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The Business Lab is a sponsored podcast produced by Insights, the custom content division of MIT Technology Review. The Business Lab podcast features a 30-minute conversation with either an executive from the sponsor partner or a technologist with expertise in a relevant technology area. The discussion focuses on technology topics that matter to today’s enterprise decision makers. Laurel Ruma, MIT Technology Review’s custom content director for the United States, is the host.

Business La‪b‬ MIT Technology Review Insights

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The Business Lab is a sponsored podcast produced by Insights, the custom content division of MIT Technology Review. The Business Lab podcast features a 30-minute conversation with either an executive from the sponsor partner or a technologist with expertise in a relevant technology area. The discussion focuses on technology topics that matter to today’s enterprise decision makers. Laurel Ruma, MIT Technology Review’s custom content director for the United States, is the host.

    Democratizing data for a fair digital economy

    Democratizing data for a fair digital economy

    The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley’s ethos of “move fast and break things” spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. “Data is the main resource of a new digital economy,” says IT for Change director Parminder Singh. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons—or open data sources—are vital to help build an equitable digital economy, but with that comes the challenge of data governance.
    This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.
    “Not everybody is sharing data,” says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, “Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it.” Singh continues, “If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone.”
    One doesn’t have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies. And this is why Singh argues, “These powerful forces should be in the hands of people, in the hands of communities, they should be able to be influenced by regulators for public interest.” Especially now that most of the data is now collected by private companies.
    IT for Change is tackling this with a research project called “Unskewing the Data Value Chain,” which is supported by Omidyar Network. The project aims to assess the current policy gaps and new policy directions on data value chains that can promote equitable and inclusive economic development. Singh explains, “Our goal is to ensure the value chains are organized in a manner where the distribution of value is fairer. All countries can digitally industrialize at if not an equal piece, but an equitable pace, and there is a better distribution of benefits from digitalization.”
    Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next. 
    This podcast was produced in partnership with Omidyar Network.
    Show notes and links
    “Unskewing the Data Value Chain: A Policy Research Project for Equitable Platform Economies,” IT for Change, September 2020
    “Treating data as commons”, The Hindu, Parminder Singh, September 2, 2020
    “Report by the Committee of Experts on Non-Personal Data Governance Framework,” Ministry of Electronics and Information Technology, Government of India
    “A plan for Indian self-sufficiancy in an AI-driven world,” Mint, Parminder Singh, July 29, 2020

    • 34分
    Building a Better Data Economy

    Building a Better Data Economy

    Tim O’Reilly, the “Oracle of Silicon Valley,” wants to shift the conversation about data value to focus on the harm that tech giants are inflicting against us with our own data.
    It’s “time to wake up and do a better job,” says publisher Tim O’Reilly—from getting serious about climate change to building a better data economy. And the way a better data economy is built is through data commons—or data as a common resource—not as the giant tech companies are acting now, which is not just keeping data to themselves but profiting from our data and causing us harm in the process.
    “When companies are using the data they collect for our benefit, it's a great deal,” says O’Reilly, founder and CEO of O’Reilly Media. “When companies are using it to manipulate us, or to direct us in a way that hurts us, or that enhances their market power at the expense of competitors who might provide us better value, then they're harming us with our data.” And that’s the next big thing he’s researching: a specific type of harm that happens when tech companies use data against us to shape what we see, hear, and believe.
    It’s what O’Reilly calls “algorithmic rents,” which uses data, algorithms, and user interface design as a way of controlling who gets what information and why. Unfortunately, one only has to look at the news to see the rapid spread of misinformation on the internet tied to unrest in countries across the world. Cui bono? We can ask who profits, but perhaps the better question is “who suffers?” According to O’Reilly, “If you build an economy where you're taking more out of the system than you're putting back or that you're creating, then guess what, you're not long for this world.” That really matters because users of this technology need to stop thinking about the worth of individual data and what it means when very few companies control that data, even when it’s more valuable in the open. After all, there are “consequences of not creating enough value for others.”
    We’re now approaching a different idea: what if it’s actually time to start rethinking capitalism as a whole? “It's a really great time for us to be talking about how do we want to change capitalism, because we change it every 30, 40 years,” O’Reilly says. He clarifies that this is not about abolishing capitalism, but what we have isn’t good enough anymore. “We actually have to do better, and we can do better. And to me better is defined by increasing prosperity for everyone.”
    In this episode of Business Lab, O’Reilly discusses the evolution of how tech giants like Facebook and Google create value for themselves and harm for others in increasingly walled gardens. He also discusses how crises like covid-19 and climate change are the necessary catalysts that fuel a “collective decision” to “overcome the massive problems of the data economy.”
    Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.
    This podcast episode was produced in partnership with Omidyar Network.
    Show notes and links
    “We need more than innovation to build a world that’s prosperous for all,” by Tim O’Reilly, Radar, June 17, 2019
    “Why we invested in building an equitable data economy,” by Sushant Kumar, Omidyar Network, August 14, 2020
    “Tim O’Reilly - ‘Covid-19 is an opportunity to break the current economic paradigm,’” by Derek du Preez, Diginomica, July 3, 2020
    “Fair value? Fixing the data economy,” MIT Technology Review Insights, December 3, 2020

    • 39分
    Leveraging collective intelligence and AI to benefit society

    Leveraging collective intelligence and AI to benefit society

    A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic.
    “The future is now,” says Frederic Vacher, head of innovation at Dassault Systèmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovation laboratory embraces the concept of the social enterprise and merges collective intelligence with a cross-collaborative approach by building what Vacher calls “communities of people—passionate and willing to work together to accomplish a common objective.”
    This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff. 
    “It’s not only software, it's not only cloud, but it’s also a community of people’s skills and services available for the marketplace,” Vacher says. “Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab.”  
    And for Dassault Systèmes, there’s unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing together industry experts, health-care professionals, makers, and scientists to tackle covid-19. Vacher explains, “We created an open community, ‘Open Covid-19,’ to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country.” This wasted time and resources during a global crisis. And, Vacher continues, the urgency of working together to share information became obvious, “They were all facing the same issues, and by working together, we thought it could be an interesting way to accelerate, to transfer the know-how, and to avoid any mistakes.” 
    Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next. 
    This episode of Business Lab is produced in association with Dassault Systèmes. 
    Show notes and links 
    How Effective is a Facemask? Here’s a Simulation of Your Unfettered Sneeze, by Josh Mings, SolidSmack, April 2, 2020 
    Open Covid-19 Community Lets Makers Contribute to Pandemic Relief, by Clare Scott, Dassault, The SIMULIA Blog, July 15, 2020
    Dassault 3DEXPERIENCE platform
    Collective intelligence and collaboration around 3D printing: rising to the challenge of Covid-19, by Frederic Vacher, STAT, August 10, 2020

    • 34分
    With Trust in AI, Manufacturers Can Build Better

    With Trust in AI, Manufacturers Can Build Better

    Some people might not associate the word “trust” with artificial intelligence (AI). Stefan Jockusch is not one of them. Vice president of strategy at Siemens Digital Industries Software, Jockusch says trusting an algorithm powering an AI application is a matter of statistics.
    This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.
    “If it works right, and if you have enough compute power, then the AI application will give you the right answer in an overwhelming percentage of cases,” says Jockusch, whose business is building “digital twin” software of physical products.
    He gives the example of Apple’s iPhones and its facial recognition software—technology that has been tested “millions and millions of times” and produced just a few failures.
    “That’s where the trust comes from,” says Jockusch.
    In this episode of Business Lab, Jockusch discusses how AI can be used in manufacturing to build better products: by doing the tedious work engineers have traditionally done themselves. AI can help engineers manage multiple design variations for semiconductors, for example, or sift through routine bug reports that software developers would have had to manually review to figure out what is causing a glitch.
    “AI is playing a bigger role to allow engineers to focus more on the real, creative part of their job and less on detail work,” says Jockusch.
    Also in the episode, Jockush explains how AI embedded in products themselves have already won over millions of people—think voice assistants like Siri and Alexa—and will someday become such a common component that people will barely talk about the value or the future of AI.
    “I mean, how many discussions do you have nowadays about the value of Excel, of cellular calculation, although we use it every day?” says Jockusch. “Everybody uses it every day in something, and it’s so universal that we hardly ever think about it.”

    • 24分
    The Fourth Industrial Revolution Has Begun: Now’s The Time to Join

    The Fourth Industrial Revolution Has Begun: Now’s The Time to Join

    2020 has created more than a brave new world. It’s a world of opportunity rapidly pressuring organizations of all sizes to rapidly adopt technology to not just survive, but to thrive. And Andrew Dugan, chief technology officer at Lumen Technologies, sees proof in the company’s own customer base, where “those organizations fared the best throughout covid were the ones that were prepared with their digital transformation.” And that’s been a common story this year. A 2018 McKinsey survey showed that well before the pandemic 92% of company leaders believed “their business model would not remain economically viable through digitization.” This astounding statistic shows the necessity for organizations to start deploying new technologies, not just for the coming year, but for the coming Fourth Industrial Revolution.
    This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.
    Lumen plans to play a key role in this preparation and execution: “We see the Fourth Industrial Revolution really transforming daily life ... And it's really driven by that availability and ubiquity of those smart devices.” With the rapid evolution of smaller chips and devices, acquiring analyzing, and acting on the data becomes a critical priority for every company. But organizations must be prepared for this increasing onslaught of data.
    As Dugan says, “One of the key things that we see with the Fourth Industrial Revolution is that enterprises are taking advantage of the data that's available out there.” And to do that, companies need to do business in a new way. Specifically, “One is change the way that they address hiring. You need a new skill set, you need data scientists, your world is going to be more driven by software. You’re going to have to take advantage of new technologies.” This mandate means that organizations will also need to prepare their technology systems, and that’s where Lumen helps “build the organizational competencies and provide them the infrastructure, whether that’s network, edge compute, data analytics tools,” continues Dugan. The goal is to use software to gain insights, which will improve business.
    When it comes to next-generation apps and devices, edge compute—the ability to process data in real time at the edge of a network (think a handheld device) without sending it back to the cloud to be processed—has to be the focus. Dugan explains: “When a robot senses something and sends that sensor data back to the application, which may be on-site, it may be in some edge compute location, the speed at which that data can be collected, transported to the application, analyzed, and a response generated, directly affects the speed at which that device can operate.” This data must be analyzed and acted on in real time to be useful to the organization. Think about it, continued Dugan, “When you’re controlling something like an energy grid, similar thing. You want to be able to detect something and react to it in near real time.” Edge compute is the function that allows organizations to enter the Fourth Industrial Revolution, and this is the new reality. “We're moving from that hype stage into reality and making it available for our customers,” Dugan notes. “And that's exciting when you see something become real like this.”
    Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.
    This podcast episode was produced in partnership with Lumen Technologies.
    Links
    “Emerging Technologies And The Lumen Platform,” Andrew Dugan, Automation.com, Sept 14, 2020
    “The Fourth Industrial Revolution: what it means, how to respond,” Klaus Schwab

    • 28分
    How AI Will Revolutionize Manufacturing

    How AI Will Revolutionize Manufacturing

    Ask Stefan Jockusch about what a factory might look like in 10 or 20 years, and the answer might leave you at a crossroads between fascination and bewilderment. Jockusch is vice president for strategy at Siemens Digital Industries Software, which develops applications that simulates the conception, design, and manufacture of products such as a cell phone or a smart watch. His vision of a smart factory is abuzz with “independent, moving” robots. But they don’t stop at making one or three or five things. No—this factory is “self-organizing.”
    “Depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product,” Jockusch says. “It will self-organize itself to do something different.”
    Behind this factory of future is artificial intelligence (AI), Jockusch says in this episode of Business Lab. But AI starts much, much smaller, with the chip. Take automaking. The chips that power the various applications in cars today—and the driverless vehicles of tomorrow—are embedded with AI, which support real-time decision-making. They’re highly specialized, built with specific tasks in mind. The people who design chips then need to see the big picture.
    “You have to have an idea if the chip, for example, controls the interpretation of things that the cameras see for autonomous driving. You have to have an idea of how many images that chip has to process or how many things are moving on those images,” Jockusch says. “You have to understand a lot about what will happen in the end.”
    This complex way of building, delivering, and connecting products and systems is what Siemens describes as “chip to city”—the idea that future population centers will be powered by the transmission of data. Factories and cities that monitor and manage themselves, Jockusch says, rely on “continuous improvement”: AI executes an action, learns from the results, and then tweaks its subsequent actions to achieve a better result. Today, most AI is helping humans make better decisions.
    “We have one application where the program watches the user and tries to predict the command the user is going to use next,” Jockusch says. “The longer the application can watch the user, the more accurate it will be.”
    Applying AI to manufacturing, Jockusch says, can result in cost savings and big gains in efficiency. Jockusch gives an example from a Siemens factory of printed circuit boards, which are used in most electronic products. The milling machine used there has a tendency to “goo up over time—to get dirty.” The challenge is to determine when the machine has to be cleaned so it doesn’t fail in the middle of a shift.
    “We are using actually an AI application on an edge device that's sitting right in the factory to monitor that machine and make a fairly accurate prediction when it's time to do the maintenance,” Jockusch says.
    The full impact of AI on business—and the full range of opportunities the technology can uncover—is still unknown.
    “There's a lot of work happening to understand these implications better,” Jockusch says. “We are just at the starting point of doing this, of really understanding what can optimization of a process do for the enterprise as a whole.”
    Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.
    This podcast episode was produced in partnership with Siemens Digital Industries Software.

    • 25分

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