Port Lympne Reserve, which runs a breeding（繁育）programme, has welcomed the arrival of a rare black rhino calf（犀牛幼息）. When the tiny creature arrived on January 31, she became the 40th black rhino to be born at the reserve. And officials at Port Lympne were delighted with the new arrival, especially as black rhinos are known for being difficult to breed in captivity（圈养）.

Paul Beer, head of rhino section at Port Lympne, said: "Obviously we're all absolutely delighted to welcome another calf to our black rhino family. She's healthy, strong and already eager to play and explore. Her mother, Solio, is a first—time mum and she is doing a fantastic job. It's still a little too cold for them to go out into the open, but as soon as the weather warms up. I have no doubt that the little one will be out and about exploring and playing every day."

The adorable female calf is the second black rhino born this year at the reserve, but it is too early to tell if the calves will make good candidates to be returned to protected areas of the wild. The first rhino to be born at Port Lympne arrived on January 5 to first—time mother Kisima and weighed about 32 kg. His mother, grandmother and great grandmother were all born at the reserve and still live there.

According to the World Wildlife Fund, the global black rhino population has dropped as low as 5500, giving the rhinos a "critically endangered" status.

"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".

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.