2023 was the year that generative artificial intelligence captured the business world’s attention.
Now, in 2024, organizations are determining how to move from sharpening their AI strategies to deploying them.
Increasingly, the effective use of AI systems will be what separates the leaders of the pack from the laggards.
Enterprise leaders, no matter their function, must be informed about the novel technology. Being equipped with the necessary language and concepts allows them to make better decisions as well as more effectively manage AI and data-related initiatives within their organizations.
But when it comes to effectively deploying AI systems within the enterprise, there are some words business leaders need to know, and some that they can leave to their engineering and data teams (for now).
The emergence of AI has brought with it an alphabet soup of acronyms, from large language models (LLMs) to recurrent neural networks (RNNs), artificial general intelligence (AGI), and beyond, making it critical for executives not to get lost in the weeds of AI’s technicalities.
See also: Demystifying AI: Why Putting a Humanizing Spin on AI’s Impact Will Get Businesses Nowhere
Having a working knowledge of AI’s technical language is important in understanding the growing landscape of AI solutions and identifying the best fit for particular organization-level needs.
To start with the basics, an AI system is governed by algorithms, or the set of rules that machines follow to learn how to perform tasks. Algorithms are the mathematical and computational processes that enable an AI system to learn from data, make predictions or perform tasks without being explicitly programmed.
An AI model is the output or result of training an algorithm on a dataset and represents the learned patterns and relationships within the data that the algorithm has identified during the training process.
The AI dataset is the corpus of data behind both the AI system’s algorithm and model, and it provides the material from which an AI system learns patterns and makes predictions. Increasingly, businesses are using their own data to train or fine-tune AI systems.
AI systems are defined by three datasets, their training dataset, their validation dataset — which helps in tuning hyperparameters and assessing model performance — and their testing dataset, which evaluates performance in real-world scenarios.
While a firm’s data team will handle the heavy lifting around model training and integration, it is important for leaders to understand the process of how a model gets its wings, so to speak.
Read also: 12 Payments Experts Share How AI Changed Everything in 2023
Labeling and annotating data provide a way for organizations to fine-tune foundational AI models with their own proprietary data. Labeling (or targeting) identifies the desired output for a particular piece of data, while annotating data is the process of labeling unstructured data with information so that it can be read by an algorithm and model.
Fine-tuning an AI model refers to the process of making incremental adjustments to a pre-trained model to adapt it to a specific task or dataset, and this approach is particularly common in transfer learning, where a model trained on a large dataset for a general task is further trained or fine-tuned on a smaller, task-specific dataset.
Fine-tuning AI can result in narrow AI, or AI systems that are designed and trained for a specific task or a narrow set of tasks. These AI systems excel at performing a particular function, but their capabilities are limited to the specific domain or application for which they were developed.
For business executives, understanding the data-driven details of AI systems is becoming increasingly necessary due to the emergence of explainable AI (XAI) and AI explainability as a key part of AI governance.
The three key elements of XAI are interpretability, transparency and trustworthiness. The goal of XAI is to provide insights into how AI models arrive at specific predictions or decisions, making the decisioning process more transparent and accountable, rather than a black box.
“Knowing about AI will let people who use the tool understand how it works and do their job better,” Akli Adjaoute, AI pioneer and founder and general partner at venture capital fund Exponion, told PYMNTS in an interview posted in November. “… Just as with a car, if you show up to the shop without knowing anything, you might get taken advantage of by the mechanic.”
To use AI most effectively, prompt engineering is emerging as a new skill set, where users can prompt AI systems to produce the most effective result with tailored queries.
Two things that business leaders need to watch out for when deploying AI are the prevalence of hallucinations, or the capacity for an AI model to provide an answer that is factually incorrect, irrelevant or nonsensical, as well as bias in the compute system’s training data that can skew its output in undesired and ultimately inequitable ways.
The terms above just scratch the surface of the rapidly filling AI dictionary.
Terms that leaders may want to be aware of but that won’t impact their day-to-day include things like artificial general intelligence (AGI), or AI systems more intelligent and capable than humans; the plethora of mathematical terms that underpin the software engineering of the models themselves like deep learning, sentiment analysis, supervised learning, model drift, emergent behavior, reinforcement learning, and other activities best left to the experts and developers themselves.
For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
We’re always on the lookout for opportunities to partner with innovators and disruptors.
Learn More