Decoding Generative AI: A Comprehensive Glossary for Business Executives

Jenna Trott  |  SEPTEMBER 21, 2023  |  5 Minute Read

AI is taking the world by storm in a big way, with Salesforce seemingly leading the revolution. At their recent Dreamforce event, Salesforce shed light on the rapid evolution of generative AI in the business world and emphasized the necessity for businesses to begin exploring and experimenting with it (if they haven’t already!). This imperative is backed by recent research which shows that 60% of organizations across industries are already using generative AI with 40% of those surveyed reporting an increase in investment as part of their strategy. But the world of AI is understandably complex, and if hearing terms like “grounding”, “machine learning”, or “deep learning” sends your head spinning, hold tight. We’re including a glossary of relevant terms to provide your team, regardless of their technical background, to demystify the puzzling nature of AI, and perhaps give your team a few more tools to add to their belt.

AI BASICS

Artificial Intelligence (AI)
AI involves machines emulating human cognition. AI can aid in elevating customer service by predicting preferences, providing relevant recommendations, and enhancing efficiency, while for teams, it automates tasks, saving time, delivering quicker service, and improving customer interactions, ultimately enhancing retention and business success.

Artificial Neural Network (ANNs)
Artificial Neural Networks (ANNs) replicate the information processing in the human brain. By recognizing patterns, ANNs can offer customers personalized recommendations and enhanced fraud detection. For teams, ANNs enable forecasting customer churn, targeted marketing, and improved customer insights within CRM systems, ultimately boosting retention and customer satisfaction.

Augmented Intelligence
Augmented intelligence blends the capabilities of computers and humans, resulting in enhanced decision-making. It enables computers to process data efficiently, while humans utilize the information to make informed choices. This benefits customers with improved service and recommendations, while also empowering teams to make strategic decisions based on insights from CRM systems.

CRM with AI
Customer Relationship Management (CRM) technology consolidates customer records to improve relationships across departments. Generative AI enhances CRM by automating tasks such as personalized email creation, leading to a more consistent customer experience and streamlined processes for teams.

Deep Learning
Deep learning is a sophisticated AI method that helps computers identify complex patterns in data, like recognizing images or understanding speech. For customers, it means more personalized and efficient experiences. For teams, it enables predictive insights in CRM systems, like understanding customer behavior and making better recommendations.

Generative AI
Generative AI, a branch of artificial intelligence, generates new content by leveraging existing data. Customers can experience things like improved, personalized marketing content, while teams enjoy faster campaign development and the opportunity to test strategies on synthetic data before deployment.

Generator
A generator is an AI software tool that generates new content based on input or training data. This benefits customers by improving AI chatbots and helps teams by creating realistic datasets for testing and training, ensuring smoother system operation and employee onboarding.

Generative Pre-trained Transformers (GPT)
Generative pre-trained transformers (GPT) are neural networks trained to generate relevant text content, enabling more personalized customer interactions and streamlining content creation and analysis for teams.

Machine Learning
Machine learning enables computers to learn and make predictions based on data, examples, and feedback. For customers, this means better products and services, while teams can use it to predict customer behavior and automate tasks.

Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of artificial intelligence that focuses on how computers can understand, interpret, and generate human language, making technology more accessible and user-friendly for customers. For teams, NLP offers valuable applications such as analyzing customer feedback, powering chatbots, and automating the creation of customer-facing content, enhancing efficiency and communication.

Parameters
Parameters in AI models are numerical values adjusted during training to optimize predictions and content generation. They influence the model’s behavior and pattern recognition. Finding the right balance is crucial – too few lead to inaccuracies, and too many can waste processing power. For customers, more parameters mean accurate responses. For teams, optimization improves AI performance without resource waste or over-specialization.

Transformer
Transformers, a type of deep learning model, excel at understanding language context, enabling businesses to enhance customer service through personalized chatbots and content generation. For teams, transformers facilitate customer interactions, sentiment analysis, and responsive customer engagement, making them valuable tools in AI-driven customer service.

AI Training & Learning

Discriminator (in GAN)
Within a Generative Adversarial Network (GAN), the discriminator plays the role of a detective, discerning between genuine and synthetic data. This technology is essential for fraud detection, enhancing customer security, and assisting teams in assessing the quality of synthetic data for personalized marketing efforts.

Generative Adversarial Network (GAN)
A Generative Adversarial Network (GAN) comprises two neural networks: a generator and a discriminator. These networks compete, with the generator creating output based on input and the discriminator determining if it’s real or fake. GANs enable personalized marketing with custom images or text for customers, while teams can use them to generate synthetic data for privacy-conscious scenarios.

Generator
A generator, like ChatGPT by OpenAI, is an AI tool that creates content by learning from training data. For customers, it enables AI chatbots to improve interactions and generate helpful content. Teams can also use generators for realistic testing and training datasets, aiding in system development and onboarding.

Grounding
Grounding in AI, also known as dynamic grounding, ensures that AI systems understand real-world knowledge and experiences, providing meaningful responses. This helps prevent irrelevant or vague responses. Grounded AI benefits customers with accuracy and relevance, enhancing the user experience, while teams benefit from reduced errors and the need for less human intervention in interactions.

Hallucination
Hallucinations in generative AI occur when it produces content that doesn’t correspond to reality or its training data, often resulting in inaccurate combinations. Consistently monitoring and managing this issue leads to a better and more reliable customer experience, while for AI teams, it emphasizes the importance of quality assurance in maintaining the accuracy and reliability of AI systems.

Large Language Model (LLM)
An LLM, or Large Language Model, is an AI system trained on vast amounts of text. It acts like an advanced conversation partner, creating personalized chatbots for customers that deliver authentic, human-like interactions for quick problem-solving. Additionally, LLMs assist teams by automating content creation, analyzing customer feedback, and efficiently managing inquiries.

Model
In AI, a model is a software program trained to recognize data patterns, serving as a mathematical representation of real-world processes. This benefits customers by providing highly accurate product recommendations and helps teams predict customer behavior and categorize customers into groups for marketing and decision-making purposes.

Prompt Engineering
Prompt engineering is the art of formulating questions or inputs to machine learning models to ensure you receive the desired answers. For customers, a well-crafted prompt enhances the performance of generative AI tools, resulting in a superior user experience. For teams, this technique can be applied for tasks such as generating personalized customer emails and extracting valuable insights from customer feedback.

Reinforcement Learning
Reinforcement learning is a technique that instructs AI models through trial and error, where they receive rewards or corrections from an algorithm based on their performance. Think of it like coaching an athlete to refine their skills. The AI makes attempts, receives feedback, and adjusts its actions to become more proficient over time, much like an athlete honing their abilities through training and guidance. Customers gain from the ongoing improvement of AI systems through human feedback, leading to interactions that are increasingly relevant and precise. For teams, reinforcement learning simplifies AI model training, expediting enhancements through customized real-world feedback.

Sentiment Analysis
Sentiment analysis entails deciphering the emotional context in the text to grasp a speaker or writer’s feelings and opinions. It finds common applications in CRM and social media to gauge brand or product sentiments. However, it may exhibit algorithmic bias as language is nuanced, making tone detection subjective, even for humans. Customers using new channels for feedback inform company decisions, while sentiment analysis helps teams gauge customer sentiments from feedback and social media, impacting brand and product management.

Supervised Learning
Supervised learning is comparable to a chef following a detailed recipe. The model (chef) is given specific instructions (questions and answers) to learn and replicate, leading to precise outcomes. It’s valuable for training systems in tasks like image recognition, language translation, and prediction. For customers, it results in more efficient systems that learn from past interactions. For teams, it enables predicting customer behavior and grouping customers based on historical data.

Unsupervised Learning
Similarly to solving a puzzle, AI autonomously uncovers hidden patterns in data, determining how the pieces fit together to make up the big picture. For customers, this translates into highly personalized experiences as hidden patterns and segments in customer data lead to tailored interactions and enhanced satisfaction. On the other hand, teams gain valuable insights into data meaning and anomalies, enhancing decision-making, productivity, and innovation within the organization.

Validation
Validation in machine learning serves as a critical step to assess a model’s performance. It entails testing the model on a dataset it hasn’t encountered during training, preventing it from simply memorizing answers. (Remember those surprise pop quizzes?) The result is better-trained models, improving program usability and the overall user experience. For teams, validation ensures the reliability of models used for predicting customer behavior or segmenting customers.

Zone of Proximal Development (ZPD)
ZPD, or the Zone of Proximal Development, parallels skill progression in education, like starting with your ABCs, moving on to words and phrases, and eventually sentences and grammar. Similarly to machine learning, it’s about training models on tougher tasks for improved learning. For customers, well-trained generative AI delivers more accurate results and teams can better foster skills for complex tasks and CRM utilization.

AI Ethics

Anthropomorphism
Anthropomorphism refers to the tendency for people to attribute human traits to AI systems, even though they lack emotions or consciousness. This can enhance customer engagement but may lead to misunderstandings or offense. Teams need to manage user expectations and communicate AI’s capabilities and limitations effectively.

Ethical AI Maturity Model
An Ethical AI maturity model is a framework that assists organizations in evaluating and enhancing their ethical AI practices, addressing transparency, fairness, data privacy, accountability, and bias. For customers, it fosters trust by showcasing responsible data usage, while for teams, it ensures adherence to ethical considerations and societal values through ongoing assessment and transparency.

Explainable AI (XAI)
Explainable AI (XAI) entails AI systems offering insights into their decision-making processes, which enhances reliability and trust for customers, especially in sensitive domains like healthcare and finance. For teams, XAI aids in comprehending AI decisions, fostering trust, improving decision-making, and refining the system.

Human in the Loop (HITL)
Human in the Loop (HITL) means having human oversight and providing feedback to AI during its development and usage. This ensures customers can trust AI systems to produce accurate and ethical results, while teams can actively customize AI models to align with their organizational goals and needs.

Machine Learning Bias
Machine learning bias arises when a computer’s decisions are influenced by limited or biased training data, resulting in unfair outcomes. Customers benefit from partnering with companies that actively combat bias, leading to more equitable experiences and enhanced trust. For teams, it’s essential to identify and mitigate bias to ensure fairness and accuracy in customer interactions, instilling confidence in their procedures.

Prompt Defense
To defend against hackers and undesirable outputs, one can establish proactive restrictions on the content machine learning models can address. This ensures that customers are not provided with offensive, confusing, or incorrect responses. For teams, it helps avoid potential problems related to unwanted information, topics, and legal issues like copyrights.

Red-Teaming
Red-Teaming entails experts testing AI systems for weaknesses through challenging prompts, ensuring AI’s safety and reliability. This results in more dependable AI for customers and allows internal teams to bolster model resilience and trustworthiness by addressing vulnerabilities.

Safety
Red-Teaming entails experts testing AI systems for weaknesses through challenging prompts, ensuring AI’s safety and reliability. This results in more dependable AI for customers and allows internal teams to bolster model resilience and trustworthiness by addressing vulnerabilities.

Toxicity
Toxicity covers offensive and harmful language. Actively monitoring and mitigating toxicity in AI systems ensures a safer and more respectful user experience for customers. For teams, addressing toxicity fosters a positive work environment, upholds a positive brand image, enhances customer safety, and reduces the risk of PR crises.

Transparency
Transparency in AI involves explaining decisions and data usage for trust and confidence. Customers gain trust in products or services, and teams can better justify AI decisions, reducing internal concerns and enhancing stakeholder trust.

Zero Data Retention
Zero data retention ensures that prompts and outputs are swiftly erased from an AI model, assuring customers that their shared information won’t be misused. For teams, it eliminates the possibility of using customer information in unintended ways, enhancing privacy and trust.