There’s more to AI than GPT

I’m on holiday, so this post will be a short one, and will definitly not contain any veiled references to contemporaneous political events.

GPT technology has become the hammer that makes everything look like a nail. But it’s worth reminding ourselves that GPT LLMs are just one kind of AI. They excel at language based tasks but are not designed for every purpose. In fact, their ability to produce credible language often masks this fact, making it seem that they are doing a good job when they’re just blagging it and playing to the crowd. So strap in, and let’s plough through what we used to call a ‘listicle’….

Other kinds of AI and what they do.

1. What are you looking at?: Convolutional Neural Networks (CNNs)

When it comes to image recognition, CNNs are the go-to AI. Designed specifically for processing and analysing visual data, these networks capture complex patterns and features in images, making them ideal for tasks like object detection, facial recognition, and even diagnosing medical conditions from X-rays. While a GPT-based AI might be great at describing an image, it doesn't hold a candle to a CNN's prowess in actually understanding the visual content.

1. On the game: Reinforcement Learning (RL)

The world of AI game playing has seen significant advancements, thanks to RL algorithms like Deep Q-Networks (DQNs) and Monte Carlo Tree Search (MCTS). These algorithms learn by interacting with their environment, optimising actions to achieve specific goals, whether it's mastering chess, poker, or even self-driving cars. When it comes to navigating complex environments and making strategic decisions, RL algorithms reign supreme over their GPT counterparts.

1. In the long term we’re all dead: Time Series Analysis with RNNs and LSTMs

For tasks involving time series analysis, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have the upper hand. They can process sequences of data and capture long-term dependencies, making them invaluable for applications like stock price prediction, speech recognition, and language translation. GPT-based AI, while versatile in language understanding, may not be as adept at handling time-sensitive data and capturing intricate temporal relationships.

1. Weird stuff: Anomaly Detection with Autoencoders and One-Class SVMs

Detecting outliers and unusual data points is crucial in many fields, such as fraud detection and quality control. Autoencoders and One-Class SVMs excel at learning patterns in the data and identifying anomalies. While GPT-based AI might be great for generating human-like text, it doesn't quite possess the same skills in pinpointing the unusual.

1. Nerd stuff: Decision Trees, Random Forests, and GBMs

Analysing structured data, such as in classification and regression tasks, often calls for decision trees, random forests, or gradient boosting machines (GBMs). These algorithms handle data with various feature types and scales, making them ideal for applications ranging from customer segmentation to predicting house prices. GPT-based AI, although powerful in natural language processing, may not be the best fit for these structured data tasks.

1. Buy this too!: Recommender Systems

Collaborative filtering, matrix factorization, and deep learning-based approaches shine in building recommender systems, learning from user preferences and interactions to deliver personalised recommendations. While GPT-based AI can generate persuasive product descriptions, it isn't designed to tailor recommendations based on individual user behaviour.

As we've seen, the world of AI is vast, and different AI approaches are better suited for different tasks. When selecting an AI solution, it's crucial to understand both the strengths and weaknesses of the available technologies. Being aware of what an AI can't do is just as important as knowing what it can do.