Technology guide 4 - Artificial intelligence

Question 1

What is artifical intelligence?

Answer 1

• Theory and development of information systems able to perform tasks that normally require human intelligence.
• Subfield of computer science

Question 2

Explain what weak AI is

Answer 2

Weak AI (narrow AI) performs a useful and specific function that once required human intelligence to perform and does so at human levels or better

character recognition, 
speech recognition, 
machine vision, 
robotics, 
data mining, 
medical informatics, 
automated investing,
Etc.

Question 3

What is strong AI?

Answer 3

hypothetical artificial intelligence that matches or exceeds human intelligence.

Question 4

What is the Turing Test?

Answer 4

computer and human pretend to be women or men, and human has to identify through questions which is human.

Question 5

Describe the preservation of knowledge for NI and AI

Answer 5

NI: perishable from an organizational POV
AI: Permanent

Question 6

Describe the duplication and dissemination of knowledge in a computer for NI and AI

Answer 6

NI: difficult, expensive and takes time
AI: easy, fast, inexpensive

Question 7

Describe the total cost of knowledge for NI and AI

Answer 7

NI: can be erratic, inconstistant, incomplete
AI: consistent and thorough

Question 8

Describe the documentation of process and knowledge for both NI and AI

Answer 8

NI: difficult and expensive
Ai: easy and inexpensive

Question 9

HOw does creativity vary between Ai and NI?

Answer 9

NI: can be very high
AI: low, uninspired

Question 10

Describe the use of senory experiences for NI and AI

Answer 10

NI: direct and rich in possibilities
AI: must be interpereted first; limited

Question 11

Describe how well NI and AI recognize patterns and relationships

Answer 11

NI: fast, easy to explain
AI: not better than people in MOST cases

Question 12

Describe the reasoning of NI and AI

Answer 12

NI: makes use of wide context of experiences
AI: good in only narrow, focused, stable domains

Question 13

What are the reasons for advancement in AI?

Answer 13

• Advancements in chip technology
• Big data
• The internet and cloud computing
• Improved algorithms

Question 14

What are the stages of AI?

Answer 14

1. Recommendation systems
2. AI analyzes the data
3. Analyze additional data from smart devices and sensors
4. Integrate 3 previous steps to enable machines to sense and respond to the world around them

Question 15

Name 4 AI technologies

Answer 15

Expert systems (ESs)
Machine Learning
Deep Learning
Neural Networks

Question 16

What are ESSs?

Answer 16

• Expert systems
  1. Computer mimics human by applying expertise in a specific domain
  2. Either support or replace decision makers
  3. Make inferences and arrive at conclusions

Question 17

What is machine learning?

Answer 17

• Develop algorithms that learn from and make predictions about data
• Build a model from example inputs to make data-driven predictions or decisions
• ML is the ability to accurately perform new, unseen tasks, built on known properties learned from training or historical data that are labelled.
• Process:
  1. Have problem
  2. Create rule
  3. Apply rule
  4. Feedback
  5. Adjust rule
  6. repeat

Question 18

What is deep learning?

Answer 18

• Subset of machine learning where the computer discovers new patterns without being exposed to labelled historical or training data
• Example applications of deep learning include
• speech recognition,
• image recognition,
• natural language processing,
• drug discovery and toxicology, and
• customer relationship management.

Question 19

What are neural networks?

Answer 19

• set of virtual neurons that work in parallel to stimulate the way the human brain works
• The neural network assigns numerical values, or weights, to connections between the neurons
• Uses four main components:
  1. Inputs
  2. Weights
  3. Bias or threshold
  4. output

Question 20

Describe current neural networks

Answer 20

• Because of improvements in algorithms and increasingly powerful computer chips and storage, deep learning researchers are able to model many more layers of virtual neurons in neural networks than previously.
• Current neural networks are able to simulate billions of neurons.

Question 21

What is image processing?

Answer 21

• Each level of neural network manages a different level of abstraction
• To process an image, the first layer is fed with raw image
• That layer notes aspects of the images such as the brightness and colors of individual pixels, and how those properties are distributed across the image.
• The next layer analyzes the first layer’s observations into more abstract categories, such as identifying edges, shadows, and so on.
• The next layer analyzes those edges and shadows, looking for combinations that signify features such as eyes, lips, and ears.
• The final layer combines these observations into a representation of a face.

Question 22

What is a depp neural network?

Answer 22

• DL automates much of the feature extraction piece of the process
• Eliminates some of the manual human intervention
• Enables the use of large data sets

Question 23

What are the AI applications

Answer 23

• Computer Vision
• Natural Language Processing
• Robotics
• Speech Recognition
• Intelligent Agents
• Monitoring and Surveillance Agents
• User Agents