IDSC: Quiz F (Topics 11+12)

wide variety of tools, applications, and methodologies to run queries, create report, dashboards, and data visualizations for decision makers; to understand the business and determine relationships among internal and external factors "BI answers what happened", studies historical data

software tools and applications used to build models to create scenarios, understand current events, and predict future state "BA answers why it happened and whether it will happen again", looking forward to understand why

improve operational efficiency, better understand customers, project future outcomes, gain insights, measure performance, discover hidden trends, etc

data access, discovery, exploration, and information sharing to react to change questions and expectations

traditional BI was a top-down approach that led to slow, frustrating reporting cycles, modern allows multiple levels of users to customize dashboards and create reports

process of discovering meaningful correlations, patterns, and trends by sifting through large amounts of data; not obvious relationships, discover patterns, predictive [OLAP]

can help you find patterns and relationships (cannot eliminate the need to understand your data and know your business), can discover hidden information and data (cannot tell you the value of that information)

proper data cleansing and preparation can be facilitated by a data warehouse, applicability to problem

what happened? hindsight

what will happen? insight

how can we make it happen? foresight

why is it happening?

Accuracy (of inputs and outputs), valuable insights, timeliness (of data going in and insights coming out), actionable

clustering (recognizing direct groupings or subcategories within the data), classifying, estimating and predicting, and affinity grouping (special kind of clustering that identifies events that occur simultaneously)

know the objective, define criteria for success and failure, select methodology, and validate models using predefined criteria

risk of spending money and time chasing poorly defined problems, mistake noise for true insight, not accessing correct data, time and effort to clean data, etc

problem definition, data gathering & preparation, model building & evaluation, knowledge deployment (insight, scoring, extraction of model details, integration of tools)

the ability of a machine to perform cognitive functions we associate with human minds, such as reasoning, learning, and problem solving

thinking humanly, thinking rationally (thought processes and reasoning), acting humanly, acting rationally (behavior)

Reactive machines (Deep Blue (chess)), limited memory (reinforcement, RNN, E-GAN, transformers), theory of mind (how others feel), self-awareness (understand existence)

"strong AI" still much theoretical, e.g. GPT-3 is closest

limited context and performs single task extremely well; machine learning and deep learning (self-driving cars)

algorithms that detect patterns and learn how to make predictions, recommendations, and decisions

type of machine learning, a biologically inspired neural network architecture; data comes pre-labeled

surpasses humans in every way, hypothetical situation

First look in 1940s, (1950) Alan Turing, 2010s had big gains, 2020s is GPT-3

Improves productivity and efficiency, while reducing potential for human error

Development costs and possibility for automation to replace human jobs

uses predictive analysis, knowledge creation, customer insight, helps with pricing, supply chain, R&D, credit card fraud, healthcare, and Netflix recommendations

(1) adverse impact of AI on labor (2) biases (3) lethal autonomous weapon systems (4) superintelligence

AI can detect gunfire, predict crime spots and even who will commit a crime; however, criminals also take advantange of AI

the European Commission published seven principles to create "trustworthy" AI programs as the baseline for companies; formulates best practices and advance public understanding of AI

gender bias, racial bias, age discrimination, and other human characteristics; prejudice based on a particular categorical distinction

Learn based on training data, which may have skewed human decisions or represent historical or social inequities

Facial recognition software considered the benchmark for testing facial recognition, however there is imbalance since the data is has was 70% male and 80% white