"What would the world be if there were no hunger?" It's a question that Professor Crystal would ask her students. They found it hard to answer, she wrote later, because imagining something that isn't part of real life—and learning how to make it real—is a rare skill. It is taught to artists and engineers, but much less often to scientists. Crystal set out to change that, and helped to create a global movement. The result一an approach known as systems thinking—is now seen as essential in meeting global challenges.

Systems thinking is crucial to achieving targets such as zero hunger and better nutrition because it requires considering the way in which food is produced, processed, delivered and consumed, and looking at how those things intersect (交叉) with human health, the environment, economics and society. According to systems thinking, changing the food system—or any other network- requires three things to happen. First, researchers need to identify all the players in that system, second, they must work out how they relate to each other, and third, they need to understand and quantify the impact of those relationships on each other and on those outside the system.

Take nutrition. In the latest UN report on global food security, the number of undernourished (营养不良) people in the world has been rising, despite great advances in nutrition science. Tracking of 150 biochemicals in food has been important in revealing the relationships between calories, sugar, fat and the occurrence of common diseases. But using machine learning and artificial intelligence, some scientists propose that human diets consist of at least 26,000 biochemicals—and that the vast majority are not known. This shows that we have some way to travel before achieving the first objective of systems t hinking - which,in this example, is to identify more constituent parts of the nutrition system.

A systems approach to creating change is also built on the assumption that everyone in the system has equal power. But as some researchers find, the food system is not an equal one. A good way to redress (修正) such power imbalance is for more universities to do what Crystal did and teach students how to think using a systems approach.

More researchers, policymakers and representatives from the food industry must learn to look beyond their direct lines of responsibility and adopt a systems approach. Crystal knew that visions alone don't produce results, but concluded that "we'll never produce results that we can't envision".

The Biggest Stadiums in the World

People have been pouring into stadiums since the days of ancient Greece. In around 8 A.Q., the Romans built the Colosseum, which remains the world's best known stadium are continues to inform contemporary design. Rome's Colosseum was 157 feet tall and had 80 entrances, seating 50,000 people. However, that was small fry compared with the city's Circus Maximus, which accommodated around 250,000 people.

These days, safety regulations-not to mention the modern sports fan's desire for a good view and a comfortable seat-tend to keep stadium capacities（容量）slightly lower. Even soccer fans tend to have a seat each; gone are the days of thousands standing to watch the match.

For the biggest stadiums in the world, we have used data supplied by the World Atlas list so far, which ranks them by their stated permanent capacity, as well as updated information from official stadium websites.

All these stadiums are still functional, still open and still hosting the biggest events in world sport.

•Rungrado 1st of May Stadium, Pyongyang, D.P.R-Korea. Capacity. 150,000. Opened. May 1,1989.

•Michigan Stadium, Ann Arbor, Michigan, U.S. Capacity: 107,601. Opened. October 1, 1927.

•Beaver Stadium, State College, Pennsylvania, U.S. Capacity: 106,572. Opened: September 17, I960.

•Ohio Stadium, Columbus, Ohio, U.S. Capacity: 104,944. Opened: October 7, 1922.

•Kyle Field, College Station, Texas, U.S. Capacity: 102,512. Opened: September 24,1927.

A machine can now not only beat you at chess, it can also outperform you in debate. Last week, in a public debate in San Francisco, a software program called Project Debater beat its human opponents, including Noa Ovadia, Israel's former national debating champion.

Brilliant though it is, Project Debater has some weaknesses. It takes sentences from its library of documents and prebuilt arguments and strings them together. This can lead to the kinds of errors no human would make. Such wrinkles will no doubt be ironed out, yet they also point to a fundamental problem. As Kristian Hammond, professor of electrical engineering and computer science at Northwestern University, put it: "There's never a stage at which the system knows what it's talking about."

What Hammond is referring to is the question of meaning, and meaning is central to what distinguishes the least intelligent of humans from the most intelligent of machines. A computer works with symbols. Its program specifies a set of rules to transform one string of symbols into another. But it does not specify what those symbols mean. Indeed, to a computer, meaning is irrelevant. Humans, in thinking, talking, reading and writing, also work with symbols. But for humans, meaning is everything. When we communicate, we communicate meaning. What matters is not just the outside of a string of symbols, but the inside too, not just how they are arranged but what they mean.

Meaning emerges through a process of social interaction, not of computation, interaction that shapes the content of the symbols in our heads. The rules that assign meaning lie not just inside our heads, but also outside, in society, in social memory, social conventions and social relations. It is this that distinguishes humans from machines. And that's why, however astonishing Project Debater may seem, the tradition that began with Socrates and Confucius will not end with artificial intelligence.