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What's the difference between strong AI and weak AI?
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Strong AI makes the bold claim that computers can be made to think on

a level (at least) equal to humans and possibly even be conscious of

themselves.  Weak AI simply states that some "thinking-like" features

can be added to computers to make them more useful tools... and this

has already started to happen (witness expert systems, drive-by-wire

cars and speech recognition software).  What does 'think' and

'thinking-like' mean?  That's a matter of much debate.

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An entity set may not have sufficient attributes to form a primary key, and its primary key compromises of its partial key and primary key of its parent entity, then it is said to be Weak Entity set.
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Partial Key:

It is a set of attributes that can uniquely identify weak entities and that are related to same owner entity. It is sometime called as Discriminator.

Alternate Key:

All Candidate Keys excluding the Primary Key are known as Alternate Keys.

Artificial Key:

If no obvious key, either stand alone or compound is available, then the last resort is to simply create a key, by assigning a unique number to each record or occurrence. Then this is known as developing an artificial key.

Compound Key:

If no single data element uniquely identifies occurrences within a construct, then combining multiple elements to create a unique identifier for the construct is known as creating a compound key.

Natural Key:

When one of the data elements stored within a construct is utilized as the primary key, then it is called the natural key.
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(Don't say you have one, but give one that is really a "positive in disguise." I am sometimes impatient and do to much work myself when we are working against tight deadlines." Or "I compliment and praise my staff, but feel I can improve."  )
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AI has achieved quite a bit
 
-Deployed speech dialog systems by firms like IBM, Dragon and Lernout&Hauspie

- Financial software, which is used by banks to scan credit card
  transactions for unusual patterns that might signal fraud. One piece
  of software is estimated to save banks $500 million annually.

- Applications of expert systems/case-based reasoning: a computerized Leukemia
  diagnosis system did a better job checking for blood disorders than human
  experts.

- Machine translation for Environment Canada: software developed in the 1970s
  translated natural language weather forcasts between English and French.
  Purportedly still in use.

- Deep Blue, the first computer to beat the human chess Grandmaster

- Physical design analysis programs,such as for buildings and highways.

- Fuzzy controllers in dishwashers, etc.

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It depends what the game does.  If it's a two-player board game,
  look into the "Mini-max" search algorithm for games .  In
  most commercial games, the AI is is a combination of high-level
  scripts and low-level efficiently-coded, real-time, rule-based
  systems.  Often,  commercial games tend to use finite state machines
  for computer players. 
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That should not be so simple.  AI is a scientific and engineering discipline depending on
sophisticated Computer Science techniqes, mathematics, etc.  It also
is sub-divided into many distinct subfields. It is not really practical to expect to understand the technical
details of AI from a USENET forum.
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