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In initial tests run during 2006 by David Ferrucci, the senior manager of IBM's Semantic Analysis and Integration department, Watson was given 500 clues from past Jeopardy! While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues, Watson's first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. InformationWeek described Kelly as "the father of Watson" and credited him for encouraging the system to compete against humans on Jeopardy! By 2008, the developers had advanced Watson such that it could compete with Jeopardy! By February 2010, Watson could beat human Jeopardy! For each clue, Watson's three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game's signalling device, but had trouble in a few categories, notably those having short clues containing only a few words.
This is called dependency hell, existing on many platforms. The (infamous) Windows variant is commonly known as DLL hell. This problem cannot occur if each version of each library is uniquely identified and each program references libraries only by their full unique identifiers. The "DLL hell" problems with earlier Windows versions arose from using only the names of libraries, which were not guaranteed to be unique, to resolve dynamic links in programs. Microsoft Windows checks the registry to determine the proper place to load DLLs that implement COM objects, but for other DLLs it will check the directories in a defined order. SetDllDirectory() function; the System32, System, and Windows directories; then the current working directory; and finally the directories specified by the PATH environment variable. Applications written for the .NET Framework (since 2002), also check the Global Assembly Cache as the primary store of shared dll files to remove the issue of DLL hell.
Typically, item response theory is employed as the psychometric model. One reason item response theory is popular is because it places persons and items on the same metric (denoted by the Greek letter theta), which is helpful for issues in item selection (see below). In CAT, items are selected based on the examinee's performance up to a given point in the test. However, the CAT is obviously not able to make any specific estimate of examinee ability when no items have been administered. So some other initial estimate of examinee ability is necessary. CAT just assumes that the examinee is of average ability - hence the first item often being of medium difficulty level. As mentioned previously, item response theory places examinees and items on the same metric. Therefore, if the CAT has an estimate of examinee ability, it is able to select an item that is most appropriate for that estimate. Technically, this is done by selecting the item with the greatest information at that point. This was generat ed wi th G SA Conte nt Generat or Demoversion.
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