📖 Reflections: Deep Learning from Obscure to Indispensable

From what I have read, large corporations like Google, Facebook, and Microsoft initially missed the revolutionary potential of deep learning—until visionary academics stepped in.

In the late 2000s, deep learning was a niche field with little industry attention. Enterprises focused on short-term gains and lacked the expertise to recognize its revolutionary potential. However, a group of visionary academics and researchers relentlessly pushed the boundaries of this technology.

Pioneers like Geoffrey Hinton, Yann LeCun, Yoshua Bengio, Andrew Ng, Fei-Fei Li, Alex Krizhevsky, Ilya Sutskever, Jürgen Schmidhuber, Ian Goodfellow, Demis Hassabis, Shane Legg, Alex Graves, and Terrence J. Sejnowski among others played crucial roles in advancing deep learning. Their groundbreaking work laid the foundations for the field's resurgence, overcoming skepticism and pushing the limits of what was possible with neural networks.

A significant turning point came in March 2013, when Google acquired DNNresearch, a startup founded by Geoffrey Hinton with his students Alex Krizhevsky and Ilya Sutskever. This acquisition allowed Google to quickly integrate its expertise in deep neural networks, enhancing its image and voice recognition capabilities.

Initially, major tech companies like Google were unaware of the significant enhancements GPUs could bring to deep learning. Early academic partners often had to set up GPU computers under their desks to train networks, highlighting the industry's unpreparedness. Google relied heavily on CPUs for deep learning tasks until researchers at NVIDIA and academic institutions demonstrated the substantial performance benefits of using GPUs instead. Jensen Huang at NVIDIA was an early exception, strategically investing in GPUs, which accelerated deep learning progress and made it scalable for broader applications.

As the technology matured and demonstrated its capabilities in computer vision, natural language processing, and reinforcement learning, enterprises realized its value and scrambled to catch up. Companies like Google, Facebook (Meta), and Microsoft began investing heavily in deep learning research and its application. Facebook and Google competed to acquire the pioneering AI company DeepMind, with Google ultimately succeeding in the acquisition.

Academia and visionary researchers were the original trailblazers, underscoring the importance of supporting pioneering research and not dismissing innovations due to a lack of immediate commercial returns. The focus on Large Language Models (LLMs) absorbs significant resources, diverting attention from other innovative AI areas. This can overshadow crucial exploratory research essential for advancing AI. Balancing resource allocation ensures sustained innovation across AI domains.

References

• ImageNet Classification with Deep Convolutional Neural Networks
• Nuts and Bolts of Building AI Applications using Deep Learning
• Google Scoops Up Neural Networks Startup DNNresearch To Boost Its Voice And Image Search Tech
• Google Acquires Artificial Intelligence Startup DeepMind For More Than $500M
• Why GPUs Are Great for AI
• NVIDIA's Role in Advancing Deep Learning
• Nvidia Hardware Is Eating the World
• Role of NVIDIA GPUs in Advancing Generative AI
• Nvidia’s Role in the New Wave of AI
• The minds behind artificial intelligence, and the bidding war for them
• Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73
• Learning Backpropagation from Geoffrey Hinton
• Backpropagation
• Mustafa Suleyman. The Coming Wave.
• Max Tegmark. Life 3.0.

Disclaimer

The opinions and insights presented in this article are based on personal research and understanding of the topic. This summary is for informational purposes only and may not cover every aspect. Readers should refer to additional sources for a more comprehensive understanding.