Sustainability Article

Clifton B. Parker’s picture

By: Clifton B. Parker

An underlying theme emerged from the Stanford Institute for Human-Centered Artificial Intelligence’s fall conference: Artificial intelligence (AI) must be truly beneficial for humanity and not undermine people in a cold calculus of efficiency.

Titled “AI Ethics, Policy, and Governance,” the event brought together more than 900 people from academia, industry, civil society, and government to discuss the future of AI (or automated computer systems able to perform tasks that normally require human intelligence).

Discussions at the conference highlighted how companies, governments, and people around the world are grappling with AI’s ethical, policy, and governance implications.

William A. Levinson’s picture

By: William A. Levinson

How will the United States’ withdrawal from the Paris Agreement affect greenhouse gas emissions? Quality Digest editor in chief Dirk Dusharme and Mike Richman, principal at Richman Business Media Consulting, point out that most manufacturers already recognize that waste, including waste of energy as represented by carbon emissions, costs the supply chain money.1 This leads to my conclusion that withdrawal from the agreement will not have any significant effect on U.S. carbon emissions.

Involving relevant interested parties

It is a basic principle of ISO 9001:2015 that organizations must identify the needs and expectations of their relevant interested parties, but not all interested parties are relevant. The Paris Agreement offers little identifiable value to organizations, so it is not a relevant stakeholder. Neither are investment banks that had hoped to profit from cap-and-trade mandates.2 The supply chain should contain nothing that does not deliver value to the other supply chain participants.

Multiple Authors
By: Kendall Powell, Knowable Magazine

When my kids, ages 11 and 8, bang through the back door after school, often the first thing out of their mouths is: “Mom! Can we play Prodigy?”

After a quick mental calculation of how much screen time they've already had for the week and how much peace and quiet I need to finish my work, I acquiesce. After all, Prodigy is a role-playing video game that encourages kids to practice math facts. It’s educational.

Right?

Though video games are increasingly making their way into classrooms, scientists who study them say the data are lacking on whether they can actually improve learning—and most agree that teachers still outperform games in all but a few circumstances.

But there is growing evidence that some types of video games may improve brain performance on a narrow set of tasks. This is potentially good news for students, as well as for the millions of people who love to play, or at least can’t seem to stop playing (see infographic).

“There is a lot of evidence that people—and not just young people—spend a lot of time playing games on their screens,” says Richard Mayer, an education psychology researcher at the University of California, Santa Barbara. “If we could turn that into something more productive, that would be a worthwhile thing to do.”

Bill Snyder’s picture

By: Bill Snyder

In 1500, China’s economy was the strongest in the world. But by the 19th century, the United States, Western Europe, and Japan had leapfrogged over China by churning out goods and services in vast quantities while the former superpower stalled.

Why? Some economists argue that China’s lack of free markets and unencumbered innovation in the West led to the shift. But what is the relationship between innovation and markets, productivity, and inequality?

The answer to that puzzle and others were explored during a recent forum on the relationship of innovation to economic growth at the Hoover Institution. Three Stanford professors, all Hoover fellows—Stephen Haber, Edward Lazear, and Amit Seru—spoke on a panel moderated by Jonathan Levin, dean of Stanford Graduate School of Business.

Michael Brundage’s picture

By: Michael Brundage

How do we get smart? I was first asked this question while sitting in on a call with a small manufacturer who supplied parts for automotive manufacturers. With all the buzz around “smart manufacturing,” this manufacturer wanted to join the movement. The problem was that its leaders didn’t know where to start.

They bought sensors without any real understanding of what to do with them. When I asked why, they said they didn’t have any useful data and they needed data to be “smart.” I asked them, “How do you not have any useful data?” They said they only had documents capturing the history of each maintenance event in the facility. These “textual maintenance work orders” were hardly useful due to their unstructured, jargon-filled nature. At the time they were right; these data in its natural form seemed useless.

Dylan Walsh’s picture

By: Dylan Walsh

In principle, the mountaineer’s work is simple: “To win the game he has first to reach the mountain’s summit,” said George Mallory, who took part in Britain’s first three attempts on Everest during the 1920s. “But, further, he has to descend in safety.”

The tension between these two goals—summiting while also surviving—makes the Himalayas context especially interesting and relevant for companies also balancing multiple goals, says Lindred Leura Greer, an associate professor of organizational behavior at Stanford Graduate School of Business.

“Mountaineering provides an interesting setting, and an extreme one, in which you’re trying to win while also trying to mitigate loss,” Greer says. “This looks a lot like, say, a startup, where you’re trying to maximize to become a unicorn while at the same time trying to make sure the small details don’t pull you under.”

Given this analogue, Greer and other researchers used mountain climbing as a lens to explore longstanding assumptions about group performance. For decades, academics have suggested a straightforward link between a group’s solidarity and its success: The more a group operates with a single mind, the better its execution.

Multiple Authors
By: Rachel Ehrenberg, Knowable Magazine

If you’re lucky, you’ve tasted a perfectly ripe fruit—a sublime peach, perhaps, or a buttery avocado. But odds are most of the fruit you’ve eaten tastes more like wet cardboard. Although plant breeders have mastered growing large, perfect-looking fruits that resist decay, ship easily, and are available year-round, flavor has fallen by the wayside.

That’s starting to change. Amid growing consumer interest in sustainable farming and good food, researchers are delving into the complex biochemistry and genetics of fruit flavor with renewed zest. Here are some basic facts about fruit, how it ripens, why much of it tastes so bland—and how scientists are trying to reclaim lost flavors.

What is fruit and how is it made?

Botanically speaking, fruits are mature, ripened ovaries containing seeds. These seed suitcases can be dry, like a pea pod, or fleshy, like an apple or tomato. A fleshy fruit, from the plant’s point of view, is a fee-for-service: a nutritious meal offered to an animal in exchange for dispersing the seeds inside.

Krystle Morrison’s picture

By: Krystle Morrison

From carrying food in from the field, to shipping processed products, to assembling a supermarket display, packaging matters. As a follow-up to our exploration of emerging trends in food packaging, we’re taking a look at several innovative technologies that could change the future of packaging.

The search for sustainability

More than half of consumers say that environmental sustainability is at least somewhat important to their purchasing decisions, and 41 percent of those shoppers look for recyclable packaging. To benefit the environment and ultimately please consumers with sustainability practices, food brands, startups, and researchers are discovering new ways to package products with recyclable, reusable, or biodegradable materials. 

Multiple Authors
By: Jill Barshay, Sasha Aslanian

When Keenan Robinson started college in 2017, he knew the career he wanted. He’d gone to high school in a small town outside Atlanta. His parents had never finished college, and they always encouraged Robinson and his two older siblings to earn degrees. Robinson’s older brother was the first in the family to graduate. “My parents always stressed how powerful an education is and how it is the key to success,” Robinson says.

When Robinson arrived at Georgia State University in Atlanta, he wanted to major in nursing. “I always knew I had a passion for helping people,” he says. Biology had been his best subject in high school. “My dad, my mom would always kind of call me like the king of trivia because I’d always have just like random science facts.”

During his freshman year, Robinson earned a B average. But the university was closely tracking his academic performance and knew from 10 years of student records that Robinson wasn’t likely to make the cut for the nursing program.

Georgia State is one of a growing number of schools that have turned to big data to help them identify students who might be struggling—or soon be struggling—academically so the school can provide support before students drop out.

Zach Winn’s picture

By: Zach Winn

Manufacturers are constantly tweaking their processes to get rid of waste and improve productivity. As such, the software they use should be as nimble and responsive as the operations on their factory floors.

Instead, much of the software in today’s factories is static. In many cases, it’s developed by an outside company to work in a broad range of factories, and implemented from the top down by executives who know software can help but don’t know how best to adopt it.

That’s where MIT spinout Tulip comes in. The company has developed a customizable manufacturing app platform that connects people, machines, and sensors to help optimize processes on a shop floor. Tulip’s apps provide workers with interactive instructions, quality checks, and a way to easily communicate with managers if something is wrong.

Managers, in turn, can make changes or additions to the apps in real-time and use Tulip’s analytics dashboard to pinpoint problems with machines and assembly processes.

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