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IDSC 3001 - WEEK 12 - EMERGING TECHNOLOGIES. Exam Questions With Correct Answers $10.49   Add to cart

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IDSC 3001 - WEEK 12 - EMERGING TECHNOLOGIES. Exam Questions With Correct Answers

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©THEBRIGHT EXAM STUDY SOLUTIONS 8/27/2024 12:01 PM IDSC 3001 - WEEK 12 - EMERGING TECHNOLOGIES. Exam Questions With Correct Answers Disruptive technologies - answerTechnologies that create market shocks and catalyze growth (aka disruptive innovation) - 2 characteristics that make them threa...

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  • August 28, 2024
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©THEBRIGHT EXAM STUDY SOLUTIONS 8/27/2024 12:01 PM



IDSC 3001 - WEEK 12 - EMERGING
TECHNOLOGIES. Exam Questions With
Correct Answers

Disruptive technologies - answer✔✔Technologies that create market shocks and catalyze growth
(aka disruptive innovation)
-
2 characteristics that make them threatening

why disruptive technologies are threatening - answer✔✔(1) they come to market with a set of
performance attributes that existing customers don't value


(2) over time the performance attributes improve to the point where they invade established
markets

artificial intelligence (AI) - answer✔✔Computer software that can mimic or improve upon
functions that would otherwise require human intelligence
-
General
Narrow
-
3 qualities

3 qualities of AI - answer✔✔intentionality


intelligence


adaptability

intentionality - answer✔✔quality of AI

, ©THEBRIGHT EXAM STUDY SOLUTIONS 8/27/2024 12:01 PM


-
Systems are programmed to make decisions from historical or real-time data or both

intelligence - answer✔✔quality of AI
-
Often incorporate machine learning, deep learning, and data analytics that enable intelligent
decision making (not human intelligence!)

adaptability - answer✔✔quality of AI
-
Systems learn from real-time data and can refine their decision making capabilities to improve an
outcome

machine learning - answer✔✔subset of AI
-
Software that contains the ability to learn or improve through experience without being explicitly
programmed


Algorithms that find patterns in data
-
Supervised
Unsupervised

supervised machine learning - answer✔✔type of machine learning
-
Y = f(X)


CLASSIFICATION
When the output is categorical
EX:
color can be red, blue, green, etc.

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