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 analyze 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 AI 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 learning 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 for the AI to learn and read. The process for imitating facial movements is similar. The developer has to analyze the movements of the 53 muscles in the human face, make a model set from the collected data for the AI news anchor to learn, 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’s expressions vary according to the content of the news report. In fact, Xin’s 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.

Flinging brightly coloured objects around a screen at high speed is not what computers’ central processing units were designed for. So manufacturers of arcade machines invented the graphics-processing unit (GPU), a set of circuits to handle video games’ visuals in parallel to the work done by the central processor. The GPU’s ability to speed up complex tasks has since found wider uses: video editing, cryptocurrency mining and most recently, the training of artificial intelligence.

AI is now disrupting the industry that helped bring it into being. Every part of entertainment stands to be affected by generative AI, which digests inputs of text, image, audio or video to create new outputs of the same. But the games business will change the most, argues Andreessen Horowitz, a venture-capital (VC) firm. Games interactivity requires them to be stuffed with laboriously designed content: consider the 30 square miles of landscape or 60 hours of music in “Red Dead Redemption 2”, a recent cowboy adventure. Enlisting AI assistants to churn it out could drastically shrink timescales and budgets.

AI represents an “explosion of opportunity” and could drastically change the landscape of game development. Making a game is already easier than it was: nearly 13,000 titles were published last year on Steam, a games platform, almost double the number in 2017. Gaming may soon resemble the music and video industries in which most new content on Spotify or YouTube is user-generated. One games executive predicts that small firms will be the quickest to work out what new genres are made possible by AI. Last month Raja Koduri, an executive at Intel, left the chip maker to found an AI-gaming startup.

Don’t count the big studios out, though. If they can release half a dozen high-quality titles a year instead of a couple, it might chip away at the hit-driven nature of their business, says Josh Chapman of Konvoy, a gaming focused VC firm. A world of more choices also favors those with big marketing budgets. And the giants may have better answers to the mounting copyright questions around AI. If generative models have to be trained on data to which the developer has the rights, those with big back-catalogues will be better placed than startups. Trent Kaniuga, an artist who has worked on games like “Fortnite”, said last month that several clients had updated their contracts to ban AI-generated art.

If the lawyers don’t intervene, unions might. Studios diplomatically refer to AI assistants as “co-pilots”, not replacements for humans.

Do you check your work emails when you're on holiday? Do you call your colleagues to ask what's happening in the office even on a day of? I do. I'm addicted to work. When I see no signal on my mobile phone or no wi-fi by the beach, I get anxious.

Technology fuels our need to stay connected all the time. 【小题1】 However, they also make it more difficult for us to relax and recharge our batteries. Actually, not all employers want us to be connected all the time. German car maker Daimler, for example, has offered to automatically delete emails sent to employees while they're on holiday.

The sender of the email receives a message asking them to get in touch with another employee who's on duty, or to re-send the message at a later date. 【小题2】 No, says the company spokesman Oliver Wihofszki. According to him, the response is basically 99% positive because everybody says, “That's a real nice thing. I would love to have that, too.”

Dr. Christine Grant is an occupational psychologist at Coventry University in Britain. She's been studying workers' inability to relax when off duty. She says, “In my research I found a number of people who were burnt out. 【小题3】”

Employers and employees alike are realizing that you're more productive if you get the work-life balance right. 【小题4】 He was worried about spending too much time on his smartphone, so he created an app to monitor his usage. The app warns him if he goes beyond a certain limit.

Perhaps we should all take some time out to consider whether we're addicted to work, or addicted to technology, or both. 【小题5】