Human memory is notoriously unreliable. Even people with the sharpest facial-recognition skills can only remember so much.

It’s tough to quantify how good a person is at remembering. No one really knows how many different faces someone can recall, for example, but various estimates tend to hover in the thousands—based on the number of acquaintances a person might have.

Machines aren’t limited this way. Give the right computer a massive database of faces, and it can process what it sees—then recognize a face it’s told to find—with remarkable speed and precision. This skill is what supports the enormous promise of facial-recognition software in the 21st century. It’s also what makes contemporary surveillance systems so scary.

The thing is, machines still have limitations when it comes to facial recognition. And scientists are only just beginning to understand what those constraints are. To begin to figure out how computers are struggling, researchers at the University of Washington created a massive database of faces—they call it MegaFace—and tested a variety of facial-recognition algorithms (算法) as they scaled up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 700,000 different people—and not just a large database featuring a relatively small number of different faces, more consistent with what’s been used in other research.

As the databases grew, machine accuracy dipped across the board. Algorithms that were right 95% of the time when they were dealing with a 13,000-image database, for example, were accurate about 70% of the time when confronted with 1 million images. That’s still pretty good, says one of the researchers, Ira Kemelmacher-Shlizerman. ”Much better than we expected,“ she said.

Machines also had difficulty adjusting for people who look a lot alike—either doppelgangers (长相极相似的人), whom the machine would have trouble identifying as two separate people, or the same person who appeared in different photos at different ages or in different lighting, whom the machine would incorrectly view as separate people.

“Once we scale up, algorithms must be sensitive to tiny changes in identities and at the same time invariant to lighting, pose, age,”Kemelmacher-Shlizerman said.

The trouble is, for many of the researchers who’d like to design systems to address these challenges, massive datasets for experimentation just don’t exist—at least, not in formats that are accessible to academic researchers. Training sets like the ones Google and Facebook have are private. There are no public databases that contain millions of faces. MegaFace’s creators say it’s the largest publicly available facial-recognition dataset out there.

“An ultimate face recognition algorithm should perform with billions of people in a dataset,” the researchers wrote.

Donald Trump has been known to do it in the middle of the night. Kim Kardashian has done it more than 22,000 times. Many people don't understand why you would want to do it at all.

We’re talking about tweeting and, unlike posting your entire life on Facebook, this social media activity demands a particular set of skills.

Why use Twitter? It’s a way to have a one-on-one virtual connection with your heroes and anybody who interests you. Likewise, you can share your life and views with an almost limitless number of people. And it all has to be done within the limit of 140 characters of text – plus photos, videos and links – which really helps you focus your mind.

Katy Perry has the most Twitter followers, with 95.6 million of the micro-blogging site’s 317 million monthly active users. Justin Bieber isn’t far behind with 91.5 million, followed by Barack Obama and Taylor Swift with around 83 million each. The heaviest hitters of Twitter are pop stars, heroes of sport and other celebrities. But you don’t need to be famous to create a buzz with the right tweets at the right time.

In fact, Katy Perry is a great example of how to do it well. She has a bubbly tweeting style that mashes up chat about her tours and her music, insights into her emotional roller coaster over the US election, and playful descriptions about her daily life. "Done with my Christmas shopping", she tweeted, and posted a link to her Instagram page that featured crazy gadgets like a "pet emergency jacket" and a "chocolate donut camera".

To get started on Twitter, pick a simple, memorable user name like @KatyPerry, post a short profile and choose a photo. Some tips: post your own photo (not a photo of your dog, it’s not Facebook) and don’t be an “egg person” – referring to the absent egg-shaped image when a user hasn’t posted their own picture. In that case, Twitter won’t get you any followers or respect.

Then, it’s time to start tweeting by sending your updates (“what’s happening?”) and following people. Building up an army of followers can take time. Generally, the more people you follow and the more you tweet, the more followers you’ll get. To become a Twitter star, you need to carve out a reputation for posting original, eye-catching tweets on trending topics – and maximise your visibility by mastering the use of hashtags.

Twitter Analytics will show you how your tweets are performing and who is following you – right down to their age category, country, income bracket, gender and interests. If your popularity is flagging and your Twitter ego is keeping you awake at night, you can turn to the business of buying thousands of "followers" from online sites. But do be ware – there may be a lot of fake profiles in there and, at the end of the day, it’ll feel like paying a crowd of people you don’t know to come to your birthday party.

Happy tweeting!

The problem with students using Google is not that the search leader is unable to offer useful educational content. It’s that finding that content using simple search terms is a difficult art to master. But some educational companies and organizations aim to make it easier to find useful educational content among the Web. They are forming a working group to come up with more detailed criteria(标准)that could eventually be added into the search lines for Google, Bing, and Yahoo !

The project was encouraged by a joint move by those major search engines to help users do more effective Web­searches. The idea behind the new education corporation is to determine a common “framework” for narrowing search results for education content—by subject area, or source type, or content type, or any number of possible criteria. The goal is also to persuade publishers of educational content to use a matching set of tags(分类)to help the search engines sort out their content more easily.

Search engines are used by college students, but they were not designed for them. This has been a subject of much   handwringing (绝望) among professors, who worry that students are not finding the most reliable content on the Internet even if more and more content providers of good fame—textbook publishers, scholars, universities and many others—have been putting useful academic resources on the open Web.

Michael Johnson, a member who will be serving on a working group devoted to developing the framework over the next six months or so, said “The project is aimed at benefiting the   publishers   of   educational   content   as   much   as students. ”