IDSC Topics 11 & 12

Artificial Intelligence

Computer software that can mimic or improve upon functions that would otherwise require human intelligence.

Machine Learning

Software that contains the ability to learn or improve without being explicitly programmed.

Neural Networks

Examines data and hunts down and exposes patterns, to build models to exploit findings.

Deep Learning

A type of machine learning that uses multiple layers of interconnections among data to identify patterns and improve predicted results.

Generative AI

A type of machine learning (ML) model that leverages neural networks, particularly deep learning, to create new content based on patterns learned from existing data.

Agentic AI

AI agents that are semi or fully autonomous that are able to perceive, reason and act on their own to solve problems.

Large Language Model (LLM)

A computer program that has been fed enough examples to be able to recognize and interpret human language or other types of complex data.

Parameters

Values that are used to determine text elements and relationships and that are further refined during training.

Corpus

In AI, this refers to the data used to train a model before it can be used.

Supervised Learning

A type of machine learning where algorithms are trained by providing explicit examples of results sought.

Self-Supervised Learning

Sometimes called unsupervised learning, where systems build pattern-recognizing algorithms using data that has not been pre-classified.

Semi-Supervised Learning

A type of machine learning where the data used to build models contains data with explicit classifications but is also free to develop its own additional classifications.

Reinforcement Learning from Human Feedback

A machine learning training technique that uses a reward model and human evaluators that will provide feedback to continually tune results.

Constitutional AI

Alignment and safety in an AI by incorporating specific rules or guidelines.

Expert Systems

Leverages rules or examples to perform a task in a way that mimics applied human expertise.

Genetic Algorithms

Model building techniques where computers examine many potential solutions to a problem.

Prompt

A request made to a generative AI system, usually in the form of written or spoken text.

Prompt Engineering

The practice of designing inputs for generative AI tools that will produce optimal outputs.

Hallucination

An incorrect answer provided by generative AI that is otherwise presented as correct.

Artificial General Intelligence (AGI)

Refers to software that's capable of learning and reasoning on any task or subject.

CAPTCHAs

An acronym standing for completely automated public Turing test to tell computers and humans apart.

Turing Test

Conceived by Alan Turing, a Turing test of software's ability to exhibit behavior equivalent to, or indistinguishable from, a human being.

AI Risks

Bias, Cybersecurity threats, Data privacy issues, Environmental harms, Existential risks, Intellectual property infringement, Job loss, Lack of accountability, Lack of explainability and transparency, Misinformation and manipulation.

Task-and-Occupation Redesign

An example is the use of machine vision systems to identify potential cancer cells — freeing up radiologists to focus on truly critical cases.

Process Redesign

An example is the reinvention of the workflow and layout of Amazon fulfillment centers after the introduction of robots and optimization algorithms.

Business Models

Need to be rethought to take advantage of AI systems that can intelligently recommend music or movies in a personalized way.

Business Analytics Platforms

Include software tools and applications used to build models and simulations to create scenarios, understand current events and predict future states.

Business Intelligence

Includes a variety of tools, platforms, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries, create reports, dashboards and data visualizations for decision-makers.

Business Intelligence (BI) 4 Major Criteria

Accurate, Timely, Actionable, Valuable Insights.

3 Types of Business Analytics

Descriptive (explain), Predictive (predict), Prescriptive (optimize).

Descriptive Analytics

Reports, dashboards, visualizations.

Predictive Analytics

Forecasting, data mining, recommender engine, pattern recognition.

Prescriptive Analytics

Simulations, econometrics, price optimization, autonomous decision making.

Clustering

Recognizing distinct groupings or sub-categories within the data.

Classifying/Categorization

Examining a customer as credit worthy or credit unworthy.

Estimating and Predicting

Activities that yield a numerical measure as the result.

Affinity Grouping

Identifies events or transactions that occur simultaneously, such as market basket analysis.

Canned Reports

Provide regular summaries of information in a predetermined format.

Ad Hoc Reporting Tools

Allow users to create custom reports on an as-needed basis.

Dashboards

Heads-up display of critical indicators for key performance metrics.

Online Analytical Processing (OLAP)

Takes data from standard relational databases, calculates and summarizes it, and stores it in a data cube.

Data Cube

Special database used to store data in OLAP reporting.

Query Tools

Tools to interrogate a data source and return a subset of data based on criteria.

Python

A general purpose programming language popular for data analytics.

R

A programming language specifically created for analytics, statistical, and graphical computing.

Graphical Query Tools

Allow users to create a query through a point-and-click or drag-and-drop interface.

Data Analysis Process

Define the question, Collect the data, Clean the data, Analyze the data, Visualize and share findings.

Keys To Business Intelligence

Understanding the business is the most important step in successful Business Intelligence.

Importance of Data Driven Decisions

Organizations harnessing data analytic capabilities can create significant value and differentiate themselves.

Importance of Data Visualization

Well designed data visualization puts data in context, provides perspective, saves time, reveals trends, and tells a story.

Business Analytics Best Practices

Know the objective, define your business use case, and validate models using predefined criteria.

Challenges in Business Analytics

Risk of spending time and money on poorly defined problems, implementing in fast-moving markets, and data privacy issues.

Data Mining

The process of using computers to identify hidden patterns in large datasets.

Key Areas of Leverage in Data Mining

Customer segmentation, marketing targeting, market basket analysis, collaborative filtering, customer churn, fraud detection, financial modeling, hiring and promotion.

Over-engineer

Building a model with so many variables that it only works on the subset of data used to create it.

3 Critical Skills for Data Mining & Business Analytics Team

Information Technology, Statistics, Business Knowledge.