Yours,

Li Jin

I am Li Hua, President of our school’s Wildlife Conservation Association.

6 . In US emergency rooms (ER), the average wait time to see a doctor is more than two hours. There are more patients in need than there are doctors, nurses and other staff to help them. Many parents have suffered through hours in the ER with a sick, upset child, only to get sent home because their case is not considered urgent. What if there was another choice—like a house call from an intelligent machine?

Now, a new study shows that AI systems can assess (评估) a child’s medical chart and come up with a diagnosis (诊断), a determination of what is wrong with that patient.

The study took place at Guangzhou Women and Children’s Medical Center in southern China. First, a team of doctors reviewed 6, 183 medical charts. They summarized the information in these charts into a list of keywords linked to disease-related symptoms or signs, such as “fever”. Researchers then taught these keywords to the AI system. Once trained, the system scanned children’s charts for the key terms, checking if they were present or not in order to come to a conclusion. Finally, it offered diagnoses based on the charts, narrowing down from among 55 illness categories.

Dongxiao Zhu, an assistant professor of computer science at Wayne State University who did not take part in the study, however, sees this as “augmented intelligence (增强智能)” rather than “artificial intelligence”, because the system handled only 55 illness categories. Compare that to thousands of possibilities in the real world. The machine cannot yet get into the more complex aspects of a medical decision.

Zhu is also concerned about the amount of human work that went into the study—namely, the time and energy spent by human doctors. They spent hours grading the machine’s assessments and comparing them to their own. It’s no wonder that the process took four years. Considering that, it may be a while before you can skip the ER and see a robot-doctor instead.

7 . One of the curious things about social networks is the way that some messages, pictures, or ideas can spread like wildfire while others that seem just as catchy or interesting barely register at all

Before you go deep into the puzzle, consider this: If you measure the height of your male friends, for example, the average is about 170 cm. You are 172 and your friends are all about the same height as you are. Indeed, the mathematical concept of “average” is a good way to capture the nature of this data set.

But imagine that one of your friends was much taller than you. This person would dramatically skew the average, which would make your friends taller than you, on average. In this case, the “average” is a poor way to capture this data set.

Exactly this situation occurs on social networks. On average, your coauthors will be cited more often than you, and the people you follow will post more frequently than you and so on.

Now Lerman from University of Southern Caledonia has discovered a related paradox, which they call the majority illusion (多数错觉). They illustrate this illusion with an example. They take 14 nodes linked up to form a small network. They then color three of these nodes and count how many of the remaining nodes link to them in a single step.

In situation (a), the uncolored nodes see more than half of their neighbors as colored. This is the majority illusion—the local impression that a specific feature is common when the global truth is entirely different. While in situation (b) the majority illusion doesn’t occur.

So how popular is it in the real world? It’s found out that the majority illusion occurs in almost all network scenarios. “The effect is largest in the political blogs network, where 60% of nodes will have majority active neigbbours, even when only 20% of the nodes are truly active,” says Lerman.

It immediately explains many interesting phenomena. For a start, it shows how some content can spread globally while other similar content does not—the key is to start with a small number of well-connected early adopters fooling the rest of the network into thinking it is common. The affected nodes then find it natural to follow the trend. A real spread finally comes into being.

But it is not yet a marketer’s charter. For that, marketers must first identify the popular nodes that can create the majority illusion for the merger audience. These influencers must then be persuaded to adopt the desired behavior or product, which is essential to the prospect of the marketing plan.

Thank you for your listening.