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Irene Pérez for Quanta Magazine

Ah, QM does it again, this time in detailing the 19 essential terms defining AI, something folks like me need to read to keep up to snuff regarding tech of a different kind. :)

Here are 3 for your reading pleasure.

ARTIFICIAL INTELLIGENCE / “The science and engineering of making intelligent machines,” according to John McCarthy, who coined the phrase in 1955 (opens a new tab). Over the following decades, the ideas and practices associated with artificial intelligence have intersected with fields as diverse as cognitive science, machine learning, robotics and linguistics. The term AI now refers mainly to systems that use artificial neural networks. Specific AI applications — especially ones based on “generative” models such as AlphaFold3 or ChatGPT — are often simply called AIs.

EMBEDDINGS / Long lists of numbers that neural networks use to represent particular relationships within their training data. They’re like coordinates: In a pretrained language model, the embedding for “dog” is geometrically similar to the embedding for “puppy,” and less similar to the one for “risotto.” But because these numerical values are assigned by a machine learning algorithm, they are rarely interpretable by humans.

BIAS / Information in an AI system that influences its output in particular ways. Generative models trained on large internet datasets often contain negative biases: For example, in 2023 (opens a new tab), the AI image generator Stable Diffusion was more likely to depict “CEO[s]” as men with light skin. However, methods like reinforcement learning and symbolic regression can be used to give AI systems more productive biases, such as taking turns in a dialogue (opens a new tab) or respecting the laws of physics (opens a new tab).

Simply awesome without question. :)