Accelerated Computing and Generative AI:
Accelerated computing is necessary due to the inefficiency of general-purpose processors in handling modern workloads.
-Accelerated computing has enabled generative AI, a new paradigm in software development that unlocks novel applications.
-This combination is driving a significant platform shift in the computing industry.

The Evolution of Computers:
-The IBM System 360 introduced the concept of a central processing unit and memory hierarchies, resembling modern computer architectures.
-This architecture has remained largely unchanged for 60 years until the emergence of accelerated computing.

NVIDIA’s Pioneering Role:
-NVIDIA pioneered accelerated computing, developing the CUDA programming model and introducing GPUs to offload and accelerate workloads from CPUs.
-Accelerated computing has achieved broad success and is now widely adopted by various ecosystems.

The New Computer Architecture and AI Production:
-Accelerated computing has led to a new computer architecture based on quantum mechanics (QM) and a new computing approach called accelerated computing.
-This architecture is ubiquitous in cloud computing, system makers, and various industries.
-Programmable computers can deliver significant speedups, improving performance by orders of magnitude.

NVIDIA’s Central Role in Technological Transformations:
-NVIDIA’s accelerated computing enabled advancements in deep learning and large-scale models, leading to the rise of AI and the fourth industrial revolution.
-This has enabled the automated production of intelligence, a valuable asset that can be produced at scale.

The AI Factory and Generative AI:
-AI factories transform raw data into valuable artificial intelligence using GPUs.
-Generative AI recognizes patterns in relationships, enabling various applications, such as text generation, image synthesis, and protein design.

Understanding Meaning and Implications:
AI’s ability to understand the meaning of words and interpret various forms of information, such as text, images, and video, has opened up new possibilities for applications like self-driving cars. The implications for various industries are significant, enabling new services and products.

Advancements in Language Models:
Large language models have made considerable progress in the past year, showcasing enhanced capabilities in language comprehension, basic math, and reasoning. However, challenges remain in multi-step reasoning and slow, deliberate thinking, presenting opportunities for breakthroughs.

The Changing Software Industry:
The software industry has undergone a transformation, with a new layer of AI-powered “copilots” or assistants emerging. These AIs utilize digital tools and create new opportunities for additive growth in the software industry.

Hardware Evolution:
The traditional data center industry has been augmented by the emergence of AI Factory Centers, serving a new function in the development and deployment of AI models.

Safety Considerations:
AI’s autonomous nature requires careful attention to safety, necessitating advanced safety mindsets, technologies, methodologies, and regulations. Each industry should enhance existing regulations to ensure the safe integration of AI products and services.

Advancements in Safety Technologies:
A range of safety technologies, including data curation, truth branding, alignment of values, guardrailing, cybersecurity, and stress testing, are crucial for ensuring the responsible development and use of AI. Government policies can foster innovation and open source collaboration while addressing potential risks.

AI as a Revolutionary Computing Breakthrough:
AI is a computing breakthrough that surpasses the impact of previous revolutions, including the PC, internet, mobile, and cloud. The demand for AI supercomputers reflects the extraordinary opportunities in this field.

Unprecedented Demand for AI Supercomputers:
NVIDIA, the largest chip company in the world, experiences unprecedented demand for its AI supercomputers. The demand exceeds current production levels, indicating the vast potential of AI.

Next Waves of AI Adoption:
Countries, industries, and companies are now embracing AI, building software and AI infrastructure. The future holds the emergence of AI factories, where companies will have separate facilities for physical products and their software and intelligence components.

AI Factories: A New Norm for Every Company:
Every company will have two factories: one for physical products and another for software and intelligence. This shift highlights the importance of AI in every industry and the need for a dedicated focus on developing intelligent systems.

NVIDIA’s Reliance on AI for Design and Innovation:
NVIDIA operates five of the world’s largest supercomputers, running 24/7 to support its software and chip design processes. AI has become indispensable for NVIDIA’s operations, underscoring its transformative impact.

National AI Strategies:
Countries recognize the importance of sovereign data and AI in shaping their knowledge, culture, and society. Nations are investing in AI research, industry, sovereign data, and AI strategies to secure their future in this field.

Singapore’s National AI Strategy 2.0:
Singapore’s National AI Strategy 2.0 is a well-written and compelling document that emphasizes expanding AI talent and building AI computing infrastructure. It reflects a thoughtful approach and a vision for the future of AI in Singapore.

Challenges and Opportunities in AI:
AI presents challenges in balancing regulation for public safety with the value of generative AI to major industries. The theoretical nature of software 1.0 limits the ability of traditional software engineers to create generative AI.

Foundation of Singapore’s National AI Strategy:
A foundation model is essential for a national AI strategy. Singapore Sea Lion Large Language Model is a model that can only be created, improved, and operated in Singapore. NVIDIA has strong partnerships with DSO, DSTA, Singtel, NSCC, DBS, and other organizations in Singapore.

Importance of AI:
AI is not a nice-to-have but a must-have for increasing competitiveness in the future.

Building AI Infrastructure in Singapore:
NVIDIA is working with local sovereign cloud service providers to stand up GPU clouds in Singapore for AI research and infrastructure development. This partnership supports the national AI strategy and meets the significant GPU demand from researchers, startups, and industries.

Landmark Year for Artificial Intelligence:
2023 is a landmark year for AI, with significant developments and implications for the future. Paying attention to current AI developments and their implications can lead to valuable insights and rewards.

Singapore’s Vision for AI:
Singapore aims to be a place where AI serves as a force for good, uplifting and empowering people and businesses worldwide. NVIDIA is honored to partner with the Singapore ecosystem to realize this goal and vision.

Singapore’s Achievements:
Singapore is a nation that punches well above its weight, achieving remarkable success despite its small population. Singapore’s humility and low-key approach contribute to its special status and achievements.

Similarities and Differences in Countries’ AI Strategies:
Various countries have AI strategies with multiple pillars. Comparing strategies reveals common enablers for successful AI journeys.

Importance of Domain-Specific Language (DSL) for Deep Learning:
CUDNN is a DSL for deep learning that provides access to tensor processing. Without CUDNN, frameworks wouldn’t have access to tensor processing.

Key Actions and Strategies for Nations:
Nations must identify what is unique about their situation and formulate strategies accordingly. Comprehensive AI strategies are essential, but prioritizing the top two or three actions is crucial.

The Importance of an Iconic Instrument:
An iconic instrument attracts the best researchers and unleashes creativity. It empowers researchers and provides them with necessary backing.

Research Focus and Quantum Computing:
Research should focus on areas where NVIDIA can make a unique contribution. NVIDIA’s strategy is to be a leading quantum computing company without building a quantum computer.

Quantum Computer Emulation and Quantum Algorithms:
NVIDIA’s systems are leading emulators for quantum algorithm development. Post-quantum encryption cryptography algorithms need to be developed now.

Quantum-Classical Architecture and CUDA Quantum:
Quantum computers are good at small data, big compute tasks. Classical computers are good at big data, small compute tasks. Quantum-classical architecture and CUDA Quantum are being developed to bridge this gap.

Interaction Between Quantum and Classical Computers:
Interfacing quantum and classical computers is a key area of research. NVIDIA is working on software and hardware solutions for this interaction.

Q&A Session with Attendees:
Russell, a conference attendee, thanked Jensen Huang for his contributions to the media industry and his speech at the conference the previous year. Russell then asked about the concept of companies having two factories: one for their product and one for their AI. He also mentioned Singapore’s need for its own AI factory.

Singapore’s Potential to Unlock Constraints with AI Factories:
Russell brought up the idea of Singapore unlocking its traditional constraints in terms of talent with the advent of AI factories. He mentioned the recent announcement of Gemini and the concept of Omniverse, a high-fidelity digital twin, and large multimodal models.

Large Language Models and Alignment:
Jensen Huang explained that today’s large language models are surrounded by other AI models, including a reinforcement learning human feedback model called Alignment. He compared this model to raising an intelligent child who knows everything but doesn’t know what’s right or wrong. Through experimentation and feedback, the model learns moral and ethical boundaries.

Conclusion:
The Q&A session highlighted the importance of AI factories for both companies and countries like Singapore. Jensen Huang emphasized the role of large language models and the need for alignment to ensure their ethical and responsible use.

AI Automates Skills:
The core of AI is the automation of skills. AI can automate tasks that require 6 million people to do manually.

Importance of Singapore’s Language Model, “Sea Lion”:
Singapore needs to create its language model due to its cultural and regional relevance. Building “Sea Lion” is necessary for Singapore’s industries and will benefit the region. The foundation model of Singapore will belong to Singapore and will not be outsourced.

Functions as a Service (FaaS):
The world is moving from Platform as a Service (PaaS) to Functions as a Service (FaaS). FaaS allows for the creation of functions and applications by connecting intelligent functions. Interaction with FaaS is done through prompts, which can be vague or specific.

Finding a Niche:
To succeed, startups should find a niche where they can be a significant player. A smaller niche is better, as it reduces competition. NVIDIA survived for 30 years by serving a $0 billion niche.

Southeast Asia’s Potential for Growth:
Southeast Asia is becoming increasingly important due to the decoupling of the United States and China. The region has access to resources, talent, and infrastructure, which are essential for startups. Southeast Asia is a perfect place and time for startups to thrive.

The Third Factory: Manufacturing Intelligence:
In addition to factories that build cars and intelligence for cars, a third factory is needed to manufacture the intelligence for automated and autonomous manufacturing. Digital twins, generative AI, and other technologies will converge to realize this vision. Every instrument, building, and factory will have a digital twin, which will exist before the physical version and operate concurrently with it.

Digital Twins and Manufacturing:
Digital twins revolutionize manufacturing by enabling alignment between digital and physical versions of products, processes, and factories. Prototyping digitally saves time, money, and allows for more daring innovations. NVIDIA designs chips completely digitally, building confidence in the physical version’s success.

Digital Twin Benefits:
Reduce energy consumption, cycle time, and costs. Push manufacturing processes to their limits for better efficiency and environmental sustainability.

Chinese Companies’ Potential in Computing Technology:
Chinese companies have a lot of opportunities in computing technology. China’s rapid progress in the automotive industry showcases its potential in building world-class technology. NVIDIA’s circumstance is straightforward: they build very critical technology.

Regulation of AI Technology:
Jensen Huang acknowledges the importance of AI and its critical impact on nations and companies. The United States regulates advanced AI technology to restrict its availability to China. NVIDIA complies with regulations and designs products that comply with the latest restrictions.

Support for Startups:
Jensen Huang empathizes with startups’ struggles during chip shortages and supply issues. He suggests reaching out to NVIDIA’s GPU infrastructure and Inception program for support. He encourages startups to grow rapidly and seeks their future support when they become successful.

AI-Based Healthcare in Singapore:
NVIDIA collaborates with companies in various aspects of AI-based healthcare. They work on data centers, data twins, computers for cars, and self-driving algorithms. NVIDIA has a healthcare practice called Clari, focusing on gene sequencing, medical imaging, and molecular dynamics simulations. Jensen Huang emphasizes the importance of the BioNemo platform for digital biology and its potential in drug discovery. Digital biology is seen as the next exciting industry, utilizing computers to understand the complexity of living things.

Future of Computer Architecture:
Jensen Huang believes the line between computing and memory will persist due to different data hierarchies and temporal nature of data. The interface between memory and processing will continue to be a challenge and a core part of computer architecture. He predicts a 10-year cycle for computer architecture abstractions, with each generation taking about 10 years to become adopted.

Large Language Models as the New CPUs:
Jensen Huang sees a future where the actual processor hardware may become less significant. Large language models (LLMs) are emerging as the central processing units of the next generation of computers. LLMs will be surrounded by a network of other language models, similar to how a computer has peripherals.

A New Way of Assembling AI:
The future computer will be a general-purpose system with a central LLM surrounded by various other language models. These models will be connected through working memory, context memory, and storage. Instead of using SDKs, these models will be assembled like teams, creating an AI with specialized team members.

Challenges in Scaling Generative AI:
Organizations are experimenting with generative AI but struggling to scale it to production levels. Before the emergence of LightChain and Rags, it was difficult to conduct POCs for generative AI. There is a need for knowledge and wisdom in building vector databases, semantic search engines, and re-ranking data.

Rapid Progress in Generative AI:
Despite the challenges, there has been remarkable progress in generative AI in a short period. Many organizations are building Rags, and the field has only been active for about four months. The rate of innovation and experimentation is accelerating.

Conclusion:
Jensen Huang believes that generative AI is revolutionizing computer architecture and will lead to a new era of computing. While there are challenges in scaling generative AI to production levels, the rapid progress and widespread experimentation indicate a promising future for this technology.

Democratization of AI:
The pace of adoption of new computing capabilities, particularly architectural language models, is extraordinary. The latest generation of computing has democratized AI, making it accessible to everyone. Creating amazing applications is now easier than ever, leading to widespread POCs and easy development of chatbots.

Crossing Domains for Success:
The value of most intelligence lies in crossing domains. Specialization in multiple domains provides a broader perspective and enables the development of unique solutions. NVIDIA’s success is attributed to its ability to see the application, algorithm, system, and chip simultaneously.

Importance of Open Science and Education:
Breakthroughs in language models are largely driven by industry, but research labs in academia can also contribute significantly. Open science, open education discovery, open knowledge discovery, open source, and open publication are essential for fostering innovation. Universities need to provide researchers with the necessary instruments and resources to conduct research effectively.

Advice for Researchers in Academia:
World-class researchers need access to proper instruments and resources to conduct research. Universities need to invest in providing researchers with the necessary infrastructure and support. The investment required to enable every school in a university with adequate resources is relatively insignificant compared to other large investments like football stadiums.

Recognizing the Need for Transformation:
Jensen Huang highlights a pressing issue in the field of science: the lack of meaningful advancements due to inadequate computational resources. He emphasizes the need for a transformation in mindset, acknowledging that substantial scientific progress requires access to powerful computing capabilities.

Self-Reflection and Asking Happy Questions:
Huang encourages self-reflection on one’s ability to ask happy questions, seeking positive outcomes from discussions. He acknowledges the pressure of asking the final question of the day and suggests that a happy question can lead to a positive and fulfilling experience.

Exploring Reasoning and Optimization:
The discussion shifts to the topic of reasoning and optimization, which Huang identifies as the next breakthrough in AI. He explains that current AI systems excel in fast thinking and generating quick responses but lack the ability for slow thinking, contemplation, and long-term reasoning. Huang envisions the use of supercomputers to perform complex optimizations and reasoning tasks, enabling the exploration of various paths and outcomes.

Addressing the Funding Challenge for Startups:
Huang acknowledges the funding challenges faced by startups pursuing research that requires substantial computational resources. He suggests that startups should identify a niche application, find partners, and explore funding opportunities. Huang emphasizes the potential for startups to make meaningful breakthroughs with the right application and sufficient funding.

Conclusion:
The discussion underscores the importance of transformative thinking, exploring new frontiers in AI, and empowering startups to drive innovation through access to advanced computational resources.