Beyond AI Buzzwords: List of Words Beside AI to Discuss AI Solutions

AI has become a buzzword, something everyone is discussing, but it feels like no one is discussing it.

My nephew just learned the word “actually.” He says “actually” at the end of almost every sentence and in the middle of every paragraph.

Ironically, my nephew is “actually” not saying anything when he uses the word “actually.”

Like my nephew using the word “actually,” the word “AI” is tagged onto brands, logos, banners, marketing, and sales pitches.

I put together a list of words with examples and definitions to help us move from talking about AI to discussing AI.

ONE

AI or Artificial Intelligence: Instead of just following instructions, AI can analyze data, recognize patterns, and make decisions independently.

Nonexample: My washing machine has advanced features, but it typically wouldn’t be considered AI. Most washing machines operate based on pre-programmed instructions and sensors to perform tasks like washing, rinsing, and spinning. They don’t adapt to new situations like an actual AI system would.

Example: However, some newer washing machine models might incorporate primary forms of machine learning for tasks like optimizing water usage or adjusting cycle times based on load size.

TWO

Generative AI or Generative Artificial Intelligence: Instead of following explicit instructions, generative AI learns patterns from existing data and uses them to generate new, original content. The keyword is “generate” because it produces new content.

Nonexample: When you call to book a doctor’s appointment, the recording asks you to press 1 for accounting and 2 for scheduling.

Example: You might experience a “pre-canned” generative AI response if you call to book a doctor’s appointment; the recording asks you for your name and patient ID number and then starts calling you by your name: “Thank you *name for calling us today. How can I help you?” You might experience an “open” generative AI response if you call to book a doctor’s appointment and the first time you call, the recording says, “Thank you for calling today. How can I assist you?” but the second time you call, the recording says, “Glad to be of service, what can I do for you today?”

THREE

Natural Language Generative AI or Open Generative AI Responses: These generated AI responses are not prescribed but spontaneously generated based on the input. Some say natural language responses are like talking to a human, but I have yet to experience this human-like conversation. It is important to remember that even natural language-generated AI responses depend on input or data to create a response.

Nonexample: Asking grandma to tell you about her childhood. The only way you will hear about my grandma’s childhood is to listen to her because there are no records to fact-check her life, not a journal or article, maybe a weathered photo or two, but she doesn’t even have a social media profile. The “input” or data my grandma uses to recall her life is all from her memories and self-reflections; the input or data is limited.

Example: Going to a company website and using their website chat (assuming the chatbot is connected to the enterprise repository and documentation) to ask how to use a specific feature.

FOUR

Input: This article is specifically on AI; the examples are typical input types for generative AI.

Nonexample: Someone’s memories. We have yet to develop technology to generate responses from individuals’ memories. In short, anything that is not published, such as gut knowledge, intuitive problem-solving, or intuition, can not be considered input because there is nothing the technology can access to generate a response directly.

Examples:

Repository: a central location for storing and managing data, files, or information. It allows organized access, retrieval, and version control of digital assets.

Database: Collection of structured data organized for efficient storage and retrieval, commonly using tables with predefined schemas.

Dataset: Collection of related data records used in machine learning and AI for training models or conducting experiments.

Corpus: Large collection of texts or data used for linguistic analysis, natural language processing, or training language models. It helps study language patterns and behaviors.

Knowledge Base: Structured repository of information and facts about a specific domain, often used in AI systems for storing data, rules, and relationships for querying and decision-making.

Library: Collection of resources, such as code modules, functions, or algorithms, that are organized and made accessible for reuse. Libraries are commonly used in software development and data science for code and algorithms.

Vault: Secure storage facility for sensitive or valuable data. Vaults often have additional security measures, such as encryption and access controls, to protect the stored information.

Warehouse: Large-scale storage facility for data from multiple sources, often used for data analysis, reporting, and business intelligence. Data warehouses handle large volumes of data and provide tools for querying and analyzing stored information.

FIVE

Data Governance: Data governance involves managing the availability, usability, integrity, and security of data within an organization. It ensures that data is accurate, consistent, and reliable, meeting the organization’s needs and complying with regulations and standards.

Nonexample: Copy and paste your company legal documents and trademark product information into ChatGPT and ask it to summarize into an email.

Example: Solutions like Home?—?NeuralSeek , which automates processes to flag input sources and training data while tuning generated responses, are accurate. Including features that support best practices such as:

Data Quality Management: The company ensures the training data is accurate, relevant, and representative by cleaning and preprocessing it to remove errors, duplicates, and biases.

Data Security and Privacy: The company employs strict measures to protect customer data for training, including encryption, access controls, and anonymization techniques to comply with data privacy regulations like GDPR and CCPA.

Data Stewardship or the Human Touch: Data stewards oversee the management, maintenance, and quality of training data, ensuring compliance with data governance policies.

Data Lifecycle Management: The company sets clear policies for managing data in developing and deploying, including defining retention periods, archival procedures, and deletion protocols to ensure responsible and ethical data management.

Metadata Management: Metadata, training data sources, data transformations, and model performance metrics are documented and managed to ensure transparency and traceability in AI development, aiding stakeholders in training.

Compliance and Risk Management: Regular audits and assessments to ensure compliance with regulations, industry standards, and internal policies, including identifying and mitigating risks associated with data misuse, bias, or non-compliance with ethical guidelines.

CONCLUSION

This is a good list, but there are more terms, such as hallucination, jailbreak, and looping. If you want to move away from talking about AI to discussing AI, join the conversation register at NeuralSeek Learning Labs . Explore using generative AI in learning labs, like NeuralSeek Learning Labs and Cerebral Blue?—?YouTube .

AI continues to dominate conversations across industries; it’s imperative to move beyond mere buzzword AI and delve into meaningful discussions about its applications. Understanding the nuances between “Generative AI” and “Natural Language Generative AI” facilitates informed dialogue. Moreover, understanding foundational concepts like “Data Governance” ensures responsible deployment, safeguarding accuracy, privacy, and compliance. As we navigate this dynamic landscape, embracing terminology that accurately reflects the capabilities and complexities of generative AI is essential for driving innovation and fostering collaboration.