27 篇博文 含有标签「英语学习」

Machine learning is a type of computer programming that can discover patterns in large datasets. Let me explain this concept with a simple example.

Imagine we want to predict whether a user will enjoy a particular book. While we don't initially know how to make this prediction, we have survey data from six users that includes their gender, age, and whether they liked the book:

User    Like/Dislike    Gender    Age
A       Yes            M         18
B       Yes            M         36
C       No             F         20
D       No             F         61
E       Yes            M         72
F       No             F         49

Looking at this data, we can observe that book preference strongly correlates with gender but shows no clear relationship with age. The pattern shows that male users generally liked the book, while female users did not, regardless of their age. This insight allows us to make predictions about new customers - we might recommend the book to male customers based on this pattern.

Machine learning programs can identify these same patterns automatically. Once a machine learning program discovers a pattern, it can save this information and use it to make predictions about new users' preferences.

However, this example is deliberately simplified. While the human brain can process hundreds of data points with a few features (like gender and age), real-world scenarios often involve millions of data points with hundreds of features. Finding patterns in such massive datasets is impossible for humans but manageable for computers. This is where machine learning truly shines - a computer program can process millions of data points across hundreds of variables in minutes, discovering complex patterns that would be impossible for humans to detect manually.

This capability makes machine learning invaluable for analyzing large-scale data and making predictions based on complex patterns that extend far beyond what human analysis could achieve.

When I was a child, Spring Festival was a momentous occasion that filled me with excitement. Now, however, it feels more like any other holiday. This shift in perspective stems largely from how different life was back then. One reason Spring Festival felt so special during my childhood was our economic situation. Growing up in a remote area of China, we lived modestly. While I never experienced famine, our daily food choices were limited, especially during winter when we mainly subsisted on rice, corn, potatoes, Chinese cabbage, and radishes. Other foods were available in the market, but they were too expensive for regular consumption. Meat appeared on our table occasionally, but not daily. Spring Festival was the one time of year when we could enjoy special dishes. Today, all kinds of foods are readily available and much more affordable relative to income. There's little distinction between holiday meals and everyday dining. If I were in China, I would visit my parents and celebrate the festival with them – that would make the holiday feel more meaningful and distinct. But here in US, it's not even a formal holiday.

The Industrial Revolution began around 1760, and in just about 200 years, humanity progressed to landing on the moon. Many people are now planning missions to land on another planet within the next decade. How long might it take us to visit another star? If we maintain our current pace of technological development, it likely wouldn’t take more than a thousand years. And to cross the Milky Way galaxy? A million years should be more than sufficient.

The Milky Way is immense—it contains at least 100 billion stars and even more planets. Given its size, it’s highly likely that life has emerged and evolved on millions of these planets. Among those, even if just one planet is a million years more advanced than Earth, the intelligent beings there should have had the capability to reach us.

Yet, despite all this, it’s puzzling that we’ve never seen any signs of aliens. Why is that?

I have a few plants at home, most of which are edible. Before winter arrived, I bought some strawberry and celery seeds to grow indoors, as Boston's winters are too cold for outdoor planting. Using some large pots I already had, I purchased a bag of compost and planted the seeds—one kind per pot.

The celery seeds germinated exceptionally well; nearly all of them sprouted. Now, one of my pots is teeming with tiny celery seedlings. The strawberries, on the other hand, were much less successful. Out of about 20 seeds I sowed, only two have grown so far.

The biggest challenge with indoor planting in winter is ensuring the plants receive enough sunlight. In Boston, daylight hours are short in winter. To make matters worse, there are trees outside my windows that, even without leaves, sometimes block the sunlight. To address this, I’ve been using a table lamp as a supplemental light source for my plants.

“The Malthusian trap, or population trap, is a condition where excess population growth is curtailed by food shortages, leading to starvation.” — Wikipedia

Before 1800, famine was a persistent threat to people worldwide. Despite significant increases in food production throughout history, humanity remained trapped in cycles of starvation. This occurred because any surplus food would lead to higher birth rates, eventually pushing the population back to the brink of hunger.

For example, high-yield crops from the Americas, such as maize and sweet potatoes, were introduced to China around 1600. While these crops helped sustain a larger population, they did not eliminate famine. Instead, China's population tripled within a century, once again straining resources.

The situation began to change after 1800, as many countries broke free from the Malthusian Trap. Two key factors contributed to this transformation:

Technological Advancements: The development of modern technologies, including machinery, fertilizers, and improved agricultural methods, allowed food production to outpace population growth.

Population Control: Perhaps even more crucially, birth rates began to decline as societies adopted practices and policies to manage population growth. For the first time, the rate of population increase slowed, falling below the rate of food production growth.

Together, these factors ushered in a new era where famine became less of an inevitable reality for many parts of the world.

Desiree Clary was the daughter of a wealthy merchant in Marseille. In her youth, she fell in love with Napoleon Bonaparte, who at the time was a low-ranking officer and financially struggling. Desiree often supported Napoleon, both emotionally and financially.

As years passed, Napoleon’s talent on the battlefield began to shine, and he rose to the rank of general. Ambitious and focused on his career, Napoleon decided to marry Josephine de Beauharnais, a wealthy widow with strong connections to senior French officials. He saw this union as a strategic move that would greatly benefit his aspirations.

Desiree learned about Napoleon’s wedding through the newspapers and was furious at his betrayal. In her anger, she stormed into the wedding and publicly confronted him, expressing her hurt and indignation.

Ironically, it was at this wedding that Desiree caught the attention of another French general, Jean-Baptiste Bernadotte. The two began courting and eventually married.

Meanwhile, Napoleon’s career soared, culminating in his crowning as Emperor of France. Despite his immense success, he began to regret his treatment of Desiree and the decisions that had separated them. According to some accounts, Napoleon even reached out to Desiree, asking if she would consider becoming the Queen of France.

I want to talk about a movie called The Thirteenth Floor. It’s an older film, released in 1999, but it left such a strong impression on me that I still remember most of the story.

Douglas, the protagonist, is a software engineer at a video game company located on the thirteenth floor of a tall building. His company has developed a groundbreaking virtual reality game that simulates life in Los Angeles in 1937.

One day, after testing the game, Douglas’s boss discovers a shocking secret. He urgently calls Douglas and asks him to come over. However, by the time Douglas arrives, his boss has been murdered. Realizing that the secret and his boss’s death must be connected to the game, Douglas decides to enter the virtual reality world to investigate.

Inside the game, Douglas finds himself in a meticulously recreated version of 1937 Los Angeles, complete with streets, buildings, and people. These "people", though mere programs, believe they are real and behave just like actual humans.

Things take a surprising turn when one of the virtual characters begins to question the nature of his existence. This character suspects that the world he lives in isn’t real and presents his proof to Douglas.

After disconnecting from the game, Douglas starts to question his own reality. Was he, too, just a part of someone else's simulation? As the story unfolds, Douglas uncovers the truth—he is, in fact, a program living in another layer of someone’s virtual reality.

I started swimming at the recreation center in our town last month. One challenge I faced was knowing the time while swimming. Since I’m nearsighted, I can’t see the clocks in the pool without my glasses. So, I decided to buy a watch.

I had two options: a mechanical watch or an electric one. Electric watches are cheaper and very accurate, but they don’t last as long. The average lifespan of an electric watch is about 10 years, and even the more expensive ones typically last no more than 20 years.

On the other hand, high-quality mechanical watches can last much longer—some of the best have been running for over a hundred years. However, they’re far too expensive for me. Even if I bought a new electric watch every 10 years for the next 50 years, it would still cost less than one good mechanical watch.

In the end, I decided to buy an electric watch, and I’m very happy with it.

The definition of a "city" (市) in China differs somewhat from that in the US. Generally, cities in China are much larger than those in the US, both in terms of area and population.

China and the US are roughly the same size geographically. However, while China has about 30 provinces, the US is divided into 50 states. In China, a medium-sized province typically has fewer than 20 cities. In contrast, a single US state can have hundreds of cities, and large states like California may have thousands. As a result, a typical Chinese city can be 10 times larger than its US counterpart.

When considering population, at least 100 Chinese cities have populations exceeding one million. Meanwhile, in the US, only about 10 cities surpass the one-million-resident mark.

Because cities in the US are often much smaller and more numerous, people may not be familiar with all of their names or locations. In many cases, metropolitan area names are used informally instead of the actual city name. For example, if someone asks where I live, I’ll say “Boston” instead of “Brookline,” my actual hometown, since many people might not know where Brookline is.

I often read articles or posts shared by strangers online. Occasionally, I wonder where the author lives, and I’ve realized there are many ways to figure out someone’s general location based on their posts.

Many articles and posts are shared on blogs or social media platforms. Some users make their profiles public, which can include location information. This makes it easy to determine where they live.

Even if someone doesn’t explicitly state their location, clues often emerge in their posts. For instance, photos shared online often contain location tags, either added manually or automatically by the device used to take the picture. Additionally, people frequently share details about the restaurants, local shops, or parks they visit, which can provide strong hints about their location.

Sports teams can also be a significant indicator of where someone lives. For example, if a person is a big fan of the Boston Red Sox, it’s likely they live in or around the Boston area.

By piecing together these details, it’s often possible to make an educated guess about where a person resides, even without direct information.