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Over the past 6 years, I’ve been updating a timeline of modern AI advances. I first created it for my PhD research and have since updated with various versions and iterations depending on who/what industry I am presenting it for. It mostly focuses on computer vision, natural language processing, and game dynamics (the various Alpha breakthroughs produced by DeepMind). All rely on what I call modern AI – the use of deep learning and reinforcement learning (advances on traditional machine learning that was mostly just computational statistics… we can have a whole separate debate on frequentist statistics vs Bayesian probabilities ☺️) along with some hardware and open source language developments that boosted progress in AI models (I’m using the loosest possible definition of AI here, another separate debate…)

Here’s the general version closing out 2022. For computer vision, it took 9 years from the combination of academic papers linking a new approach to neural networks (Hinton et al, 2006) with applying GPUs to unlock compute limitations (Raina et al, 2009) and crack what was considered a virtually impossible task – for computers to outperform humans at object detection in photo-realistic images. But the approach wasn’t enough to achieve a breakthrough in natural language. Then came a modification, a paper proposing transformer models (Vaswani et al, 2017). Within 4 years, Microsoft announced a model that paralleled humans at completion prediction, reading comprehension and commonsense reasoning. Just over a year later, ChatGPT was launched… 

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