How can AI acronyms be useful for entrepreneurs? In business, where the dynamics of change demand rapid decisions and adaptation, artificial intelligence becomes a key element of firms’ strategies. It not only streamlines processes and automates tasks but also opens doors to innovation and competitiveness. If you’re running a business, you might not encounter terms like LLM or RAG on a daily basis. Understanding AI acronyms used by specialists is essential to fully harness the potential of this technology. The following article is a compendium of knowledge that will help you better understand the frequently used terminology in the field of artificial intelligence. Read on.

What do artificial intelligence specialists talk about? Deciphering AI acronyms

AI specialists often use acronyms to describe complex technologies and processes. It’s worth understanding what lies behind these terms to be able to consciously leverage the opportunities offered by AI. For example, when you hear “RAG” or “XAI,” yoiu may not be sure what that means. RAG, Retrieval-Augmented Generation, is a technology that enriches language generation with information retrieval, while XAI, Explainable AI, focuses on the transparency and comprehensibility of decisions made by AI systems. We don’t need to explain what AI is today, but acronyms like these require explanation. So let’s start with one of the most ubiquitous acronyms – the general name of the technology behind ChatGPT.

LLM (Large Language Model)

LLM, or Large Language Model, is the foundation for systems such as chatbots, which can generate text, code, or translate languages. It is an artificial intelligence trained to estimate the likelihood of sequences of words, using a neural network with over 175 billion parameters.

The training of LLM involves showing examples and adjusting weights to reduce errors. In LLM, every text is represented by vectors with many numbers, determining its position and relationships in the model’s “language” space. Continuing text means following paths in this space.

Imagine them as “super readers” with vast knowledge and the ability to process information and respond in a way similar to humans. Popular examples of LLMs include:

  • Gemini Pro (Google),
  • GPT-4 (OpenAI), and
  • Llama 2 (Meta).

In business, LLM can streamline communication and information flow within a company, for example, by automatically generating reports, translating documents, and answering employees’ questions. Using LLM through chat, dedicated software, or APIs can also support the creation of new business models and strategies by analyzing large amounts of data and identifying trends that were previously unseen.

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a technique that combines semantic information retrieval with text generation. This allows the model to find relevant documents, such as those from Wikipedia, providing context that helps the text generator produce more accurate, richer, and less error-prone results. RAG can be customized, and its internal knowledge effectively modified without the need to retrain the entire model, which is costly and time-consuming. This is particularly useful in situations where facts may evolve over time, eliminating the need for retraining to access the latest information.

AI acronyms

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

GPT (Generative Pre-trained Transformer)

We all know the acronym GPT because it became part of the name of the most popular AI chatbot. But what exactly does it mean? Generative Pre-trained Transformer, GPT, is an AI model that generates text resembling human-created text by predicting the next word in a sequence. In the learning process, it acquires knowledge from billions of pages of text written by humans to later determine the probability of the next word.

GPT models are based on neural network architectures called transformers, which can generate text and respond to questions in a conversational manner. They are used for a wide range of tasks, including:

  • translating languages,
  • summarizing documents,
  • generating content,
  • writing code, and many other tasks.

GPT models can be used without further training in a technique called Zero-shot learning, or adapted to a specific task through learning from a few examples (Few-shot learning).

NLP (Natural Language Processing)

NLP, or Natural Language Processing, is the field that deals with techniques and technologies allowing machines to understand and process human language.

This forms the basis for the mentioned LLM, RAG, and GPT, allowing them to understand words, sentences, and their meanings. Thus, NLP can turn text data into useful business insights. NLP applications have broad use, extending beyond AI assistants and chatbots, to tasks like:

  • sentiment analysis – allows to determine what emotions are present in the text, for example, whether an opinion expressed on social media is positive, negative, or neutral,
  • summarizing documents – automatically creating summaries of long texts, which saves users time,
  • machine translation – enables fast and efficient translation of texts between different languages. For example, Meta’s SeamlessM4T model is capable of translating text and speech between 100 languages.

ML (Machine Learning)

ML, or Machine Learning, is the fundamental branch of AI. It is an overarching field that involves training computers to learn from data without programming them directly. AI uses data and algorithms to mimic the way humans learn, gaining experience over time.

The term “machine learning” was coined by Arthur Samuel in 1959, in the context of his research on playing checkers. Technological advancement has enabled the creation of innovative products based on ML, such as recommendation systems and autonomous vehicles.

Machine learning is a key component of Data Science, using statistical methods to forecast and make decisions in many businesses. The demand for Data Scientists is growing alongside the expansion of big data. This particularly applies to experts capable of identifying significant business questions and analyzing data. ML algorithms are created using programming frameworks such as TensorFlow and PyTorch.

AI acronyms

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Robotic Process Automation (RPA)

RPA, or Robotic Process Automation, is a technology of automation where computers mimic human actions performed in specific programs and applications. RPA is a practical application of AI that directly impacts operational efficiency. It automates routine tasks, such as data entry or customer service, allowing companies to focus on more strategic activities.

Deep Learning (DL)

Deep Learning (DL) is an advanced branch of ML that is based on neural networks inspired by the structure of the human brain. These networks learn from vast amounts of data to recognize patterns and relationships, and then use this knowledge to make predictions and decisions. DL enables the execution of the most complex tasks, such as image recognition, object identification, and classification in photos and videos.

As a result, DL is crucial for the development of technologies such as:

  • forecasting and optimizing energy consumption,
  • controlling autonomous vehicles,
  • preventing financial fraud by detecting anomalies in transactions, or
  • personalizing offers and content to individual user preferences.

Reinforcement Learning (RL)

Reinforcement Learning (RL) is a type of machine learning (ML) in which the AI model learns “on its own” through trial and error, instead of being trained from prepared data. In other words, AI adapts through interactions with the environment, receiving rewards for desirable actions and penalties for ineffective ones.

Reinforcement Learning is useful in tasks where we know exactly what outcome we want to achieve, but the optimal path to reach it is unknown or too difficult to program. For example, training robots to navigate in complex environments.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) is a system consisting of two competing neural networks:

  • Generator, which creates new data, such as images or text,
  • Discriminator, which tries to distinguish real data from generated data.

This competition motivates both networks to improve, leading to increasingly realistic and creative results.

Explainable AI (XAI)

Explainable AI (XAI) is a somewhat lesser-known but very important acronym in the field of artificial intelligence. It is an approach to AI that focuses on providing clear and understandable explanations for the actions or decisions made by AI systems. XAI is crucial for responsible AI development: transparency, compliance with legal regulations, security, and supporting innovation.

AI acronyms. Summary

AI acronyms like LLM, RAG, GPT, and XAI represent advanced technologies that are changing the way businesses operate. From process automation to better understanding customer needs – AI opens up new possibilities. Familiarity with these terms is key to navigating the field of artificial intelligence and harnessing its potential in your business. Knowledge of these technologies enables not only the optimization of existing processes but also the exploration of new areas for innovation and growth.

AI acronyms

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LLM, GPT, RAG...What do AI acronyms mean? | AI in business #91 robert whitney avatar 1background

Author: Robert Whitney

JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.

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