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【即時翻譯字幕 直播完整版】AI教父黃仁勳台大演講 宣告「新的運算時代」開始 |三立新聞網 SETN.com

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💫 Short Summary

The video discusses the evolution of computing, the impact of accelerated computing on various industries, and Nvidia's advancements in AI technology. It highlights the transition to generative AI, the development of digital humans, and the Blackwell supercomputer. The focus is on energy-efficient token generation, the importance of deep learning switches, and the future of AI in robotics. Nvidia's Blackwell platform aims to enhance AI performance and scalability, with a focus on physical AI understanding physics. The video showcases the potential of AI in revolutionizing industries and creating generative AI factories through advanced technology and robotics.

✨ Highlights
📊 Transcript
The importance of determination and perseverance in facing challenges.
Pushing through obstacles and standing strong against adversity.
Staying motivated, never stopping, and believing in one's own power to overcome difficulties.
Keeping moving forward, dreaming big, and spreading one's wings to achieve success.
Emphasis on achieving greatness and reaching unlimited potential.
Encouragement to take control, give your all, and aim high towards goals.
Message of empowerment and determination to overcome challenges.
Theme of being ready to shine and face obstacles with confidence and resilience.
Inspiration for perseverance, self-belief, and seizing opportunities for growth and success.
The importance of perseverance and determination in overcoming challenges.
The emphasis on never giving up and staying focused on reaching goals.
Encouragement to take risks and go all in to achieve personal goals.
The message conveys resilience and the belief in oneself, even in the face of obstacles.
The motivational tone of the segment is enhanced by the background music and applause.
The importance of determination and resilience in overcoming challenges.
Emphasizing the strength and power within oneself to face obstacles.
A message of empowerment and self-belief to not give up and stand strong in adversity.
Conveying a sense of moving forward and never looking back.
Focusing on inner strength and perseverance in the face of difficulties.
Nvidia CEO discusses generative AI impact on industries and partnerships in Taiwan.
Emphasis on AI infrastructure and future of computer graphics and simulations.
Role of accelerated computing and AI in shaping industry highlighted, with focus on Omniverse.
Evolution of computer technology since the 1960s and transformative potential of current technologies mentioned.
Excitement expressed for future of AI and computer science innovations.
The evolution of computing has led to continuous advancements in hardware and software separation through operating systems.
As CPU performance scaling has slowed down and computational requirements continue to grow, a shift towards accelerated computing is necessary.
Accelerated computing, utilizing specialized processors like GPUs, can greatly enhance performance for applications that benefit from parallel processing.
Benefits of Accelerated Computing with GPUs
GPUs can reduce processing time and costs when paired with CPUs.
Performance can increase significantly with only a slight rise in power consumption and costs.
Adapting software for accelerated computing can be challenging, as it requires rewriting algorithms to run in parallel.
Advancements like CDNN deep learning library have made accelerated computing more accessible in the past 20 years, enabling applications in AI and physics.
Key highlights of recent technological advancements in telecommunications and data processing.
Cuda accelerates 5G radio and enables software-defined telecommunications networks.
Kitho and C Quantum are utilized for chip manufacturing and quantum computer simulation.
CDFF boosts data processing speed and supports popular libraries like Spark and Pandas.
Domain-specific libraries such as CDNN play a vital role in enhancing deep learning algorithms.
Google has integrated CDF into the cloud to enhance data processing speed.
Pandas is the most popular data science library in the world, used by 10 million data scientists and downloaded 170 million times each month.
It is considered the Excel of data scientists and can now be used in Google's cloud data centers platform accelerated by KDF.
The acceleration of data processing with CUDA has reached a virtuous cycle, breaking the traditional 60-year model of computing platforms.
By reducing the cost of computing, developers and scientists can discover new algorithms that drive further advancements in computing technology.
Advancements in AI driven by training large language models with internet data and belief in cheaper computing costs.
The development of CUDA has enabled generative AI and the creation of a digital twin of Earth for predicting climate change impacts.
AI has made continuous weather prediction at every square kilometer possible with minimal energy consumption.
Collaboration with scientists and understanding the foundation of deep learning are crucial for AI advancements.
Nvidia's GPU architecture was reinvented after 2012 to focus on deep learning, resulting in the creation of the world's first AI supercomputer.
They scaled up their technology over time, leading to the development of large language models such as GPT-3.
OpenAI released GPT-3 in 2022, which quickly became popular for its human-like interaction and ease of use.
The technology enabled training on vast amounts of data and understanding natural language patterns, revolutionizing the AI field.
The Night Market holds sentimental value for the speaker, evoking childhood memories and featuring a memorable fruit lady vendor.
Introduction of Chad GPT marks shift to generative AI era.
Chad GPT can produce tokens like words, images, and more for various applications.
Generative AI era signifies a new industrial revolution with AI factories creating commodities for every industry.
Scalable and repeatable methodology leading to rapid invention of generative AI models across all sectors.
AI transforming from data processing tools to intelligence-generating factories, revolutionizing manufacturing processes.
Evolution of computing from CPUs to accelerated GPU computing has led to the development of AI, generative AI, and an industrial revolution impacting various industries.
The shift towards generating data instead of retrieving it is gaining momentum due to its energy efficiency and contextual relevance.
The future of computing involves computers generating skills and performing tasks, with the emergence of new software like Nims and Nvidia inference microservices.
The complexity of AI models requires distributed computing across multiple GPUs for efficient processing, directly impacting throughput, revenue, and quality of service in data center operations.
Highlights of AI in a Box Software:
The AI in a box software includes Cuda, CNN tensor RT, and Triton for inference services, making it cloud-native and scalable in a Kubernetes environment.
It offers management services, monitoring capabilities, and common APIs for easy communication.
Users can chat with the AI after downloading it, which integrates pre-trained models and offers various versions for different domains like healthcare and digital biology.
The AI can be used for customer service agents across industries, representing a significant advancement in customer service capabilities.
The future of applications involves the use of Nims, expert teams that can seamlessly work together to provide personalized and engaging interactions in various industries.
Nims will be crucial in fields such as customer service and healthcare, revolutionizing the way tasks are broken down and executed.
Digital humans like Sophie will play a significant role in industries by offering AI interior design suggestions, customer service support, and healthcare assistance.
The aim is to create more natural interactions with digital humans to bridge the gap of realism, leading to endless possibilities and applications in different sectors.
Advances in generative AI and computer graphics are leading to the development of digital humans capable of human-like interactions.
Nvidia's Ace offers digital human technology in the form of microservices for developers to easily incorporate into their systems.
AI technology is increasingly being integrated into PCs and laptops to provide assistance to users through AI-powered applications.
AI is transforming the training of models by allowing them to learn from large datasets without requiring extensive human labeling.
These advancements are revolutionizing the capabilities of AI technology and expanding its potential applications.
Advancements in AI technology require a physical understanding of physics principles to create realistic images and videos.
Learning from video, synthetic data, and computer simulations are crucial methods for developing AI systems with this capability.
Blackwell, a second-generation Transformer engine, incorporates technologies like large chips, secure AI, and MV link for connecting multiple GPUs.
The system also features a reliability and availability engine that tests every transistor and memory component to prevent failures in supercomputers with thousands of GPUs.
The Blackwell supercomputer: A technological marvel with unparalleled computational capabilities and energy efficiency.
Blackwell's performance far exceeds previous models, leading to a 350 times reduction in energy consumption.
The improved energy efficiency enables faster data processing and model training, making previously impossible tasks achievable.
Advancements in technology have significantly decreased energy usage, enhancing overall efficiency and lowering costs.
Blackwell's production board is the most complex and high-performance computer ever created, highlighting the rapid progress in computational power and energy efficiency within just eight years.
Energy efficiency improvements in generating tokens for language models.
Transition from significant energy requirement to using only 0.4 joules per token.
Introduction of Blackwell chips and DGX systems for more efficient token generation.
Advancement in technology to support large language models, including liquid cooling options and the development of the MV link switch for connecting multiple GPUs.
Significant increase in bandwidth and AI flops with lower power consumption emphasized.
Importance of mathematics in switches for deep learning.
Nvidia's DGX showcases the MV link spine connecting multiple GPUs for power savings.
Challenges of adapting Ethernet architecture for deep learning tasks in AI factories.
Emphasis on the bursty nature of communication in AI systems and the importance of the last arrival of data.
Necessity of creating provisions for efficient data processing in Ethernet networks.
Highlights of RDMA Technology for Communication between Nick and the Switch.
Importance of congestion control, adaptive routing, and noise isolation in optimizing network performance.
Lower network utilization leads to improved training time and cost efficiency.
Introduction of Spectrum X800 and mention of upcoming models for handling large numbers of GPUs.
Future trend towards millions of GPU data centers due to the increasing presence of generative AI, emphasizing the need for advanced infrastructure.
Introduction of Nvidia's Blackwell platform and its success in the AI industry.
The Blackwell platform aims to enhance performance, reduce costs, and scale AI capabilities for global adoption.
Nvidia emphasizes innovation and customization by offering disaggregated systems for different data centers and customer needs.
The platform includes CPU, GPU, NVLink, and NVSwitch components, providing a versatile solution for various industries and applications.
Company focuses on modular systems with Blackwell platform for building and selling entire data centers in parts yearly.
Pushing technology limits in TSMC process technology, packaging technology, and memory technology.
Developing Reuben platform and Reuben Ultra platform with emphasis on architectural compatibility and software richness.
Transitioning to Nvidia Blackwell platform and discussing next wave of AI involving physical AI understanding physics and cognitive capabilities for robotics.
Robotics powered by physical AI is revolutionizing industries with the development of robots that can learn, perceive, and understand the world around them.
Multimodal llms are enabling robots to have a deeper understanding of their environment through various senses.
Reinforcement learning is essential for robots to advance their skills by learning from human demonstrations.
Nvidia Omniverse acts as an operating system for creating physical AI, allowing robots to autonomously interact with objects and navigate their surroundings.
The next wave of AI is focused on robotics powered by physical AI, which has the potential to transform various industries.
The complexity of building robotic warehouses and factories is discussed in the video segment.
Companies like Foxcon are utilizing SD Ks, APIs, Edge AI technology, and PLC systems to enhance their operations.
Foxcon is creating digital twins of their factories using Nvidia Omniverse for efficient planning and operation monitoring.
The digital twins optimize floor layouts, line configurations, and camera placements for improved efficiency.
AI applications like Metropolis are used by Foxcon for robotic perception and manipulation, showcasing the significant role of technology in modernizing traditional data centers.
Integration of Isaac manipulator and perceptor into AI robots for manufacturing efficiencies.
Leading industrial automation software and systems companies collaborating on factories globally.
Advanced robotics technology adoption in Taiwan, focusing on physical AI in factories.
Nvidia's production plans for self-driving cars with autonomous features in 2022 and 2026.
Advancements in cognitive and understanding capabilities of humanoid robots.
The development of robots and the ease of adapting human-like robots due to the abundance of training data.
Excitement about progress in robotics showcased with demonstrations of robots during the presentation.
Emphasis on the future of robotics and AI, focusing on building advanced computers that can walk and roll.
Mention of the similarity in technology between building traditional and advanced computers, anticipating an extraordinary journey ahead in this field.