Most of us have quite good memories, butour memories are limited. For example, we cannot remember everyone we have evermet or what we did on every single day of our lives. However, there are some people who do have prodigious memories. These people have a rare condition known as savant syndrome (学者症候群). Savants suffer from a developmental disorder, but they also exhibit great talents that contrast(显出差异) sharply with their physical and mental disabilities.

    Kim Peek (1951-2009) was a savant wholived in Salt Lake City, Utah, in the United States. He was born with damage to parts of his brain, but it seemed that other parts of his brain, particularly those relating to memory, became over-developed.

Peek's unique abilities appeared at avery early age. When he was just 20 months old, he could already remember every book that was read to him. Peek could read two pages of a book at the same time—one page with the right eye and one with the left—in less than 10 seconds and remember everything he read. By the time he died, Peek had memorized more than 9, 000 books. He could remember all the names and numbers ina variety of telephone books. He could remember thousands of facts about history, literature, geography and sports.

    Dr. David Treffert, an expert on savantsyndrome, once described Peek as “a living Google” because of his astonishing ability to memorize and connect facts. However, at the same time, Peek was unable to carry out simple tasks, such as brushing his hair or getting dressed, and he needed others to help him. In 1989, the movie Rain Man won the Oscar forbest Picture. The main character in the movie, played by Dustin Hoffman, wasbased on Kim Peek's life. He started to appear on television, where he would amaze audiences by correctly answering difficult questions on different topics. Peek became world famous, and he and his father began touring widely to talk about overcoming disabilities. He inspired a great many people with his words.“Recognizing and respecting differences in others, and treating everyone in the way you want them to treat you, will make our world a better place foreveryone. Everyone is different.”

    Students taking Georgia Tech's online Knowledge-Based Artificial Intelligence course received some surprising news. Jill Watson one of the nine teaching assistants(TAs) that had helped them finish the challenging course for the past five months was not a ‘‘she'' but an “it”—an intelligent robot!

    Watson is the brainchild of Ashok Goel, who teaches the popular online course. The Professor-of Computer and Cognitive Science in the School of Interactive Computing came up with the idea as a way to deal with a number of questions posed by students in the online forums(论坛). According to Goel, every time the course is offered, the 300 or so students that enroll post over 10,000 questions which are often repetitive. This led Goel to wonder if a smart robot would handle the questions which require standard responses.

    Having worked' with IBM's Watson technology platform in the, the professor knew it would be ideal for his artificial TA: Jill Watson. The artificial intelligence system that uses natural language processing and machine learning to analyze large amounts of data has even been cleverer than human competitors on the television show. It would therefore easily be able to handle routine questions that required little “thinking”.

    The professor and his team of graduate students began by populating Jill's memory with 40,000 questions and answers from past terms. Then came the testing stage. At first, Jill was not very good and often gave strange answers. It often got stuck on certain keywords. By the end of the semester, Jill had attained enough knowledge and skills to participate in forums without any management from Goel, or the other assistants.

    Everyone needs friends. We all like to feel close to someone. It is nice to have a friend to talk, laugh and do things with. Surely, there are times when we need to be alone. We don't always want people around. But we would feel lonely if we never had a friend.

    No two people are the same. Sometimes friends don't get along well, which doesn't mean that they no longer like each other. Most of the time they will go on being friends. Sometimes friends move away, then we feel very sad. We miss them much, but we can call them and write to them. Maybe we would never see them again, and we can make new friends. It is surprising to find out how much we like new people when we get to know them. Families sometimes name their children after a close friend. Many places are named after men and women, if they are friendly to people in a town. Some libraries are named this way. So are some schools. We think of these people when we go to these places.

    There's more good news for people, if they have friends. These people live longer than those people if those don't have friends. Why? It could be that they are happier. Being happy helps you stay well. Or it could be just knowing that someone cares, if someone cares about you, you take better care of yourself.

    Let us all raise a glass to AlphaGo and the advance of artificial intelligence. AlphaGo, DeepMind's Go-playing AI, just defeated the best Go-playing human, Lee Sedol. But as we drink to its success, we should also begin trying to understand what it means for the future.

    The number of possible moves in a game of Go is so huge that in order to win against a player like Lee. AlphaGo was designed to adopt a human—like style of gameplay by using a relatively recent development--deep learning. Deep learning uses large data sets," machine learning" algorithms （计算程序） and deep neural networks to teach the AI how to perform a particular set of tasks. Rather than programming complex Go rules and strategies into AlphaGo DeepMind designers taught AlphaGo to play the game by feeding it data based on typical Go moves. Then, AlphaGo played against itself, tirelessly learning from its own mistakes and improving its gameplay over time. The results speak for themselves.

    Deep learning represents a shift in the relationship humans have with their technological creations. It results in AI that displays surprising and unpredictable behaviour. Commenting after his first loss, Lee described being shocked by an unconventional move he claimed no human would ever have made. Demis Hassabis, one of DeepMind's founders, echoed this comment:"We're very pleased that AlphaGo played some quite surprising and beautiful moves."

    Unpredictability and surprises are—or can be—a good thing. They can indicate that a system is working well, perhaps better than the humans that came before it. Such is the case with AlphaGo. However, unpredictability also indicates a loss of human control. That Hassabis is surprised at his creation's behaviour suggests a lack of control in the design. And though some loss of control might be fine in the context of a game such as Go, it raises urgent questions elsewhere.

    How much and what kind of control should we give up to AI machines? How should we design appropriate human control into AI that requires us to give up some of that very control? Is there some AI that we should just not develop if it means any loss of human control? How much of a say should corporations, governments, experts or citizens have in these matters? These important questions, and many others like them, have emerged in response, but remain unanswered. They require human, not human - like, solutions.

    So as we drink to the milestone in AI, let's also drink to the understanding that the time to answer deeply human questions about deep learning and AI is now.

    A dog spent the last four years of his life waiting a crossroad in the Thai city of Khon Kaen as if waiting for someone. People originally thought the dog had been abandoned, but then realized that he looked healthy, so people asked around about him. It turned out that the dog had indeed been spending most of his time around that crossroad, but a woman had been coming -.round regularly to bring him food and water.

    One day, while photographing the dog everyone called Leo, a reporter met the woman who had been taking care of him. She had come to drop off some food. After learning the story about the dog and the woman, the reporter decided to share the story on social media. The post soon went viral and the photos of Leo got shared hundreds of times. And the photos reached the eyes of Leo^ former old owner.

    Nang Noi Sittisarn, a 64-year-old woman from Thailand's Roi Et Province, almost had a heart attack when her daughter showed her a photo of the beloved dog named BonBon she had lost during a car trip. When she learned that he had been waiting for her in the same spot for the last four years，her heart melted(融化).

    Auntie Noi told her daughter to drive her to where the dog was waiting. When she got there and called his name. BonBon, the poor dog started wiggling(扭动)his tail and came to her，but when she tried to take him home with her, he was unwilling to follow. She didn't want to force the dog to come with her so she agreed to leave him with his new master. However, she and her daughter will come to visit him regularly.

Would a person born blind, who has learned to distinguish objects by touch, be able to recognize them purely by sight if he regained the ability to see? The question, known as Mołyneux's problem, is about whether the human mind has a built-in concept of shapes that is so inborn that a blind person could immediately recognize an object with restored vision. Alternatively, the concepts of shapes are not inborn but have to be learned by exploring an object through sight, touch and other senses.

After their attempt to test it in blind children failed, Lars Chittka of Queen Mary University of London and his team carried out another experiment on bumblebees. To test whether bumblebees can form an internal representation of objects, they first trained the insects to distinguish globes from cubes using a sugar reward. The bees were first trained in the light, where they could see but not touch the objects. Then they were tested in the dark, where they could touch but not see the items. The researchers found that the insects spent more time in contact with the shape they had been trained to associate with the sugar reward, even though they had to rely on touch rather than sight to distinguish the objects.

The researchers also did the opposite test with untrained bumblebees, first teaching them with rewards in the dark and then testing them in the light. Again, the bees were able to recognize the shape associated with the sugar reward, though they had to rely on sight rather than touch in the test. In short, bees have solved Molyneux's problem because the fact suggests that they can picture object features and access them through sight or touch.

However, some experts express their warnings against the result. Jonathan Birch, a philosopher of science, cautions that the bees may have had prior experience associating visual and tactile (触觉) information about straight edges and curved surfaces in their nests.