Welcome to MODL, where AI models are critiqued, honed and developed from scratch. But first, let’s get on the same page:
What exactly is an AI model?
The term AI model is a very general term. It can refer to any kind of programme designed to complete a task or process involving at least one strand of artificial intelligence technology. However, it usually means something slightly more specific; a model designed to process data and identify patterns or produce conclusions that would have taken a human years.
But there are other kinds of AI models. We know of generative AI models that are designed to produce some kind of output, typically words or images, and these often use a technology called natural language processing (NLP). This means these programmes can be instructed using human language instead of computer code, which is at the heart of the AI revolution we’re experiencing today.
The cutting edge of technology has become accessible for virtually anyone.
Large language models
Large language models or LLMs are the most ubiquitous form of AI. This is what ChatGPT is. Other well-known ones are Claude, Gemini and Llama. Most of the tech giants of the world now have their own LLM available to use for free (with premium options).
LLMs use the technology known a natural language processing, which we mentioned earlier. This means we can interact with these highly complex programs using natural language (the words we use to communicate in everyday life). Since no coding knowledge is required, LLMs are bringing a world of possibility to entrepreneurs and professionals without technical know-how.
Since these LLMs are capable of highly complex tasks like writing technical articles, creating detailed images, and even coding websites or apps, you can emulate the output of swaths of team members as long as you have access to the internet. This is why AI is truly changing the game from a business and innovation perspective.
Chatbots and conversational AI
A significant genre of what we think of a AI models are AI-powered chat interfaces. The reality is that some of these are more AI-powered than others. Think about it: chatbots have existed on website for decades, long before AI was mainstream.
These chatbots weren’t using AI technology such as natural language processing, they were simple following rule-based protocols like “if this, then that”. No AI required. What we’re now familiar with today, are more sophisticated chat experiences that process and analyse information on a much deeper level. These do require branches of AI
Advanced AI models
If you’re really into AI models, you can look into the different types of advanced AI models. This get a little more technical, but AI is far more than ChatGPT, despite the terms becoming almost synonymous in recent years. Here are eight types:
1. Transformers
These models are used in tasks like understanding and generating text, such as in chatbots and translation services. They handle large amounts of data effectively, which makes them good at tasks like writing essays, summarizing articles, or answering questions.
2. Generative adversarial networks (GANs)
GANs consist of two models that work against each other to create realistic-looking data, like images or music. One model tries to create fake data, while the other tries to detect it, which helps them improve over time. They’re often used for creating realistic images or even deepfake videos.
3. Convolutional neural networks (CNNs)
These models are mainly used for analyzing visual data, such as identifying objects or patterns in pictures. They are very effective at tasks like recognizing faces, sorting photos, or detecting items in a video.
4. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks
These models are designed to work with data that comes in a sequence, such as sentences or time-based data. They are used for tasks like predicting the next word in a sentence, understanding speech, or analyzing patterns over time.
5. Reinforcement learning models
These models learn by trial and error, like a robot learning to walk. They interact with their environment, learn from feedback, and get better at a task over time. They are often used in areas like game playing or training autonomous vehicles.
6. Autoencoders
Autoencoders are used to find patterns in data without being told what to look for. They can help compress data, remove noise from images, or detect unusual patterns, like finding errors in a dataset.
7. Diffusion models
These are newer models that are good at generating high-quality images. They can create very realistic pictures or enhance details in an image, making them useful for graphic design or artistic tasks.
8. Graph neural networks (GNNs)
These models are designed to work with data that has connections or relationships, like a social network or a map. They are good at analyzing how things are linked together, making them useful for tasks like recommending friends, finding routes, or understanding relationships in data.
More info coming soon!
If you’re looking for the original MODL website – that was a platform to book a model for modelling jobs such as photoshoots, commercials and fashion shows.