GTC Europe 2017 - Opening Keynote | Summary and Q&A

TL;DR

Nvidia CEO Jensen Huang introduces the power of deep learning and how it is revolutionizing industries by solving unsolvable problems and enabling intelligent machines.

Key Insights

• 🏑 GPU computing and deep learning have become essential tools for researchers and developers in various fields, enabling breakthroughs and solving complex problems that were previously considered unsolvable.
• 🥺 The end of Moore's Law and the emergence of deep learning have led to a shift in computing paradigms, with GPU computing taking center stage as a powerful alternative to traditional CPUs.

Transcript

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Questions & Answers

Q: What are some of the applications of deep learning in different industries?

Deep learning has been applied in various fields, such as scientific computing, deep learning research, database acceleration, media and entertainment, self-driving cars, healthcare, and logistics, among others. It has revolutionized how tasks like image and speech recognition, language translation, and robotics are performed.

Q: How has Nvidia's GPU computing advanced the field of deep learning?

Nvidia's GPU computing has provided the computational power necessary to train deep neural networks effectively. By harnessing the parallel processing capabilities of GPUs and optimizing them with tools like CUDA and TensorRT, deep learning researchers can achieve breakthroughs in performance and efficiency that were previously impossible with traditional CPUs.

Q: What is the significance of the end of Moore's Law for the computer industry?

Moore's Law, which states that the number of transistors in a microchip doubles approximately every two years, has reached its limit due to physical constraints. The end of Moore's Law means that traditional CPUs can no longer deliver increased performance solely through transistor scaling. This has led to the rise of alternative computing paradigms like GPU computing, which offer higher performance by leveraging parallelism rather than transistor speed.

Q: How has Nvidia's deep learning technology impacted the field of voice recognition?

Nvidia has developed deep learning voice recognition technology that surpasses traditional methods in terms of accuracy and speed. By applying convolutional neural networks (CNNs) to analyze sound information and mapping it to images, Nvidia's technology enables real-time, high-performance voice recognition that can be deployed within companies' own networks, allowing them to retain and analyze data for further insights.

Summary & Key Takeaways

• Nvidia's deep learning technology has enabled breakthroughs in various fields, such as scientific computing, deep learning research, and database acceleration.

• The end of Moore's Law, combined with the emergence of deep learning, has turbocharged the adoption of GPU computing, which offers 1,000 times more performance potential than traditional CPUs.

• The introduction of the Nvidia Holodeck, a virtual reality environment, showcases the potential of deep learning in creating immersive, interactive experiences.

• Optimizing compilers like TensorRT are essential for making deep learning networks run efficiently across different devices, from massive data centers to small autonomous machines.