"Shortcuts in AI: The Consequences of Data-Driven Decision Making"

"Shortcuts in AI: The Consequences of Data-Driven Decision Making"

— Undefined Trio

In the late 1980s, AI researchers were faced with a challenge: they were struggling to program machines to emulate human-like reasoning and language comprehension. The solution they found was to shift focus from trying to mimic human intelligence to leveraging statistical patterns in data for decision-making.

This transition marked a significant shortcut in AI development. It eliminated the need for machines to understand complex phenomena like language. Instead, machines could predict outcomes based on patterns in data, much like an auto-complete feature predicting the next word in a sentence without understanding the context.

Frederick Jelinek of IBM was a significant proponent of this approach, pioneering the development of "statistical language models," precursors to today's GPTs. But this transition also introduced new challenges, like sourcing sufficient data for training these statistical algorithms. In response, researchers turned to the web, harvesting data from online sources^19^.

^19^ Source: "To understand AI's problems, look at the shortcuts taken to create it" - Nello Cristianini, The Conversation

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