ML

Question 1

Data Analysis vs. Data Science

Answer 1

Data Analysis/Analytics - analyses existing data

Data Science - explore the ideas

Question 2

Weak AI vs Strong AI (artificial general intelligence AGI))

Answer 2

Weak AI (Narrow AI) - focuses on performing specific tasks

Strong AI (artificial general intelligence AGI) - aims to create intelligent machines that are indistinguishable from the human mind.

Artificial Super Intelligence (ASI), super AI, super intelligence - super form of AI

Question 3

Loebner Prize competition

Answer 3

Loebner Prize competition - a human judge guesses whether the output was created by a human or a computer.

Question 4

Turing test

Answer 4

Turing test - In this test, there is a person known as the “interrogator” who seeks to identify a difference between computer-generated output and human-generated ones through a series of questions. If the interrogator cannot reliably discern the machines from human subjects, the machine passes the test. However, if the evaluator can identify the human responses correctly, then this eliminates the machine from being categorized as intelligent.

Question 5

Chinese Room Argument (CRA)

Answer 5

Imagine a person, who does not speak Chinese, sits in a closed room. In the room, there is a book with Chinese language rules, phrases and instructions. Another person, who is fluent in Chinese, passes notes written in Chinese into the room. With the help of the language phrasebook, the person inside the room can select the appropriate response and pass it back to the Chinese speaker.

Question 6

Supervised learning

Answer 6

Most common method. Labeled data. Predict trends. Best when you know what your output should be.

Question 7

Unsupervised learning

Answer 7

Not labeled, but machine should find the categorization itself.

Question 8

Unsupervised learning

Answer 8

Not labeled, but machine should find the categorization itself. Good for clustering things together and detect anomalies. identifies patterns.

Question 9

Reinforcement learning

Answer 9

You tell what the goal is and how to achieve it. You give rules and machine learns it. Robotics, games, alphago.

Question 10

Insight generation

Answer 10

Analyze more data. Faster. Uncover patterns in the data.

Question 11

ETL system

Answer 11

Extract, transform, Load

Question 12

Data Warehouse, Data Lake

Answer 12

Load (ETL) data to one place for easier analysis

Question 13

Data Warehouse, Data Lake

Answer 13

Load (ETL) data to one place for easier analysis
Data warehouse - data for a specific purpose
Data lake - data for anything

Question 14

AI flywheel

Answer 14

Data -> Predictions -> Customer Experience -> Traffic ->

Question 15

EMUC

Answer 15

Employes Metrics Users Customer

Question 16

Scrum vs Kanban

Answer 16

Kanban is more suitable as we can not predict the amount of time required for the task completion

Question 17

Training data

Answer 17

Data used to train a model (80%)

Question 18

Validation data

Answer 18

Data used to validate model performance

10% of all data

Question 19

Testing data

Answer 19

Is used to test the model once it is developed and fine-tuned (10% of all data)

Question 20

Precision

Answer 20

True/False - Positive/Negative
real\pred NO. Yes
NO. TN FP
YES FN TP

Precision= TP/TP+FP - optimize it when FP are important

Question 21

Recall

Answer 21

Recall=TP/TP+FN - optimize when False Negatives are important

Question 22

F1 score

Answer 22

Use it when both FP and FN are important

Question 23

Hierarchy of needs

Answer 23

1) AI
2) Data Science
3) Data Analytics
4) Data Infrastructure
5) Data Collection

Question 24

Triple track agile

Answer 24

Three simultaneous tracks: Discovery, Data and Delivery(to not block the data scientists)

Question 25

GDPR

Answer 25

General Data Protection Law

Question 26

COPPA

Answer 26

Children Online Privacy Protection Act

Question 27

FOIP

Answer 27

Freedom on Information and protection of privacy