WASHINGTON—What parent doesn’t want to mold his or her child to be better, faster, smarter, more capable? Even though human parents can’t do that, a robot that builds its own children can. Scientists at the University of Cambridge in England have created a mother robot that not only constructs its own children, but tests their performance.

It's a robotic version of nature’s survival of the fittest. The mother robot analyzes the performance of each of the “children” it creates, and incorporates preferential traits into the next generation.

“As the mother creates them and puts them to work, she evaluates how they’re behaving, and she uses data from this behavior to create the next generation of robots,” explained research scientist Andres Rosendo.

There's no human intervention, except for a computer command to create a robot capable of moving from one place to another. The mother builds its children by gluing together pieces with small motors inside, in different configurations. Then it watches how quickly the children move, keeping the designs of the ones that moved the fastest.

“The mother robot can actually build hundreds of child robots and see the performance of these child robots. And if their performance is good, keep their design for the next generation. And if bad, just let it go，” said Fumiya Iida, lead researcher.

The motivation to produce better children is controlled by the research team, which provides an incentive.

“We program the robot based on some functions that define the reward the robot is going to get, depending on the construction that they make. They cannot change their own reward. In the case of the child robot, it’s distance, so the longer the distance the robot walks，the better the reward it receives,” said Rosendo.

After several generations，the “children” are running twice as fast.

“The mother robot generated 500 robots to see which one is good and which one is bad,” said Iida.

The researchers suggest the machines could be used in an auto plant, for example, where robot cameras examine each car in the assembly line, evaluate any mistakes, and then design a better car.

News anchors(主播) must have been reluctant to read out the following news: Xin Xiaomeng began working as the world’s first female artificial(人工的) intelligence news anchor at Xinhua News Agency on Sunday, three months after a male robot joined the profession.

Unlike previous news robots though, Xin does not read news like a cold machine; she reads it almost like a human being. The muscles on her face stretch and relax-and her reactions change-as she continues reading. That’s why many news anchors were worried: Will AI replace us in the near future?

To find the answer, we have to analyse the technologies that support Xin at her job. Three key technologies are used to support Xin. First, samples of human voices are collected and synthesized (合成). This is followed by the collection and synthesis of human muscle movement samples. And third the voices and movements are married in a way that when the Al news anchor reads, the micro -electric motors behind her face move to make her expressions seem more human.

Yet we need a thorough knowledge of deep leaning technology to make a robot imitate a person’s voice. The developer needs to collect tens of thousands of pieces of pronunciations, input them Into the machine and match them with the text or the Al to lean and read. The process for imitating facial movements is similar. The developer has to analyse the movements of the 53 muscles in the human face, make a model set from the collected data for the AI news anchor to lean, and imitate the movements of facial muscles via programs

Both the technologies used to make Xin’s performance impressive are mature. The real difficulty lies in the third -the technology to match the pronunciations with facial movements so that Xin expressions vary according to the content of the news report. In fact, Xins expressions don' t always change according to the content. As a result, her expressions look anything but human. Actually. AI is still no match for human qualities.

Ann has been severely paralysed (瘫痪)for more than 18 years. She cannot speak and normally communicates. But now, she has been able to speak through an image on the computer using technology that translated her brain signals into speech and facial expressions.

The advance raises hopes that the technology could be on the edge of transforming the lives of people who have lost the ability to speak due to conditions like strokes (中风).

The latest technology uses tiny electrodes (电极) put on the top layer of the brain to detect electrical activity in the part of the brain that controls speech and face movements. These signals are translated directly into a digital avatar’s (化身) speech and facial expressions including smiling, frowning or surprise.

The team put 253 paper-thin electrodes on the top layer of Ann’s brain over an area important to speech. Afterwards Ann worked with the team to train the system to detect her unique brain signals for various speech sounds by repeating different phrases repeatedly. The computer learned 39 distinctive sounds and a Chat GPT-style language system was used to translate the signals into understandable sentences. This was then used to control an avatar with a voice personalised to sound like Ann’s voice before the injury, based on a recording of her speaking at her wedding.

The technology was not perfect, translating words incorrectly 28% of the time in a test run involving more than 500 phrases, and it generated brain-to-text at a rate of 78 words a minute, compared with the 110–150 words typically spoken in natural conversation. However, scientists said the latest advances in accuracy and speed suggest the technology is now at a point of being practically useful for patients. A crucial next step is to create a wireless form that could be put beneath the skull (颅骨).