How We Taught Machines to Think

Author(s): Vita Haas

Originally published on Towards AI.

And Occasionally Fail Spectacularly

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Artificial Intelligence is no longer lurking in the shadows — it’s in our pockets, powering our social feeds, recommending what to binge-watch next, and occasionally generating images of people with far too many fingers. AI has evolved from a niche academic pursuit into an inescapable part of daily life. But the journey to this point has been anything but smooth. It is a tale of grandiose predictions, crushing disappointments, and moments so ridiculous they could be lifted straight from a Monty Python sketch. Let’s dive into the origins of AI and the often bizarre road that led us to today’s world of neural networks, chatbots, and algorithmic absurdity.

The story of AI starts in the 1940s, when scientists, fresh off a World War that had forced them to push technological innovation to its limits, started pondering a question: Can machines think? Enter Warren McCulloch and Walter Pitts, two neuroscientists who proposed the first theoretical model of a neural network in 1943. Their work suggested that the brain could be mimicked by a system of mathematical logic gates, inspiring later neural network research.

Meanwhile, Alan Turing, the eccentric British mathematician… Read the full blog for free on Medium.

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Published via Towards AI