TECHNOLOGY FOR MARKETING

4 AI Definitions for Marketers

Understanding common AI definitions is critical for Marketers today.

In a conversation with Salesforce CEO Marc Benioff at Dreamforce 2023, OpenAI CEO, Sam Altman positioned generative AI hallucinations as a new catalyst for creativity (watch the full conversation here).

Until that moment, my perception of AI hallucination had been entirely negative. Sam’s comment encouraged me to better understand the concept of hallucination. And each definition I found included a new term I did not know. This led me to 4 AI definitions for Marketers. Here are the layers of the onion:

AI Definition 1: Hallucination

A generative AI hallucination refers to the phenomenon where an artificial intelligence system, produces outputs that are creative and imaginative but not grounded in reality. This phenomenon is particularly evident in a generative or language model like GPT. In the context of a generative model like GPT-3.5, for example, a hallucination might occur when the model generates text that is coherent and grammatically correct but contains fictional or improbable information. Hallucinations are inherently “creative” because they represent the output of a natively quantitative-based tool (a GPT) that extends beyond the “factual.” Hallucinations are the reason not to take GPT output as inherently accurate (as one attorney learned the hard way) and also the reason most generative models can be a natural enhancement to the creative process.

AI Definition 2: GPT

My own mother freely uses GPT in her daily conversation now, and I am fairly confident that despite her wisdom, she could not define the acronym. GPT stands for “Generative Pre-trained Transformer.” It is a type of artificial intelligence model designed for natural language processing tasks. GPT models are part of a broader family of transformer-based neural networks.

Generative: This refers to the model’s ability to generate text or other forms of data. GPT models are primarily used for tasks like text generation, completion, and language understanding.
Pre-trained: Before fine-tuning on specific tasks, GPT models are pre-trained on massive amounts of text data. This pre-training involves predicting the next word in a sentence. This helps the model learn grammar, syntax, semantics, and even some degree of world knowledge.
Transformer: GPT is built upon the transformer architecture. A deep learning architecture known for its effectiveness in handling sequential data like text. Transformers use self-attention mechanisms to capture dependencies between words in a sequence. This makes them suitable for a wide range of natural language processing tasks.

AI Definition 3: Neural Networks.

Neural networks: A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected processing units called neurons organized into layers. Information processes through these neurons. Each neuron applies a mathematical operation to its input (prompt) and passes the result to the next layer. Neural networks play a role in various machine learning tasks. Tasks include pattern recognition, image classification, natural language processing, and regression. In addition, Neural networks adjust connections (weights) between neurons during training to learn complex relationships in data.

And our final AI Definition: [Grounded] Prompt.

A grounded AI prompt is an input or query given to an artificial intelligence system that is rooted in a specific context, domain, or task. It provides clear and relevant information to guide the AI’s response, ensuring that the output is meaningful and contextually appropriate. Grounded prompts help AI systems generate accurate and useful responses by providing essential details or constraints for the task at hand. In the Salesforce context, Grounded Prompts focus the Einstein1 Copilot generative response on the data within your CRM system (either natively present or connected through Data Cloud).

In Sam’s interview from Dreamforce he expanded on hallucination as a creative tool. He went on to explain that when they began work on what we now know as ChatGPT they expected the innovation to impact manual labor first, knowledge work second, and, maybe, possibly creative work in the distant future. In fact, creative work has been most materially impacted in the early stages of the generative AI wave.