IDSC Test F

CAPTCHAs

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

Online Analytical Processing

Takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube.

Python

A general purpose programming language that is also popular for data analytics.

What was the business analytics controversy?

Opponents point to it 'predictive policing' capabilities and raise concerns that personal data is collected in anticipation of crimes.

What are the concerns about AI being implemented so quickly?

- Strategy
- Cybersecurity
- Governance
- Ethics

What are the AI layers based on the picture on the slides?

- Artificial intelligence
- Machine learning
- Neural networks
- Deep learning
- Generative AI
- Agentic AI (subcategory of Generative AI, but not considered a separate layer)
- Large language models (training mechanism for AI, not considered as part of the layers)

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, like defective versus error-free, or stock price.

Self-Supervised Learning is also called what?

Unsupervised learning

Self-Supervised Learning

Systems build pattern-recognizing algorithms using data that has not been pre-classified.

Expert Systems

Leverages rules or examples to perform a task in a way that mimics applied human expertise. Example MYCIN is a medical diagnostics system.

Trained based on a lot of clinical insights and data. It came from other experts to find other components they are looking for.

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 to be aware of:

- 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

Classifying

An example of classifying is to examine a customer as credit worthy or credit unworthy.

Affinity Grouping

Affinity grouping is a special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis.

What is the process of analyzing data?

1. Define the question
2. Collect the data
3. Clean the data
4. Analyze the data
5. Visualize and share your findings

What is the key to business intelligence?

The most important step in successful Business Intelligence is not about technology, it is about understanding the business.

What are the challenges of business analytics?

- Risk of spending lots of money and time chasing poorly defined problems or opportunities.
- Trying to implement in fast-moving markets. Fixing a plane while it is flying.
- Mistaking noise for true insight.
- Trying to make BA perfect the first try.
- Not accessing the correct data.
- Time and effort to clean the data.
- Potential for data privacy issues

What was the case that was discussed in the slides regarding business analytics?

Banking
- The banking industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data.
- As banks face increasing pressure to stay profitable, understanding customer needs and preferences becomes a critical success factor.
- New models of proactive risk management are being increasingly adopted by major banks and financial institutions.

Predicting Customer Churn

- Valpak, one of North America's leading direct marketing companies, recognized an opportunity to boost customer retention using insights from historical customer behavior data.
- However, the Valpak team was unsure if their data could predict which customers are likely to leave and why, and they lacked the data science expertise necessary to extract those insights.

Constitutional AI

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

Genetic Algorithms

Model building techniques where computers examining many potential solutions to a problem. Finds the optimal solution (shortest distance to a solution).

Artificial Intelligence (AI)

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.

How are LLM's created?

They are created by breaking apart text and creating multi-tiered relationships between the individual elements of text

Trained LLMs understand what?

- Break part language
- Weight relationships it identifies
- Come up with complex, multi-faceted, nuanced response that can consider words, context, style and more

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 that may further enhance result accuracy.

Reinforcement Learning from Human Feedback

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

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

Refers to software that's capable of learning and reasoning on any task or subject, including developing reasoning about topics not presented through a training corpus.

What are some AI driving changes

- Tasks and occupations (example: the use of machine vision systems to identify potential cancer cells)
- Business processes (example: Amazon fulfillment centers after the introduction of robots and optimization algorithms based on machine learning)
- Business models

What were the case study discussed in the slides for Topic 11?

- Autonomous Trucks
- AI and Trading

What do autonomous trucks do?

It takes advantage of the most sophisticated technologies available to deliver a next-generation haulage solution. This boosts safety, productivity, and availability on busy mine sites, especially those in difficult or remote locations.

What do intelligent trading systems do?

- Monitor both structured (databases, spreadsheets, etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it.
- It is also called algorithmic, quantitative or high-frequency trading.

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 analytics often includes data mining, predictive analytics, applied analytics and statistics.
FOCUSES ON THE FUTURE

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 to decision-makers.
FOCUSES ON THE NOW (CURRENT)

What is the business intelligence criteria? (

- Accurate
- Timely
- Actionable
- Valuable insights

When is BI only effective?

If it is trusted and used to guide human decisions.

What are the three types of business analytics?

1. Descriptive (explain)
2. Predictive (predict)
3. Prescriptive (optimize)

Clustering

Recognizing distinct groupings or sub-categories within the data.

Estimating and Predicting

Estimating and predicting are two similar activities that normally yield a numerical measure as the result. From the set of existing customers we may estimate the overall indebtedness of the candidate customer.

Canned Reports

Provide regular summaries of information in a predetermined format.

Ad hoc reporting tools

Puts users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters

Dashboards

Heads-up display of critical indicators that allows managers to get a graphical glance at key performance metrics.

Data cube

Special database used to store data in OLAP reporting.

Query Tools

A tool to interrogate a data source or multiple sources and return a subset of data, possibly summarized, based on a set of criteria.

R

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

Graphical Query Tools

Allow a user to create a query through a point-and-click or drag-and-drop interface, rather than requiring programming knowledge.

What is the importance of data driven decisions?

- Technology companies were built for analytics. Compared to legacy / non tech companies.
- Legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition.

If data visualization is well designed, then what are the outcomes?

- Puts Data in Context
- Provides Prospective
- Saves Time
- Reveals Trends
- Tells a Story

What are the best practices of business analytics?

- Know the objective for using Business Analytics. Define your business use case and the goal ahead of time.
- Define your criteria for success and failure.
- Select your methodology and be sure you know the data and relevant internal and external factors
- Validate models using your predefined success and failure criteria

Data Mining

The process of using computers to identify hidden patterns in, and to build models from, large datasets.

What are the key areas of leverage for data mining?

- Customer segmentation
- Marketing and promotion targeting
- Market basket analysis
- Collaborative filtering
- Customer churn
- Fraud detection
- Financial modeling
- Hiring and promotion

What are the prerequisites for data mining to work?

- Organization must have clean, consistent data
- Events in that data should reflect trends.

Over-engineer

Build a model with so many variables that the solution arrived at might only work on the subset of data used to create it.

What are the three critical skills that are important when recruiting data mining and business analytics?

1. Information technology
2. Statistics
3. Business knowledge

What was the data mining example discussed in the slides?

A Midwest grocery chain used data mining software to analyze local buying patterns.
- They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer.
- Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, they only bought a few items.
- The retailer concluded that they purchased the beer to have it available for the upcoming weekend.

What are some examples of data mining applications?

- Fraud Detection
- Stock Market Price Prediction
- Customer Purchasing Behavior
- Healthcare
- Education
- IT Security - Intrusion Detection
- Customer Segmentation
- Corporate Surveillance
- Criminology
- Bioinformatics