for autonomous driving. We talk about his own history and early interest in transportation before diving into the development of digital maps and the apps that have spearheaded this technology. Ro explains data collection, safety policies, and some of the main challenges today. He also describes differences between the US and international markets, notably China. We discuss different solutions, how to share data across platforms, and how Carmera aims to grow in the future.
    Key Points From This Episode:

    The early roots of Ro’s interest and affinity for transportation technologies.
    Mapping technologies over the years and the shift towards HD maps. 
    Carmera’s stance towards LIDAR and data collection.  
    Aiming for the best historical and forward-thinking data set for vehicles.  
    Safety policy, trust, and verification.  
    Comparing the US markets to those abroad and the implications of global demand. 
    The biggest challenges currently facing Carmera and the HD maps market.  
    Issues with data across formats and platforms and minimizing latency. 
    Unexpected use cases and future ideas for Carmera. 
    ARK’s own findings on the autonomous future and the market opportunities. 

     
    Tweetables:
    “I think if you’ve ever spent time in developing countries, you don’t take roads for granted, you see just how much of the nervous system they really are for the real world.” — @ro_gupta
    “Basically, almost all computer vision that you see being commercialized is deep learning, neural network based, or a lot of it is.” — @ro_gupta

    • 58 min

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