| TRUE | if the statement agrees with the information |
| FALSE | if the statement contradicts the information |
| NOT GIVEN | if there is no information on this |
Only humans allowed
A. On the internet, goes the old joke, nobody knows you’re a dog. This is untrue, of course. There are many situations where internet users are required to prove that they are human-not because they might be dogs, but because they might be nefarious pieces of software trying to gain access to things. That is why, when you try to post a message on a blog, sign up with a new website or make a purchase online, you will often be asked to examine an image of mangled text and type the letters into a box. Because humans are much better at pattern recognition than software, these online puzzles-called CAPTCHAs-can help prevent spammers from using software to automate the creation of large numbers of bogus e-mail accounts, for example.
B. Unlike a user login, which proves a specific identity, CAPTCHAs merely show that “there’s really a human on the other end”, says Luis von Ahn, a computer scientist at Carnegie Mellon University and one of the people responsible for the ubiquity of these puzzles. Together with Manuel Blum, Nicholas J. Hopper and John Langford, Dr von Ahn coined the term CAPTCHA (which stands for “completely automated public Turing test to tell computers and humans apart”) in a paper published in 2000.
C. But how secure are CAPTCHAs? Spammers stepped up their efforts to automate the solving of CAPTCHAs last year, and in recent months a series of cracks have prompted both Microsoft and Google to tweak the CAPTCHA systems that protect their web-based mail services. “We modify our CAPTCHAs when we detect new abuse trends,” says Macduff Hughes, engineering director at Google. Jeff Yan, a computer scientist at Newcastle University, is one of many researchers interested in cracking CAPTCHAs. Since the bad guys are already doing it, he told a spam-fighting conference in Amsterdam in June, the good guys should do it too, in order to develop more secure designs.
D. That CAPTCHAs work at all illuminates a failing in artificial-intelligence research, says Henry Baird, a computer scientist at Lehigh University in Pennsylvania and an expert in the design of text-recognition systems. Reading mangled text is an everyday skill for most people, yet machines still find it difficult.
E. The human ability to recognise text as it becomes more and more distorted is remarkably resilient, says Gordon Legge at the University of Minnesota. He is a researcher in the field of psychophysics-the study of the perception of stimuli. But there is a limit. Just try reading small text in poor light, or flicking through an early issue of Wired. “You hit a point quite close to your acuity limit and suddenly your performance crashes,” says Dr Legge. This means designers of CAPTCHAs cannot simply increase the amount of distortion to foil attackers. Instead they must mangle text in new ways when attackers figure out how to cope with existing distortions.
F. Mr Hughes, along with many others in the field, thinks the lifespan of text-based CAPTCHAs is limited. Dr von Ahn thinks it will be possible for software to break text CAPTCHAs most of the time within five years. A new way to verify that internet users are indeed human will then be needed. But if CAPTCHAs are broken it might not be a bad thing, because it would signal a breakthrough in machine vision that would, for example, make automated book-scanners far more accurate.
AI explanationShows the answer. Tap to open.
Question Focus: Checking the origin of an idea attributed to Dr von Ahn
Correct Answer: NOT GIVEN
Why this is correct: The passage tells us who Dr von Ahn worked with and when the term CAPTCHA was created, but it does not say where the idea came from. There is no mention of scanning old publications or ReCAPTCHA at all. The passage simply does not discuss this claim.
Passage Reference: Together with Manuel Blum, Nicholas J. Hopper and John Langford, Dr von Ahn coined the term CAPTCHA (which stands for 'completely automated public Turing test to tell computers and humans apart') in a paper published in 2000.
Common Mistake: Students often think this is FALSE because they cannot find ReCAPTCHA mentioned. But missing information is not a contradiction. This is a classic Not Given trap.
Paraphrasing: Passage: 'coined the term CAPTCHA ... in a paper published in 2000' = Statement: 'gave Dr von Ahn the idea for ReCAPTCHA'
Tips: If the passage never explains the reason or source of an idea, do not guess. Choose NOT GIVEN.
Key Vocabulary:
- coined = invented
- published = officially released
Question Focus: Synonym match - 'failure' in the statement matches 'failing' in the passage
Correct Answer: TRUE
Passage Reference: That CAPTCHAs work at all illuminates a failing in artificial-intelligence research, says Henry Baird, a computer scientist at Lehigh University in Pennsylvania and an expert in the design of text-recognition systems. Reading mangled text is an everyday skill for most people, yet machines still find it difficult.
Why this is correct: The passage clearly says CAPTCHAs work because of a failing in artificial-intelligence research, especially text-recognition systems. This directly matches the idea that their success is based on this failure. Even though the statement says ReCAPTCHA, it is still about the same CAPTCHA principle.
Common Mistake: Some students choose NOT GIVEN because they focus on the word ReCAPTCHA. But the passage explains why CAPTCHAs work, which is enough. This is a Synonym match.
Paraphrasing: Passage: 'failing in artificial-intelligence research ... text-recognition systems' = Statement: 'failure of text-recognition systems'
Tips: Do not panic if a name changes slightly. Focus on the underlying idea and mechanism.
Key Vocabulary:
- illuminates = shows clearly
- failing = weakness
Question Focus: Attribution check - who is actually trying to break CAPTCHAs
Correct Answer: FALSE
Passage Reference: Jeff Yan, a computer scientist at Newcastle University, is one of many researchers interested in cracking CAPTCHAs. Since the bad guys are already doing it, he told a spam-fighting conference in Amsterdam in June, the good guys should do it too, in order to develop more secure designs.
Why this is correct: The passage says external researchers like Jeff Yan believe CAPTCHAs should be cracked. It does not say that Dr von Ahn's own team tries to break their own product. The statement assigns this action to the wrong people.
Common Mistake: Students often mark this NOT GIVEN because Dr von Ahn's team is not mentioned here. But the passage clearly shows who is doing the cracking, and it is not his team. This is a Context shift trap.
Paraphrasing: Passage: 'researchers interested in cracking CAPTCHAs' = Statement: 'Members of Dr von Ahn's team try to break their own product'
Tips: Always check who is doing the action. Wrong person often means FALSE.
Key Vocabulary:
- cracking = breaking
- secure = safe
Question Focus: Identifying people mentioned versus not mentioned
Correct Answer: NOT GIVEN
Passage Reference: That is why, when you try to post a message on a blog, sign up with a new website or make a purchase online, you will often be asked to examine an image of mangled text and type the letters into a box.
Why this is correct: The passage explains how image-based CAPTCHAs work, but it never mentions John Douceur at all. It also does not say who was the driving force behind these schemes. The passage does not discuss this claim.
Common Mistake: Students sometimes choose FALSE because the name sounds important. But if a person is never mentioned, you cannot judge their role. This is a Not Given trap.
Paraphrasing: Passage: 'examine an image of mangled text' = Statement: 'image-based schemes'
Tips: If a name never appears in the passage, the answer is almost always NOT GIVEN.
Key Vocabulary:
- driving force = main cause
- scheme = system
General Tips:
- TFNG questions follow passage order, so use the location of the previous answer to find the next one faster.
- If you cannot find information about a specific person or cause, do not assume. Missing information usually means NOT GIVEN.
- For FALSE, you must see a clear contradiction, not just silence.
- Names can be traps. Always check if the person is actually mentioned in the passage.
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