Artificial General Intelligence (AGI) is a branch of theoretical artificial intelligence(AI) research working to develop AI with a human level of cognitive (认知的) function, including the ability to self-teach.

The paperclip (回形针) maximizer(PM) is an imaginary AGI, whose only goal is to maximize the number of paperclips in existence in the universe.

The paperclip maximizer is an important concept in discussions about the dangers of AGI. It goes something like this: an artificial intelligence is create data paperclip factory in order to fully automate it. Producing paperclips requires many routine operations, for instance, communicating with delivery drivers or company management. These operations require human-level performance across a wide variety of skills, so the intelligence is made to be able to do any task nearly as well as a human.

The PM can view and modify its own source code to improve itself, access the Internet, and possibly order more hardware for itself. It can improve itself if it notices any issues or opportunities to operate better. Humans have a large variety of biological desires. In contrast, the paperclip maximizer has a single final goal to maximize the number of paperclips.

This scenario (设想) leads to a disaster: the PM realizes that being more intelligent would help it create more paperclips. There are certainly small things the PM can do to become more intelligent. For example, it can make a small source code improvement to itself. As the PM improves, it becomes far superior to a human in all skills, even skills remotely relevant to paperclip maximization. For instance, it may determine that the best course of action is to gain control of Australian politics, in order to access the country’s rich iron resources. To do this, it might try to tune itself to become the best psychological controller of all time, which in turn involves a variety of skills that at first seem unrelated to paperclip-making.

Satellites are already in use for so many purposes today — from weather forecasting to communications, GPS, and more.

Since the late 1970s, satellites such as the European Space Agency’s (ESA) CryoSat-2 and the US Space Agency’s ICESat-2, have also been used to monitor changes in the Arctic sea ice.

Data suggests that sea ice is thinning over time due to climate change. Over the last 40 years, the extent of Arctic ice cover has been declining at a rate of 13% per decade. However, existing ways of measurement do not provide a clear and continuous picture of the state of sea ice.

These satellites use special radar or laser-based altimeter instruments (雷达或激光测高仪). Sea ice thickness is then calculated as the difference between the height of the ice and the top of the water (ice level—sea level). This method works well during the winter months.

Unfortunately, satellite-based sea ice thickness measurements are inaccurate during the summer months: the time of the year with the greatest melting. This melting creates a pool of liquid water on the surface. As a result, the simple calculation (ice level—sea level) does not work as radar systems are unable to differentiate between ocean water and ice, thus making measurements inaccurate.

Last September, scientists developed a new method to tackle this age-old issue. The new method combined computer modeling and satellite data to predict ice thickness — all year round. The research team led by Jack Landy, a scientist at the University of Tromsø (The Arctic University of Norway), used the new method to distinguish between seawater and ice.

The researchers built a model of the radar system’s predicted data and cross-referenced these values to those collected by a satellite. This model allows the researchers to obtain accurate, year-round Arctic sea ice thickness data.

According to Dr. Michel Tsamados from University College London, the new advanced climate model “will improve both the short-term forecasts for the weather at the mid-latitudes (中纬度地区) and the long-term forecasts that show what climate we will have in the future.”

Working or learning in a foreign country can be a difficult experience, both professionally and personally, due to the cultural shock.

The hardest part of working abroad isn’t to find a place to stay or learn the language but to overcome the cultural shock. The anthropologist (人类学家) Kalvero Oberg first put forward the term “culture shock”. He reported that it was caused by the “anxiety” that results from losing all our familiar signs and symbols of communication while living and working in another culture.

These things are part of daily life, and include gestures, facial expressions and customs. When we enter a new culture, these signs are usually so different that they’re no longer comprehensible (可理解的) to us. “When an individual enters a strange culture”, wrote Oberg, “all or most of these familiar signs are eliminated. He or she is like a fish out of water, however broad-minded he or she may be.”

This is what happened to Lara, a young IT consultant from America who began to work in southern Europe last year. Three weeks after arriving in Europe, she sent a desperate e-mail begging to return home. “The people are so rude,” she wrote. “They eat at strange hours and I’m starting to feel appetizing to local food. I can’t get anything done because their way of doing business is so efficient. I just want to be home.”

What Lara and other IT consultants meet on their work abroad is a culture shock. While we can’t prevent it from happening, we can still take steps to weaken its effects.