2 . “NON-FUNGIBLE TOKENS” (NFTs) leapt from the more obscure corners of the internet into the mainstream in March 2021 when Christies, a British auction house, sold a digital work of art for $69m. What it actually flogged was an NFT, a cryptocurrency chit that proves a buyer owns an intangible marker connected to a unique piece of digital art, music or other item. Much like René Magritte’s painting of a pipe that proclaims “this is not a pipe” an NFT is not the thing it represents. Tweets, videos of basketball dunks and even the source code to the world wide web have been sold as NFTs in recent months. From June to September they generated almost $11bn in sales, an eight-fold increase on the previous four months, according to DappRadar, a market tracker. What exactly is an NFT? And why are people spending tens of millions of dollars on them?

An NFT is a record on a cryptocurrency’s blockchain (an immutable ledger（不可篡改的账本） that can record more than just virtual coins) that represents pieces of digital media. Invented a few years ago, it can link not only to art but also to text, videos or bits of code. Promoters of NFTs claim that they solve a thorny（棘手的） problem with digital art: how to own an original. For creators who freely upload their work or sell it as identical copies, the concept of an original is difficult to pin down（确定）. Exclusivity is impossible to enforce when digital files can be shared freely on the internet. But collectors want the cachet that comes with having an exclusive claim on an artwork. This is where NFTs fit in.

To mint（铸币，造币） an NFT, the creator establishes a unique record of the artwork, generally on a website. Then the creator places the record on a blockchain, usually Ethereum’s, which requires a transaction fee known as gas. Possession of a private encryption key associated with the transaction proves ownership. This gives an artist or collector something to sell. An NFT may link to a version of the work, but rarely includes the rights to reproduce or distribute it. That differentiates it from a commercial licensing arrangement, too.

NFTs have myriad（众多的） problems. They often change hands using cryptocurrencies, many of which currently have sky-high valuations, leading to fears of a bubble. Anyone can mint an NFT, since the systems involved are decentralised（去中心化的）, although doing so with someone else’s work could be infringe their copyright. Some artists have already claimed misappropriation of their work.Most NFTs are simply links to images. Unless they have been issued in a certain way to ensure they are tamper-proof（防更改的） these can in theory be meddled with after the sale. The high electricity usage of blockchains—Bitcoin’s is greater than that of Chile—has prompted arguments over whether artists are contributing to climate change by embracing NFTs. And ownership may be difficult to prove in the long term, as web-based records may not last for ever.

Yet NFTs have some value beyond the cryptocurrency hype （加密货币炒作）: artists struggle to make a living when their works can be easily replicated and pirated. NFTs will create new problems in an attempt to solve old ones, but for now many creators and collectors are too busy cashing in to care.

4 . Computers are getting better at writing their own code but software engineers may not need to worry about losing their jobs just yet.

DeepMind, a U.K. artificial intelligence lab acquired by Google in 2014, announced Wednesday that it has created a piece of software called AlphaCode that can code just as well as an average human programmer. The London-headquartered firm tested AlphaCode’s abilities in a coding competition on Codeforces — a platform that allows human coders to compete against one another. “AlphaCode placed at about the level of the median competitor, marking the first time an AI code generation system has reached a competitive level of performance in programming competitions,” the DeepMind team behind the tool said in a blogpost.

But computer scientist Dzmitry Bahdanau wrote on Twitter that human-level coding is “still light years away.” “The system ranks behind 54.3% participants,” he said, adding that many of the participants are high school or college students who are just learning their problem-solving skills. Bahdanau said most people reading his tweet could “easily train to outperform AlphaCode.”

Researchers have been trying to teach computers to write code for decades but the concept has yet to go mainstream, partly because the AI tools that are meant to write new code have not been versatile enough.

An AI research scientist, who preferred to remain anonymous as they were not authorized to talk publicly on the subject, told CNBC that AlphaCode is an impressive technical achievement, but a careful analysis is required of the sort of coding tasks it does well on, versus the ones it doesn’t.The scientist said they believe AI coding tools like AlphaCode will likely change the nature of software engineering roles somewhat as they mature, but the complexity of human roles means machines won’t be able to do the jobs in their entirety for some time.“You should think of it as something that could be an assistant to a programmer in the way that a calculator might once have helped an accountant,” Gary Marcus, an AI professor at New York University, told CNBC. “It’s not one-stop shopping that would replace an actual human programmer. We are decades away from that.”

DeepMind is far from the only tech company developing AI tools that can write their own code.

Last June, Microsoft announced an AI system that can recommend code for software developers to use as they work. The system, called GitHub Copilot, draws on source code uploaded to code-sharing service GitHub, which Microsoft acquired in 2018, as well as other websites. Microsoft and GitHub developed it with help from OpenAI, an AI research start-up that Microsoft backed in 2019. The GitHub Copilot relies on a large volume of code in many programming languages and vast Azure cloud computing power.

Nat Friedman, CEO of GitHub, describes GitHub Copilot as a virtual version of what software creators call a pair programmer — that’s when two developers work side-by-side collaboratively on the same project. The tool looks at existing code and comments in the current file, and it offers up one or more lines to add. As programmers accept or reject suggestions, the model learns and becomes more sophisticated over time. The software makes coding faster, Friedman told CNBC. Hundreds of developers at GitHub have been using the Copilot feature all day while coding, and the majority of them are accepting suggestions and not turning the feature off, Friedman said.In a separate research paper published on Friday, DeepMind said it had tested its software against OpenAI’s technology and it had performed similarly. Samim Winiger, an AI researcher in Berlin, told CNBC that every good computer programmer knows that it is essentially impossible to create “perfect code.”“All programs are flawed and will eventually fail in unforeseeable ways, due to hacks, bugs or complexity,” he said.“Hence, computer programming in most critical contexts is fundamentally about building ‘fail safe’ systems that are ‘accountable’.”

In 1979, IBM said “computers can never be held accountable” and “therefore a computer must never make a management decision.”Winiger said the question of the accountability of code has been largely ignored despite the hype around AI coders outperforming humans.

“Do we really want hyper-complex, intransparent, non-introspectable, autonomous systems that are essentially incomprehensible to most and uncountable to all to run our critical infrastructure?” he asked, pointing to the finance system, food supply chain, nuclear power plants and weapons systems.

Perhaps no other scientist has had a     1    (great) impact on China’s aerospace than Qian Xuesen. He was a well-respected man,     2    served his homeland with effort, achievement, and devotion. Born in Hangzhou in 1911, Qian attended schools in Beijing and Shanghai. In 1935, he went to the US    3    further study and then worked there. After overcoming some difficulties, he returned to China in 1955, and was put in charge of developing China’s rocket science as well as the space and missile programme.     4    (face) with challenges, Qian didn’t feel discouraged. When     5    (ask) “Can we Chinese possibly make missiles?”, his reply was “Why not?”Under his leadership, China developed its own missiles, rockets, and man-made satellites successfully. Qian was     6    (knowledge). However,    7     made him an outstanding and creative scientist was    8    (probable) his interest in other things like music and drawing, which gave him     9    (inspire) in scientific research. The whole country was     10    (sad)by Qian’s death in 2009.