Transformers To Diffusion Models: Ai Jargon Explained

Artificial intelligence is becoming ever more prevalent in our lives. It's no longer confined to certain industries or research institutions; AI is now for everyone.

It's hard to dodge the deluge of AI content being produced, and harder yet to make sense of the many terms being thrown around. But we can't have conversations about AI without understanding the concepts behind it.

We've compiled a glossary of terms we think everyone should know, if they want to keep up.

An algorithm is a set of instructions given to a computer to solve a problem or to perform calculations that transform data into useful information.

The alignment problem refers to the discrepancy between our intended objectives for an AI system and the output it produces. A misaligned system can be advanced in performance, yet behave in a way that's against human values. We saw an example of this in 2015 when an image-recognition algorithm used by Google Photos was found auto-tagging pictures of black people as "gorillas".

Artificial general intelligence refers to a hypothetical point in the future where AI is expected to match (or surpass) the cognitive capabilities of humans. Most AI experts agree this will happen but disagree on specific details such as when it will happen, and whether it will result in AI systems that are fully autonomous.

Artificial neural networks are computer algorithms used within a branch of AI called deep learning . They're made up of layers of interconnected nodes in a way that mimics the neural circuitry of the human brain.

Big data refers to datasets that are much more massive and complex than traditional data. These datasets, which greatly exceed the storage capacity of household computers, have helped current AI models perform with high levels of accuracy.

Big data can be characterised by four Vs: "volume" refers to the overall amount of data, "velocity" refers to how quickly the data grow, "veracity" refers to how complex the data is, and "variety" refers to the different formats the data come in.

The Chinese Room thought experiment was first proposed by American philosopher John Searle in 1980. It argues a computer program, no matter how seemingly intelligent in its design, will never be conscious and will remain unable to truly understand its behaviour as a human does.

This concept often comes up in conversations about AI tools such as ChatGPT, which seem to exhibit the traits of a self-aware entity - but are actually just presenting outputs based on predictions made by the underlying model.

Deep learning is a category within the machine-learning branch of AI. Deep-learning systems use advanced neural networks and can process large amounts of complex data to achieve higher accuracy.

These systems perform well on relatively complex tasks and can even exhibit human-like intelligent behaviour.

A diffusion model is an AI model that learns by adding random "noise" to a set of training data before removing it, and then assessing the differences. The objective is to learn about the underlying patterns or relationships in data that are not immediately obvious.

These models are designed to self-correct as they encounter new data and are therefore particularly useful in situations where there is uncertainty, or if the problem is very complex.

Explainable AI is an emerging, interdisciplinary field concerned with creating methods that will increase users' trust in the processes of AI systems.

Due to the inherent complexity of certain AI models, their internal workings are often opaque, and we can't say with certainty why they produce the outputs they do. Explainable AI aims to make these "black box" systems more transparent.

These are AI systems that generate new content - including text, image, audio and video content - in response to prompts. Popular examples include ChatGPT, Dall-E 2 and Midjourney.

Data labelling is the process through which data points are categorised to help an AI model make sense of the data. This involves identifying data structures (such as image, text, audio or video) and adding labels (such as tags and classes) to the data.

Humans do the labelling before machine learning begins. The labelled data is split into distinct datasets for training, validation and testing.

The training set is fed to the system for learning. The validation set is used to verify whether the model is performing as expected and when parameter tuning and training can stop. The testing set is used to evaluate the finished model's performance.