【推荐1】China: Making Graduates Employable

Universities in China are facing similar demands to improve the employability of their graduates as those in the UK, new research among employers has revealed.     1     In Britain, the government has set up its own internship scheme to help graduates find work during the recession.

But what are the skills employers want and how much do they differ between the two nations?     2     The partnerships are funded by the government through the British Council under the second phase of the Prime Minister’s Initiative for International Education. In this scheme, universities are encouraged to exchange ideas and develop programmes aimed at improving the entrepreneurship and employability of graduates. “We found employers in both countries put a great emphasis on leadership qualities and initiative – things that are learned through the activities students engage in and the responsibilities they take on during their time at university,” Heffernan told a conference in London organized by the council, which is inviting proposals for 10 new UK-China partnerships.

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Generally, it includes family and friends and links with people working in other companies, voluntary organizations, or leisure activities. The Chinese also tend to take more time building up relationships with people before getting down to business. The University of Plymouth and its partner China Agricultural University in Beijing are working to determine the skills most likely to lead to employability and successful careers. Staff at both universities have conducted face-to-face and telephone interviews with local employers of graduates in three areas, marketing, human resources and finance-accounting.

    4     In the UK they chose communication including written and verbal communication, networking skills and foreign language ability - as the top priority followed by the ability to work in a team.

“Students in China generally lose touch with society and they need help to understand how companies work and what is involved in the different jobs and professions. They know very little outside the campus and that is where I think they differ from students in the UK. We can share our experiences.”

【推荐2】At first the question was how quickly people would get back to the office. Then it was whether they would ever return. The last three years has introduced in a major change in white-collar working patterns. The office is not dead but many professionals have settled into a hybrid (混合的) arrangement of some office days and some remote days.

Hybrid working has much to recommend: flexibility for employees, periods of concentration at home, bursts of cooperation in the office. A new paper from Harvard Business School describes an experiment in which workers at BRAC, a non-profit organisation in Britain, were randomly assigned to three groups, each spending different amounts of time working from home. The intermediate (中等的) group, who spent between 23% and 40% of their time in the office, performed best on various performance measures.

But a shift on this large scale is bound to raise tricky issues. In workplaces that have moved to hybrid work, there are still plenty of open questions. One is how to handle the impact of less time in the office for new joiners and younger workers. Research by Emma Harrington of the University of Iowa shows that software engineers receive more feedback on their code when the team sits next to each other in the office, especially new engineers. According to Nicholas Bloom of Stanford University, making new employees spend more time in the office can be a good way of integrating them into company culture and improving their competence. And these younger employees were most likely to quit when everyone was forced to go remote.

A second question concerns how strictly to enforce attendance on days when teams are meant to be in the office. An agreement holds that there should be agreed “anchor days” on which all the people come to work in the office; since the idea is to spend time together, as many people as possible should be there. But one person on the team might have moved somewhere else; someone else might have asked to stay home to let the repairer in. In practice, therefore, hybrid working still often means a mixture of people on screen and people in the office.

Other questions exist. How to define performance measures so managers do not spend time worrying about lazy workers at home? Do you require company-wide anchor days or team-level ones? The era of hybrid working is only just beginning, so it will take time for answers to emerge. But if there is a message from this first full year of hybrid working, it is that flexibility does not mean a free-for-all.

【推荐1】A study from 2010 said that raising prices by 1% without losing sales can increase profits by 8.7% on average. Getting the prices right can be difficult. Set them too high and you lose customers; set them too low and you leave money on the table.

To make more money, shopkeepers have been turning to price-optimization (优化) systems that predict how customers will respond to price changes. These systems are becoming cleverer thanks to advances in artificial intelligence (AI). While older systems used historical sales data to estimate (估计) price sensitivity for individual goods, the latest AI-powered systems can find relationships between multiple goods. These AI-powered systems use big data to estimate price sensitivity — how much demand increases as the price falls or how much demand decreases as the price goes up-for thousands of products. Price-sensitive (价格敏感的) goods can then be discounted and price-insensitive ones marked up.

All this makes pricing systems “much more three-dimensional”, observes Chad Yoes, the pricing official at Walmart, a supermarket. In February, Starbucks, a chain of coffee shops, expressed pride in its use of AI to price products “on an ongoing basis”. US Foods, a food company, says its pricing system can promote sales and profits.

Price-optimization may make prices change more. “Shopkeepers are pricing faster today than they ever have before,” says Matt Pavich of Revionics, a pricing-software firm. That is especially true in the fast-moving world of e-commerce. But even Walmart changes the prices of many items in its stores 2-4 times a year, says Mr Yoes. up from once or twice a few years ago.

Sysco, a food company, says the AI-powered system allows it to lower prices on “key value items” and raise prices on other products. It can thus increase profits by expanding sales while maintaining profits. That keeps investors content and shoppers sweet.

【推荐2】Generative A.I., the software engine behind ChatGPT, is seen as an exciting new wave of technology. But companies in every industry are mainly trying out the technology and thinking through the economics. Widespread use of it at many companies could be years away.

Generative A.I., according to forecasts, could sharply boost productivity and add trillions of dollars to the global economy. Yet the lesson of history, from steam power to the Internet, is that there is a long lag between the arrival of major new technology and its broad adoption — which is what transforms industries and helps fuel the economy.

The investment craze is going on right now. In the first half of 2023, funding for generative A.I. start-ups reached $15.3 billion, nearly three times the total for last year. Company technology managers are sampling generative A.I. software from a host of suppliers and watching to see how the industry develops.

In November, when ChatGPT was made available to the public, it was a “Netscape moment” for generative A.I., said Rob Thomas, IBM’s chief commercial officer, referring to Netscape’s introduction of the browser in 1994. “That brought the Internet alive,” Mr. Thomas said. But it was just a beginning, opening a door to new business opportunities that it took years to create.

In a recent report, a timeline for the widespread adoption of generative A.I. application was presented. It assumed steady improvement in currently known technology, but not future break-throughs. Its forecast for main-stream adoption was neither short nor precise, a range of 8 to 27 years. The broad range is explained by plugging in different assumptions about economic cycles, government regulation, company cultures and management decisions. “We’re not modeling the laws of physics here; we’re modeling economics and societies, and people and companies,” said Michael Chui, a partner at the McKinsey Global Institute. “What happens is largely the result of human choices.”

【推荐3】In the Pixar movie Up, a fun cartoon dog called Dug wears a magical collar which can detect and translate his barks and cries into fluent human speech. Humans have always been fascinated by the potential to communicate with the animals. This week, an article in the New York Times documented major efforts from a group of researchers using machine-learning algorithms (算法) to analyze the different calls of whales, chickens, bats, cats, and more.

There are several ways to train AI systems now. Typically, Al systems learn through training with labeled data of human language which can be well supplied by the Internet. But analyzing animal language is different. Scientists have to instruct software programs on what to look for, and how to organize the data. This process requires matching gained vocal (发声的) recordings with the visual social behaviors of animals. A group studying Egyptian fruit bats, for example, also used video cameras to record the bats themselves to provide context for the calls.

Many critics of this approach point out two weaknesses of current AI language models: being unable to truly understand the relationships between words and the objects in the real world, and scientists’ little understanding of animal societies. Al language models for humans rely on a computer mapping out the relationship between words and the contexts they could appear in. But these models have their own weak points, and can sometimes be a black box—researchers know what goes in and comes out, but don’t quite understand how the algorithm is arriving at the conclusion.

Another factor that researchers should take into account is that animal communications might not work at all like human communications. There might be unique elements to animal language due to physiological and behavioral differences.

Making a Translator for animals has been a popular project that’s been in the works for the last decade. Although some software has shown some success in identifying the basic vocabulary of certain animals, it’s still a far cry from understanding the complex animal languages.