Business Lab

If you drive in the United States, chances are you can’t remember the last time you bought a paper map, printed out a digital map, or even stopped to ask for directions. Thanks to GPS and the mobile mapping apps on our smartphones and their real-time routing advice, navigation is a solved problem.

But in developing or fast-growing parts of the world: not so much. If you live in a place like Doha, Qatar, where the length of the road network has tripled over the last five years, commercial mapping services from Google, Apple, Bing, or other providers simply can’t keep up with the pace of infrastructure change. 

“Each one of us who grew up in Europe or the US probably cannot understand the scale at which these cities grow,” says Rade Stanojevic, a senior scientist at the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, a Qatar Foundation university, in Doha. “Pretty much every neighborhood sees a new underpass, new overpass, new large highway being added every couple of months.” 

As Qatar copes with this rapid growth—and especially as it prepares to host the FIFA World Cup in 2022—the bad routing advice and accumulating travel delays from outdated digital maps is increasingly costly. That’s why Stanojevic and colleagues at QCRI decided to try applying machine learning to the problem.

A road network can be interpreted as a giant graph with where every intersection is a node and every road is an edge, says Stanojevic, whose specialty is network economics. Road segments can have both static characteristics, such as the designated speed limit, and dynamic characteristics, such as rush-hour congestion. To see where traffic really is going—rather than where an old map says it should go—and then predict the best routes through an ever-changing maze, all a machine-learning model would need is lots of up-to-data data on both the static and dynamic factors. “Fortunately enough, modern vehicle fleets have these monitoring systems that produce quite a lot of data,” says Stanojevic.

Stanojevic is talking about taxis. His team at QCRI partnered with a Doha-based taxi company called Karwa to collect full GPS data on their vehicles’ comings and goings. They used that data to build a new mapping service called QARTA that offers routing advice to drivers at Karwa and other operators such as delivery fleets.

Stanojevic says QARTA’s deeper understanding of the actual road and traffic situation in Doha helps drivers shave tens of seconds off every trip, which translates into a fleet-wide efficiency gain of 5 to 10 percent. “If you’re running a fleet of 3,000 cars, five percent of that is 150 cars,” Stanojevic says. “You can basically remove 150 cars from the road and not lose any business.”

Although QCRI’s system probably can’t compete with the big map-services providers in the developed world, it could help cities in the Middle East and other developing regions manage growth more wisely, Stanojevic says. And a few years from now, as more autonomous vehicles take to the streets, machine-learning-based routing advice could look at the big picture in a busy city and help fleets cut carbon emissions by keeping drivers out of traffic jams. “By having some sort of a global view of what’s going on in the whole city, autonomous vehicles can actually reroute us to have some sort of global load balancing, to help everyone be better off.”

This podcast was produced in partnership with the Qatar Foundation.

Show notes and links

Qatar Computing Research Institute

Sofiane Abbar, Rade Stanojevic, Shadab Mustafa, and Mohamed Mokbel, Traffic Routing in the Ever-Changing City of Doha, Communications of the ACM, April 2021

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For the past several years, economists and government leaders have regularly sounded alarms about the dangers of big tech monopolies. On her 2020 campaign website, for example, Senator Elizabeth Warren said “big tech companies have too much power, too much power over our economy, our society, our democracy." In the months since the election, politicians on both the left and right have expressed concerns over how to encourage competition and innovation among the big tech leaders, and even how to hold onto democratic ideals in the face of digital misinformation and conspiracy theories. 

The challenge with a company like Facebook is that its business model actively encourages tribalism and anger, which is not the way markets usually work, says Paul Romer, an economics professor at New York University who previously served as the chief economist of The World Bank and was the co-recipient of the 2018 Nobel Prize in Economics Sciences. “When economists defend the market, we have this very simple idea in mind, where I as a buyer give something and get some good back,” he says. “None of those features are characteristic of this new market for digital services, where advertising is like the hidden method of capturing compensation for these firms.”

 Users, he says, “are being manipulated in ways that they don't fully understand.”

Regulators won’t work because big tech firms are too powerful, Romer maintains, while traditional antitrust laws are not well-suited to deal with this problem. However, he says that a progressive tax on digital advertising revenue, passed by state legislatures, could create a unique incentive for companies such as Google and Facebook to split up their businesses and discourage growth by acquisition.

Such a progressive tax model, however, needs to be aggressive: “The kind of tax that I think would create a big incentive to change, at say Google and Facebook, the two biggest firms in this market, has to be a tax where the average tax rate they pay right now, given their size, is 35% of their revenue.”

Show notes and links:

·     Paul Romer, Taxing Digital Advertising, May 1, 2021

·     Maryland Breaks Ground with Digital Advertising Tax, National Law Review, March 17, 2021

·     Once Tech’s Favorite Economist, Now a Thorn in Its Side, Steve Lohr, New York Times, May 20, 2021

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Executives need to clearly communicate risks but also bring context to data. Tech talk is out: speaking the same language will win the day.

It’s drilled into the heads of board directors and the C-suite by scary data-breach headlines, lawyers, lawsuits, and risk managers: cybersecurity is high-risk. It’s got to be on the list of a company’s top priorities.

But how many directors get lost in the technicalities of technology? The challenge for a chief information security officer (CISO) is talking to the board of directors in a way they can understand and support the company.

Niall Browne, senior vice president and chief information security officer at Palo Alto Networks, says that you can look at the CISO-board discussion as being a classic sales pitch: successful CISOs will know how to close the deal just like the best salespeople do. “That's what makes a really good salesperson: the person that has the pitch to close” he says. “They have the ability to close the deal. So they ask for something.” 

“For ages,” Browne says, CISOs have had two big problems with boards. First, they haven’t been able speak the same language so that the board could understand what the issues were. The second problem: “There was no ask.” You can go in front of a board and give your presentation, and the directors can look like they’re in agreement, nodding or shaking their heads, and you can think to yourself, “Job done. They’re updated.” But that doesn’t necessarily mean that the business’s security posture is any better.

That’s why it’s important for CISOs to raise the board’s understanding to the level where they know what’s needed and why. Especially when it comes to new advances in cybersecurity, like attack surface management, which is “probably one of the areas that CISOs focus least on and yet is the most important,” Browne says. For example, “many times the CISO and the security team may not be able to see the wood from the trees because they're so involved in it.” And to do that, CISOs need a set of metrics so that anybody can read a board deck and within minutes understand what the CISO is trying to get across, Browne says. “Because for the most part, the data is there, but there's no context behind it.” 

This episode of Business Lab is produced in association with Palo Alto Networks.

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During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as “widespread domestic and international exploitation” that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company’s network. This type of continuous tracking of inventory doesn’t scale with how humans work, but machines can handle it easily.

For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. “It’s pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run,” observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks.

You might ask why companies don’t simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher.

But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don’t know the answer, it’s impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn’t understand the risk—and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks—well, the story almost writes itself.

That’s why Kraning thinks it’s so important to create a minimum set of standards. And, he says, “Boards and senior executives need to be minimally conversant in some ways about cybersecurity risk and analysis of those metrics.” Because without that level of understanding, boards aren’t asking the right questions—and cybersecurity executives aren’t having the right conversations.

Kraning believes attack service management is a better way to secure companies with a continuous process of asset discovery, including the discovery of all assets exposed to the public internet—what he calls “unknown unknowns.” New assets can appear from anywhere at any time. “This is actually a solvable problem largely with a lot of technology that's being developed,” Kraning says. “Once you know a problem exists, actually fixing it is actually rather straightforward.” And that’s better for not just companies, but for the entire corporate ecosystem.

Show notes and links:

“A leadership agenda to take on tomorrow,” Global CEO Survey survey, PwC

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In a recent survey, “2021 Thriving in an AI World,” KPMG found that across every industry—manufacturing to technology to retail—the adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, “Covid-19 has accelerated the pace of digital in many ways, across many types of technologies.” Justice continues, “This is where we are starting to experience such a rapid pace of exponential change that it’s very difficult for most people to understand the progress.” But understand it they must because “artificial intelligence is evolving at a very rapid pace.”

Justice challenges us to think about AI in a different way, “more like a relationship with technology, as opposed to a tool that we program,” because he says, “AI is something that evolves and learns and develops the more it gets exposed to humans.” If your business is a laggard in AI adoption, Justice has some cautious encouragement, “[the] AI-centric world is going to accelerate everything digital has to offer.”

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 association with KPMG.

Show notes and links

“2021 Thriving in an AI World,” KPMG

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There’s nothing new about conspiracy theories, disinformation, and untruths in politics. What is new is how quickly malicious actors can spread disinformation when the world is tightly connected across social networks and internet news sites. We can give up on the problem and rely on the platforms themselves to fact-check stories or posts and screen out disinformation—or we can build new tools to help people identify disinformation as soon as it crosses their screens.

Preslav Nakov is a computer scientist at the Qatar Computing Research Institute in Doha specializing in speech and language processing. He leads a project using machine learning to assess the reliability of media sources. That allows his team to gather news articles alongside signals about their trustworthiness and political biases, all in a Google News-like format.

“You cannot possibly fact-check every single claim in the world,” Nakov explains. Instead, focus on the source. “I like to say that you can fact-check the fake news before it was even written.” His team’s tool, called the Tanbih News Aggregator, is available in Arabic and English and gathers articles in areas such as business, politics, sports, science and technology, and covid-19.

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 the Qatar Foundation.

Show notes and links

Tanbih News Aggregator

Qatar Computing Research Institute

“Even the best AI for spotting fake news is still terrible,” MIT Technology Review, October 3, 2018

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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

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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

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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

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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.”

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