AI 900 (Masoud) Flashcards
What is AI
It is basically the concept of human intelligence inside of machines.
Artificial General Intelligence
When you talk about general A.I., we’re talking about a computer system that can take control of itself. It’s self-aware. It can teach itself a new task, quite a common theme within some science fiction movies. It does not currently exist in real life in any great form.
Azure ====> A.I.
Azure is not general A.I.
Narrow Artificial Intelligence
Computer systems which use human intelligence but have very strong limitations in what they can do.
such as: Siri, Cortana
Machine Learning
The study of computer algorithms that improve automatically through experience.
1.Unsupervised learning : ability to find patterns in data without human help
- Supervised learning: humans label the data and give general guidance
Natural Language Processing
The second area of AI:
Allows a machine to read and understand human language.
Machine translation, question answering, sentiment analysis
Perception
Third area of AI:
The ability to use input from sensors- images, audio, lidar (light detection and ranging), sonar (Sound Navigation and Ranging), radar (Radio Detection And Ranging), and touch.
Covers things like facial recognition, speech recognition and object recognition.
What is ML
ML allows computers to use data to forecast the future without specifically being programmed.
In ML, a model is a ….
is a program that can be used to recognize a pattern in a data
- A model can be used to “predict” future behaviors.
- A model can be used to “categorize” something as one thing or another
- A model can be used to “recognize” people, objects and landmarks using unseen images.
- A model can be used to “understand” the context of natural human text or speech
train
You “train” a model using “training data”
Evaluate
You “evaluate” a model using “test data” to measure how accurate is it.
Deploy
Once a model has been deployed, it can recognize patterns in data it has never seen before
Some common AI workloads
- Prediction and Demand Forecasting
- Anomaly detection
- Computer Vision
- Natural Language Processing (NLP)
- Conversational AI - Chat Bots
Using ML to predict
- Give the machine all the relevant data you know
- Tell it for which field you want to predict
- It develops a model which it uses to make a prediction
the prevalence of AI
causes some ethical and moral challenges
unintended consequences of leaving important decision to a computer
Unintended consequences
- Decisions that are wrong
- Decisions that are illegal (or at least , go against your own values)
- Decisions that cannot be explained by anybody
- Decisions that are harmful to society at large
Six Principles Should Guide AI Development
code: “TARIFS “
- Transparency
- Accountability
- Reliability and safety
- Inclusiveness
- Fairness
- Security and Privacy
Principle of Fairness
AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways.
Principle of Reliability and Safety.
To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions
Principle of Privacy and Security
Many countries and regions in the world are developing new standards and laws
to try to protect the data of its citizens. Laws are always slower than technology.
Principle of Inclusiveness
At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences.
Principle of Transparency
When AI systems are used to help inform decisions that have tremendous impacts on people’s lives, it is critical that people understand how those decisions were made.
Principle of Accountability
The people who design and deploy AI systems must be accountable for how their systems operate.
Common Machine Learning Types
(Regression)
A type of supervised learning
The ability to predict the outcome variable
given 1 or more predictor variables.
(Predictor variable, also known sometimes as the independent variable, is used to make a prediction for dependent variables)
The result is numeric - price, amount, size, etc.
It’s about finding the relationships between the variables between the X axis and the Y axis.
Common Machine Learning Types
(Classification)
A type of supervised learning
Cluster analysis - assign a score to the odds of it belonging to a cluster
What type of fruit is this?
Types of Classification
1- Binary classification
only has two answers,0 and 1
2- Multi-class classifications
allow for other options
Common Machine Learning Types
(Clustering)
A type of unsupervised learning
Find groups of related things among data
What traits do my best customers
have in common?
Core Machine Learning Concepts
1 - Feature:
is an input variable
2- Label:
is the thing we’re predicting
Features and Labels in a Dataset
Given a pile of data, you (data scientist) need to determine which bits are relevant
to make decisions on
● Experiment
● Domain knowledge
● Keep in mind the principles of AI
Evaluate the Results - Regression
Use the validation dataset to test the model, and measure how close or far the actual results are from the predicted results
“Mean Square Error”
“Large differences” are much “worse” than small differences.
Evaluate the Results - Classification
The result is to give a prediction score that the subject is part of the group
“70% confident this is an apple, 30% confident this is a pear”
So if an apple is mis-identified as a pear, that’s ok as long as it only happens 30%
of the time…
False Positives vs False Negatives
Compare true positives with false positives and true negatives with false negatives when evaluating the model.
How important is it to you that it never has a false positive?
Accuracy vs precision
Azure Machine Learning
“Azure Machine Learning is a cloud-based service
that helps simplify some of the tasks
and reduce the time it takes
to prepare data, train a model,
and deploy a predictive service.”
