Intro

From image recognition to trend prediction, AI models are revolutionizing the world, but comprehending them can be difficult. Their difficult names are a major barrier. Even seasoned professionals may find it challenging to rapidly understand these names due to their frequent use of jargon and technical terms. 

One of the most important steps in making these potent tools more accessible to everyone, whether you are a developer or just interested in how AI functions, is to simplify  AI model Names. We can promote greater cooperation and communication in all domains by simplifying and making model names more understandable.

Guide to simplify AI model names

Although AI technology is developing at an astonishing rate, mainstream acceptance and comprehension are frequently hampered by the intricacy of AI model names. You’ve undoubtedly experienced the agony of deciphering ambiguous names like “BERT-based text classification model” or “Transformer model for image captioning” if you’re a developer, intimidating, or even someone who is just interested in AI. 

When all you want is to know what the model can accomplish and how it could benefit you, these complicated phrases can be intimidating. The purpose of this guide is to simplify and provide solutions for more understandable AI terminology so that everyone can more easily interact with the technology.

We Need Simpler Nomenclature for AI Models

Let’s be honest: the way we name the models contributes significantly to the frightening nature of AI. The true function of models with names like “GPT-3,” “ResNet50,” or “T5” is unclear. In actuality, these names are essentially a collection of numbers and acronyms that mean little to anyone who isn’t already familiar with the AI industry.

The problem with this complicated nomenclature is that it makes it more difficult for anyone to rapidly understand what an AI model truly accomplishes, whether they are students or business owners. If we could just call it what it is, wouldn’t that be fantastic? 

It is far simpler to understand a moniker like “Smart Image Recognition” than “CNN for Object Detection.” We don’t need to understand all the technical jargon to understand what the model is doing, thanks to its clear and informative nomenclature.

 

 A Simpler Method for Finding Capabilities

The last thing you want to do when implementing AI in your project or business is to have trouble understanding model names and their capabilities. What if there was an easy-to-understand method for figuring out each model’s capabilities just by looking at its name? To find out if the model is the best fit for your needs, you wouldn’t have to sift through research papers or a technical handbook.

For example, how about labeling it “Image Classifier for Product Photos” rather than the perplexing “VGG16 for Image Classification”? In this manner, you can tell right away that the model is made to categorize photographs, which is especially helpful for product cataloging or eCommerce.

 LLMs, or large language models

Large Language Models (LLMs) are among the most remarkable advances in artificial intelligence. These are models that can comprehend and produce text that is similar to that of a person, such as GPT-3 or BERT. They are able to write essays, translate languages, respond to inquiries, and even compose poems. However, we frequently don’t learn much about their extensive skills from their names.

Wouldn’t it be easier if we called these models “Conversation AI” or “AI Text Generator” instead? In this manner, even someone with little to no experience with AI would understand right away that the model’s function is to produce text or have conversations.

Models Gcore Supports

A range of AI models created to satisfy various business requirements are housed on the Gcore platform. They support a wide variety of models, each with a distinct purpose. The worst part is that people may still find it difficult to choose which model is best for their company due to the names’ complexity.

For instance, Gcore provides a range of models in the fields of multimodal modeling, picture generation, and speech recognition. Let’s examine some of these, and I’ll demonstrate how we can simplify everything.

Use of AI Models in Business

 Although AI models are quite powerful, they must be simple to use and comprehend in the commercial world. Imagine attempting to integrate AI into your business without fully comprehending the functions of each model. Simplified names make it easy for organizations to choose which model best suits their particular requirements.

Understanding AI models fast can help you save time and money, regardless of your industry—retail, healthcare, marketing, or finance. Speech recognition and picture generation models may create new opportunities for consumer engagement and innovation. Businesses can more easily recognize and incorporate AI into their operations with simple names.

Creation of Images

 It’s really incredible how AI can create visuals from text. With models like DALLE, you can use words to describe an image, and the AI will turn your words into a visual depiction. But what does “DALL·E” actually tell us? Not at all.

This might be streamlined to “AI Art Creator” or “Text-to-Image Generator.” People can quickly grasp the model’s possible uses, whether they be for marketing, design, or even entertainment, because the model’s goal is made apparent in its name.

Gcore-Supported Speech Recognition Models

Speech recognition is another area where AI has advanced remarkably. Businesses may use voice-activated assistants, transcription services, and even real-time translations thanks to Gcore’s AI models that can convert speech to text. Once more, though, the models have names that are so confusing that they might leave you baffled.

Let us substitute “voice assistant model” or “speech-to-text AI” for “DeepSpeech for Transcription.” This not only makes the concept easier to understand, but it also makes it seem more approachable to anyone who wants to use it in their company.

Multimodal Frameworks

 AI systems that can process and combine text, pictures, and sound are known as multimodal models. A multimodal model might, for instance, simultaneously read an article, view an image, and create a caption. Like other AI models, these are quite powerful, but it’s not immediately clear from their titles what they do.

Multimodal model names could be simplified to “Text-Image-Sound AI” or “All-in-One Data Model.” This makes it considerably simpler for businesses to assess the model’s relevance by immediately communicating that it can handle various input types simultaneously.

 

 Numerous Models, With Gcore’s Assistance

 Gcore offers a wide range of models to accommodate various AI requirements. They have models for practically any use scenario, including text generation, speech recognition, and image creation. Businesses may select the ideal solution for their unique requirements and browse the site with ease thanks to the simplification of these models’ names.

 

 The Path Ahead for Naming AI Models

 AI has a bright future, and as more models are created, it will be essential to make their names simpler so that everyone can use them efficiently. We can increase the accessibility of AI technology for companies, developers, and the general public by implementing names that are clearer and more descriptive.

Easier-to-understand AI models will lead to new opportunities, regardless of whether you’re a giant corporation searching for the next big invention or a small business owner attempting to incorporate AI into your marketing efforts.

 We can close the gap between the general public and sophisticated AI technologies by emphasizing simplicity and clarity. By working together, we can build a future in which AI is accessible to everyone, not just specialists.