AI Glossary 2

Question 1

Artificial Intelligence

Answer 1

The ability of machines to exhibit human-like capabilities
such as reasoning, learning, planning, and creativity.

Question 2

Models

Answer 2

Mathematical representations inside an AI system that determine how it makes predictions or decisions based on data.

Question 3

Neural Networks

Answer 3

AI systems modeled on the neuron connections in the human
brain, comprised of layers of interconnected nodes.

Question 4

Node

Answer 4

An interconnected point inside a neural network that transmits signals to other nodes.

Question 5

Weight

Answer 5

Adjustable parameters in a neural network that determine the strength of connections between nodes.

Question 6

Layers

Answer 6

Levels within a neural network where nodes are organized, with data flowing from input layers to hidden layers to output layers.

Question 7

Inputs

Answer 7

Data fed into an AI system that is used for making predictions or
decisions.

Question 8

Deep Learning

Answer 8

A type of machine learning that uses neural networks, especially
those with many hidden layers, to identify complex patterns and relationships in
data.

Question 9

Natural Language Understanding (NLU)

Answer 9

The ability of a computer to
comprehend human speech or text.

Question 10

Natural Language Processing (NLP)

Answer 10

The ability of a computer to analyze and
process human language.

Question 11

Machine Learning

Answer 11

The use of statistical models and algorithms that learn from
data to make predictions without explicit programming.

Question 12

AI Benefits for Support Agents, Managers, and Customers

Answer 12

Increases efficiency,
provides insights, and reduces service representative strain.

Question 13

Predictive AI

Answer 13

Analyzes historical data to predict potential future outcomes.

Question 14

Generative AI

Answer 14

Creates new content like text, images or music based on
patterns in training data.

Question 15

Main AI capabilities

Answer 15

-Numeric predictions
-Classifications
-Robotic Navigation
-Language Processing

Question 16

Numeric predictions

Answer 16

Predicting numerical outcomes like sales forecasts or
manufacturing defects.

Question 17

Classifications

Answer 17

Categorizing data based on similarities, like detecting
spam emails or identifying objects in images.

Question 18

Robotic Navigation

Answer 18

Adapting movement and actions based on environmental feedback, like self-driving cars.

Question 19

Language Processing

Answer 19

Understanding and generating human language,
like chatbots or text summarization.

Question 20

Concerns of AI

Answer 20

-Hallucinations
-Data security
-Plagiarism
-Sustainability
-Unknown attribution

Question 21

Hallucinations

Answer 21

When AI predictions diverge significantly from grounded
truth.

Question 22

Data security

Answer 22

Protecting training data and maintaining robust models
resistant to attacks and potentially exposing data end-users provide while
using AI models.

Question 23

Plagiarism

Answer 23

Potential for AI systems to replicate copyrighted material from training data.

Question 24

Sustainability

Answer 24

Environmental impact of computationally intensive AI model training.

Question 25

Unknown attribution

Answer 25

Output that lacks clarity on its original source due to
an unclear path to what influenced the created output.

Question 26

Einstein Discovery

Answer 26

The Salesforce AI capability that generates predictive models and insights from data without needing a data scientist. It helps discover relevant patterns in all your data.

Question 27

Einstein Prediction

Answer 27

Predict business outcomes, such as churn or lifetime value.
Create custom AI models on any Salesforce field or object with clicks, not code.

Question 28

Einstein Next Best Action

Answer 28

A Salesforce tool that leverages predictive intelligence to provide personalized, timely recommendations within Salesforce to
guide optimal next actions. It combines predictive models, tangible recommendations, and automation to turn insights into action.

Question 29

Einstein Language

Answer 29

A Natural Language Processing capability within the
Salesforce Platform that allows extracting insights from unstructured text data
across Salesforce objects. It can understand context and intent within text in various languages.

Question 30

Einstein Vision

Answer 30

A set of Einstein Platform services that automate image
classification for field service use cases. It can classify images uploaded in Salesforce based on customized categories. Useful for field agents capturing images and videos onsite for automatic logging and routing.

Question 31

Einstein Computer Vision

Answer 31

The subset of Einstein AI capabilities that employs
deep learning algorithms to identify, categorize, caption, and understand visual elements like objects, scenes, and activities in images and videos. Enables automating visual perception tasks.

Question 32

Call Summaries

Answer 32

A Service Cloud Einstein feature that automatically creates
summarized notes from call transcripts between service agents and customers.
Uses speech-to-text, natural language processing, and summarization to capture
key discussion points, requests, and action items.

Question 33

Einstein Lead Scoring

Answer 33

A Sales Cloud Einstein capability that automatically scores leads based on predictive analysis of historical lead and opportunity data to determine propensity to convert. Provides visibility into factors influencing the score to focus sales efforts on hot leads.

Question 34

Einstein Opportunity Scoring Forecasting

Answer 34

Sales Cloud Einstein feature that
automatically scores opportunities to indicate the likelihood of closing a deal
based on predictive analysis of historical opportunity data and machine learning algorithms. Provides focus on highest potential deals.

Question 35

Einstein Opportunity Forecasting

Answer 35

A Sales Cloud Einstein capability that predicts the most likely sales revenue outcome based on historical opportunity data and current pipeline trends. Creates data-driven forecasts to complement manager predictions.

Question 36

Einstein Case Classification

Answer 36

A Service Cloud Einstein feature that combines natural language processing, predictive algorithms, and pre-defined routing rules
to automatically categorize cases for faster resolution. Reduces manual case classification effort.

Question 37

Chatbots

Answer 37

Automated applications that simulate human-like conversational interactions to respond to routine customer queries and requests, often using
capabilities like natural language processing. Salesforce offers Einstein Bots, AI-powered chatbots natively integrated with CRM data.

Question 38

Salesforce’s Trusted AI Principles

Answer 38

-Responsible - Prioritize human rights, protect data, maintain scientific rigor, prevent misuse;
-Accountable - Uphold responsibility towards customers, partners, society; seek
continual feedback and improvement;
-Transparent - Ensure explainability and
clarity behind AI recommendations and processes; -Empowering - Augment
human intelligence, don’t fully replace it;
-Inclusive - Ensure AI benefits people inclusively, reflecting diverse values.

Question 39

5 Guidelines for Responsible AI Development

Answer 39

-Accuracy - Deliver verifiable,
transparent results with explanations;
-Safety - Reduce bias, toxicity, and misuse; protect privacy and security;
-Honesty - Respect data provenance; clarify when
content is AI-generated;
-Empowerment - Enhance human abilities; don’t fully automate where human judgment is needed;
-Sustainability - Balance innovation pace and energy usage.

Question 40

Ethical AI Practice Maturity Model

Answer 40

-Ad Hoc - Informal advocacy for ethical
practices;
-Organized & Repeatable - Formal programs and training established;
-Managed & Sustainable - Ethical checkpoints integrated across product lifecycle;
-Optimized & Innovative - Mature ethical practices fully integrated with privacy, legal, design.

Question 41

Biases- What Are They

Answer 41

Systematic errors or unfair assumptions in data or
models that lead to discriminatory or prejudicial outcomes.

Question 42

Biases - Why You Don’t Want Them

Answer 42

Biased models output skewed, inaccurate predictions and unfair decisions; Biased AI systems exclude or disadvantage
certain groups; Biased algorithms undermine trust in AI.

Question 43

Biases - How to Avoid

Answer 43

Have clean, high-quality data without embedded societal
biases; Validate data by checking for sampling bias and underrepresentation;
Don’t use parameters that could introduce unintentional bias like gender,
ethnicity, ZIP code (demographic data); Continuously monitor models for
developing biases and adjust accordingly.

Question 44

Data Quality

Answer 44

Having accurate, complete, consistent and relevant data. This
comprises dimensions like:
Age: When data was last updated.
○ Completeness: Whether all expected data is present.
○ Accuracy: Extent to which data is correct.
○ Consistency: Standardized formatting of data.
○ Duplication: Presence of duplicate records.
○ Usage: How effectively data is being leveraged.

Question 45

How to Make Data Good and How to Clean Data

Answer 45

-Remove Duplicates: Delete any duplicated data records.
-Fix Incorrect Data: Fix Incorrect Data: Correct any erroneous data values
like typos.
-Fill in missing data: Populate blank data fields where possible.

Question 46

Salesforce Tools to help with data cleaning

Answer 46

-Validation Rules: Ensure data adheres to specified formats and
values.
-Duplication Rules: Prevent duplicate record creation.
-Required fields: Make key fields mandatory for more complete data.
-Third-party app exchange apps can also help

Question 47

Data Management Strategies

Answer 47

-Naming: Establish conventions for object, field, and record naming.
-Formatting: Define consistent data formats.
-Workflow: Set processes for data lifecycle and maintenance.
-Quality: Institute standards around accuracy and completeness.
-Privacy/Security and Permissions: Determine appropriate data access
levels to prevent data breaches and only the correct users access to data.
-Monitoring: Perform periodic data reviews and audits.

Question 48

Training Data

Answer 48

The historical, labeled datasets used to train machine learning
models to find patterns and make predictions. Quality training data is essential for developing effective AI.