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Subfields of AI
machine learning and deep learning
Machine learning
uses data and algorithms to emulate the way humans learn, relies on human interaction to gain knowledge
Deep learning
uses data and algorithms to create learning, some portions of learning are automated
Examples of deep learning
speech recognition, customer service, digital image analysis, automated stock trading
4 positive outcomes of AI
increased productivity - higher profitability
decreased human risk & injury - automates dangerous tasks like mining, etc.
diminished human errors - computers have lower chance of making mistakes
ability to better target product and service offerings - data science
2 drawbacks of AI
job losses due to automation
privacy concerns
data repurposing - use of data outside original scope
data spillover - use of data from individuals who were not the original targets of data collection
data security
modernizing
prepares data for an AI and hybrid eCloud world
AI ladder approach to information architecture (IA)
infuse, analyze, organize, collect
Infuse - AI ladder step
operationalize AI throughout the business to be used across multiple oeprating areas
Analyze - AI ladder step
build and scale AI with trust and transparency, can later be scaled to maximize benefits and insights
Organize - AI ladder step
create business-ready analytics foundation by considering of data has been cleansed, if it’s complete, etc.
Collect - AI ladder step
make data simple and accessible
Cloud computing
computing model where processing, storage, software applications, and other services are provided over a network
Deloitte study on cloud computing
cloud-based software used to deliver AI to 70% of companies that use AI and 65% of companies use cloud services to create AI
Examples of cloud-based AI applications
Internet of things, ai as a service (AIaas) which is a type of SaaS, Chatbots
AIaaS
type of SaaS, allow for experimentation with AI with lower risk than full implementation
Chatbots
computer programs that process and simulate human conversations and allow organizations to give customers an experience similar to communicating with a real person
Advantages of cloud-based AI
enhanced security, cost savings, increased efficiency, improved data management
Disadvantages of cloud-based AI
data privacy, internet connectivity concerns, lack of platform control
Purpose of cognitive computing
seeks to better understand unstructured data
4 Steps of cognitive computing
observation - observing behaviors, occurrences, reading large datasets, etc.
interpretation - drawing conclusions and generating hypotheses about meaning
evaluation - determine which hypothesis makes the most sense
decision - decide course of action or which decision to take
Natural Language Processing (NLP)
used by cognitive systems, study and application of programming techniques that allow computers to understand spoken words and text that are inputted by humans
Ransomware-as-a-service (RaaS)
makes detection and prevention increasingly service because machine learning can detect anomalies with ransomware
Types of machine learning
supervised and unsupervised learning
Supervised learning
uses data class labels within data sets that specify what the data represents, used to create classification model
Classification model
designed when machine learning allocates a label value to a specific class then seeks to recognize these values to decide categories they fit into
Unsupervised learning
machine learning algorithms are used to examine and cluster unlabeled data sets
Clustering
approach used in data mining that groups unlabeled data based on parallels and variances within the data sets, uncovers hidden associations
Drawbacks of unsupervised learning
increased complexity due to large volumes of data, need for human intervention to validate results A
Application of unsupervised learning
medical imaging- assists radiologists with accurate/faster diagnoses
Reinforcement learning
focuses on using machine learning to analyze a scenario that in turn creates learning about how a behavior can be optimized to achieve maximum outcome/reward
Neural network
method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain
Layers of neural network
input, hidden, output
Input layer - neural network
execute initial handling of data (initial processing, analysis, categorization)
Hidden layer - neural network
can be multiple hidden layers, processing becomes increasingly complex as data moves to the next layer, output from each layer is analyzed & refined
Output layer - neural network
analyzes information from previous layers to make final prediction or conclusion
Application of neural networks
Rendering invariant state-prediction (RISP)
seeks to overcome cost and difficulty from motion capture technology by eliminating necessity of sensors
Generative adversarial networks (GANs)
type of machine learning model that uses 2 neural networks to compete against each other to create artificial instances of data that are interpreted as real data
Types of neural networks in GAN
generative network & discriminative network
Generative network - GAN neural network
used to create new synthetic instances of data that are erroneously identified as real data
Discriminative network- GAN neural network
tests and identifies which data it receives has been synthetically created, enables GAN to create real looking images by using the discriminative network to identify need for reworking until perceived as real image
Uses of GANs
adaptation of black and white images to color, generation of realistic images from text, etc.
Deep Learning Process used to build, train, deploy systems
collect data, choose and optimize algorithm, setup and manage training environment (neural networks, etc.), train/retrain/tune models, deploy models into production, scale and manage production environment
Common types of inference for deep learning models
batch inference: scheduled on recurring basis
real-time inference: gathered on request
Federal Trade Commission (FTC) policies for use of AI
be transparent with consumers, explain how algorithms make decisions, ensure good decisions, hold themselves accountable for ethics/fairness/non-discrimination
10 principles to consider when articulating methods for the development/use of AI
create public trust
public participation in rule-making processes
scientific integrity & information quality
risk assessment
benefits and costs: analyze which tools produce net benefits
flexibility
fairness & nondiscrimination
disclosure & transparency
safety & security
interagency coordination: collaboration with governmental agencies on best uses
Ethical concerns surrounding AI
privacy and surveillance, bias and discrimination, role of human judgement
Differential Privacy (DP)
mathematical framework that can be used to analyze how much an algorithm recalls data and information about individuals
Surveillance
used to monitor customer patterns and behaviors to identify shoplifting or shooting suspects, can also cause data privacy problems
AI bias
variance that exists in the output provided by AI algorithms
Factors causing AI bias
cognitive bias or incomplete data
Cognitive bias
conscious or unconscious errors in cognition that impact individual’s judgments, assumptions, and decision making
3 methods to gain human trust in AI
transparency to dispel misconceptions, instilling human values in AI, collaboration through establishing a common set of standards and platforms
5 Guiding principles for ethical use of AI
socially beneficial, avoid forming unfair bias, follow laws, incorporate transparency and accountability, implement data security & privacy
AI in business
sales & marketing, accounting, IT operations, manufacturing
AI in healthcare
robotic surgery, health tracking, pharmaceuticals (increase efficiency in processing)
AI in government
automation of routine tasks, military (precise targeting), policing aka law enforcement, predictive policing
4 careers associated with AI
user experience: analyzes customer data to optimize products and services
AI engineer: build AI models
machine learning engineer: building of machine learning models and training models
data scientist: develop data models and algorithms to automate processes
Tips to learn AI
learn about the math used, study computer programming languages, take free courses
AI chatbot
computer program that incorporates artifical intelligence and natural language processing to interpret user-provided questions and provide automated responses
examples of AI chatbots
ChatGPT, Caktus AI, AI-Powered Bing
Artificial Intelligence language models
computer software programs that use artifical intelligence to process and generate human languages
NLU
natural language understanding, used to understand the user’s needs better in AI
Chat GPT uses
idea generation & research, language translation, sentiment analysis (tone detection), analysis of computer code, marketing
Caktus AI
first AI designed specifically for education, pay to use service
Uses of Caktus AI
essay writer, paragraph generator, content improver, python writer
Features of AI powered bing
targeted search results, completed responses, display options, create and compose emails/brand image/etc., image creator
AI plagiarism detector examples
AI text classifier by open AI, GPTZero, Turnitin
Ethical problems with chatbots
transparency, chatbot identity reinforcing gender stereotypes, proper communication in regards to sensitive topics, accurate data representation
Ethical problems with chatbots for students
misinformation (wrong answers), plagiarism due to chatbot’s lack of citations and students thus not citing their sources
Areas of research for chatbots
personalization, interactions in multiple languages, technology integration (virtual reality), emotional intelligence, AI-assisted shopping and task completion, artificial general intelligence (being able to complete any tasks that the human brain can do)