They found that by analyzing the topology of the web—which pages link to other pages—computers could roughly determine the most “interesting” and “relevant” pages .
The importance of a page about Sergei Prokofiev could be determined, in part, from the number of pages that linked to it with the link text “Sergei Prokofiev.” And in part from the importance of those other pages vis-à-vis Prokofiev.
The second FOREACH loop goes through that list of unique words, looking for the one that has the largest count.
After determining the most commonly occurring word, it prints the word and the number of occurrences.
One early search engine attempted to mask its inadequacy by achieving a semantic understanding of the queries being entered in its search boxes.
Ask Jeeves (now known simply as Ask) encouraged users to type actual questions rather than keywords: “Where can I buy shoes?
By the mid-’90s a number of researchers—including, most famously, two Stanford grad students named Sergey Brin and Larry Page—were trying to improve the quality of search results by ranking their importance.
Alan Turing, the analytical genius who broke the German ENIGMA code during World War II and formulated some of the fundamental principles of computer science, famously proposed a “test” for whether a computer was intelligent: could it, in text-only conversation, convince a person that it was human?
Turing predicted in 1950 that a computer would have at least 128 megabytes of memory and be able to pass his test with reasonable frequency by the year 2000.
Symbolic logic cannot admit such ambiguities: they must be spelled out explicitly in the translation from language to logic, and computers can’t figure out the complex, ornate, illogical rules of that translation on their own.
Second, a program analyzing natural language must determine what that sentence represents in the world, or, to put it another way, its meaning.