ITI CCO EXAM Study Guide

Dark Patterns

 tricks used in websites and apps that make you do things that you didn't mean to. 

Confirmshaming

emotionally manipulating a user into doing something that they would not otherwise have done.

Design Thinkng Process

• Empathize

  • Understanding a problem situation use of technology from another persons perspective 

  • Important to interview many people and with different attributes  

• Define 

  • Analyzing interviews and other data leads to understanding of problems from the experience of others 

  • Should result in a useful problem statement for the next stage 

• Ideate → prototype → test (and repeat) 

  • Developing a prototype takes many iterations 

  • Prototypes are not produces that are ideas made into artifacts

UI

screens, buttons, toggles, icons, and other visuals you interact with while using the website or app.

UX

entire interaction you have with a product including how you feel about the interaction.

Intelligence

the ability to acquire and apply knowledge and skills

AI (Artificial Intelligence)

the theory and development of computer systems able to perform tasks that normally require human intelligence

Machine Learning

an area of AI in which a computer generates rules and predictions based on raw data that has been fed into it. Also, a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

Supervised ML

labeling examples in a specific domain to teach AI to recognize patterns.

Training Stage

take 80% of the labeled data and instruct the ai to look for patterns in good shoes training data and bad shows training data.

Testing Stage

remove the labels to test the AI

Futurism

the study of social and technological advancement for the purpose of exploring how people will live and work in the future

Why is predicting the future so difficult?

• Population growth is not linear 

• GDP growth is not linear 

• Tech advancements are not linear 

Moore’s Law

• Exponential increases including computing power per dollar/microchip 

  • The number of translators in an integrated circuit (IC) doubles about every two years

Narrow, Weak AI

• Trained to perform a specific task 

• Can only perform one thing or a specific set of things 

• Narrow scope of expertise

Generalized AI, Strong AI

• Ability to learn and adapt to new situations 

• Can perform a wide range of tasks at or near a human like level 

• Can understand and adapt to a board complex unanticipated experience

Instrumental Convergence

• An intelligent agent with unbounded but harmless goals can act in surprising and harmful ways 

• Unintended consequences 

• Example: AI designed to make humans smile might learn how to tell jokes 

DNS

Domain Name System

Domain Name

any text or string that you can enter into your webpage (example: google.com)

IP Address

• Four set numbers (172.25.43.123). If you type in the IP address of a particular website you will be routed to that specific webpage.

• The IP address is the internet protocol that has a set of rules that helps millions of devices communicate with each other.

DNS Resolver

Acts as a phonebook in the entire thing and bridges the gap between human communication and the DNS and networking world

Intelligent Behaviors

Machines can recognize a visual scene, understand a text written in a natural language, or perform an action in the physical world.

Functions of Machine Learning System

• Descriptive: the system uses the data to explain what happened 

• Predictive: the system uses the data to predict what will happen 

• Prescriptive: the system will use the data to make suggestions about what actions to take 

Supervised Machines

trained with labeled data sets which allow the models to learn and grow more accurately over time (the most common type used today). Example: trained with pictures of dogs and other things all labeled by humans. The machine would learn ways to identify pictures of dogs on its own.

Unsupervised Machine

a program looks for patterns in unlabeled data. Finds patterns or trends that people aren't explicitly looking for. Example: could look through online sales data and identify different types of clients making purchases

Reinforcement Machines

trains machines through trial and error to take the best action by establishing a reward system. Trains models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions.

Natural Language Processing

machine learns to understand natural language as spoken and written by humans instead of the data and numbers normally used in programming (siri).

Neural Networks

modeled on the human brain- millions of processing nodes are interconnected and organized into layers. Cells are connected, each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the cells with each cell performing a different function. Example: in a network trained to identify a picture the different nodes would assess the information and arrive at an output that indicates the picture's features.

Deep Learning

neural networks with many layers. Modeled on the way the human brain works and powers many machine learning uses- the more layers you have the more potential for complex things to go well  

Layered Network

process extensive amounts of data and determine the weight of each link in the network.

Recommendation Algorithms

• suggest what information appears on your feed and product recommendations are fueled by machine learning

Image Analysis and Object Detection

machine learning can analyze images for different information (identifying people and telling them apart) 

Fraud Detection

• machines can analyze patterns

• How someone normally spends or where they normally shop 

• Identifies potential fraudulent credit card transactions, log ins, or spam emails. 

Automatic Helplines or Chatbots

customers or clients don't speak to humans but instead interact with a machine.

Use machine learning and natural language processing with the bots learning from records of past conversations to come up with an appropriate response

Medical Imaging and Diagnostics

machine learning can be trained to examine medical images or other information and look for certain markers of illness like a tool that can predict cancer risk

Explainability

the ability to be clear about what the machine learning models are doing and how they make decisions

Bias and Unintended Outcomes

• Machines are trained by humans and human biases can be incorporated into algorithms

• The program will learn to replicate the biased data and perpetuate forms of discrimination 

• Chat bots on twitter can pick up on offensive and racist language

Teachable Machine

A web based tool that makes creating machine learning models fast easy and accessible to everyone

How to Use Teachable Machine

• Gather: group your example sinto classes that you want the computer to learn 

• Train: train your model to instantly test it out to see whether it can c correctly classify new examples 

• Export: sites apps and more- you can download your model and host it online

What to use to teach the machine

• Use files or capture examples life 

• Use it entirely on device without any webcam or microphone data leaving your computer 

• Images: teach a model to classify images using files or your webcam 

• Sounds: teach a mode to classify audio bu recording short sound samples 

• Poses: teach a model to classify body positions using files or striking poses in your webcam

2040/2050 (TED Talk)

the year there is a 50% probability that we will have achieved human level machine intelligence.

Intelligence as an Optimization Process (TED Talk)

steers the future into a particular set of configurations 

AI Goal: Make Humans Smile (TED Talk)

• Weak AI: performs useful or amusing actions that cause the user to smile 

• Superintelligent AI: realizes there is more effective way to achieve this goal 

  • Take control of the world and stick electrodes into facial muscles of humans to cause constant beaming grins

SOPA

Stop Online Piracy Act

PIPA

Protect IP Act

TED Talk Video

Only few points from the TED Talk video are added. Watch the video for better understanding.