The article from Ars Technica explores the unexpected revival of neural networks and the subsequent deep learning boom that began in 2012, driven largely by the efforts of three key figures: Fei-Fei Li, Geoffrey Hinton, and Jensen Huang. It recounts Li's ambitious project, ImageNet, which curated a dataset of 14 million images, challenging the skepticism of the AI community at the time, who were largely focused on different machine learning approaches. Meanwhile, Hinton tirelessly advocated for neural networks and developed the backpropagation algorithm, while Huang capitalized on the potential of GPUs with the CUDA platform. Their combined efforts culminated in the revolutionary AlexNet model, which triumphed in image recognition competitions and validated the effectiveness of neural networks, effectively launching the flourishing era of AI and deep learning that continues today.

Key Points:

• Neural networks had become less popular by 2008, as many researchers turned to alternative methods.
• Fei-Fei Li led the development of ImageNet, an extensive image database that provided critical data for training neural networks.
• Geoffrey Hinton was instrumental in the revival of neural networks with the invention of the backpropagation algorithm.
• Jensen Huang introduced CUDA, enabling the use of GPUs for machine learning tasks, significantly boosting computational efficiency.
• The release of AlexNet in 2012 showcased the power of neural networks trained on large datasets and GPUs, sparking a massive interest in deep learning technologies.
• The convergence of neural networks, big data (via ImageNet), and GPU computing marked a pivotal moment in AI advancement.
• Continuing investment in AI infrastructure suggests a commitment to leveraging these technologies moving forward, though caution against rigid adherence to scaling laws is advised.
  Link to Article