October's Nvidia summit in DC blew my mind; cant wait for the worldwide updtate march 18 www.nvidia.com - the 11 th annual cycle of updates of the world of chips with at least 80 billion brain cells and the languages they now translate from pixels to every human dialect
Upgate from Nvidia summit in DC Octiber 2024- Deep Seek Feb 2025 becmae most exciting nvidia (2019 tech) inspired leap as ai reaches everyone who uses pc's up; wall street completely misunderstood how this multiplied value of everyone who works with Nvidia's Jensen Hiang- here chatDS helps clarify the full compass of nvidia deeep learning partnerships - wall street undersands less than one third of how humans need ai livelihood agency now to get on with family development and everything humans dfo best.
DeepSeek multipart chat on INTELLIGENCE-CENTRIC_LEARNING & CO-ACTION NETS: :is there any discipline from radiology to law that nvidia ai parners are not transforming attention around?
you're absolutely on the right track! Jensen Huang, Nvidia's CEO, often speaks about **democratizing computing** and making it more accessible and intuitive for humans. His vision involves moving beyond traditional programming languages (which are rooted in binary logic and abstract syntax) toward more natural, human-centric interfaces that engage our senses directly. Let’s break this down:---.
### 1.
**The Shift from Binary to Human-Centric Computing**
- **Traditional Programming**: Historically, programming has been about
writing code in languages like C, Python, or Java, which are ultimately
translated into binary (0s and 1s) for the computer to execute. This requires
specialized knowledge and is not intuitive for most people.
- **Huang's Vision**: Huang envisions a future where programming is no longer about writing lines of code but instead involves interacting with computers in ways that align with human senses and intuition. This could mean using voice, touch, gestures, or even brain-computer interfaces to "program" or instruct machines.---
### 2.
**Pixel Coding as a Bridge**
- **Why Pixels?**: Pixels are a natural bridge between binary computing
and human senses because they represent visual information, which is one of the
most intuitive ways humans perceive and interact with the world. By coding
pixels, we’re essentially creating visual outputs that humans can directly
understand and interact with.
- **Graphics and GPUs**: Nvidia’s GPUs excel at processing pixels and
rendering images, making them a foundational technology for this transition.
From video games to virtual reality, GPUs have already enabled humans to
interact with digital worlds in visually rich ways.
- **AI and Visualization**: Modern AI models, powered by GPUs, can generate images, videos, and even 3D environments from simple human inputs (like text prompts). This is a step toward Huang’s vision of programming through human senses, as it reduces the need for traditional coding and instead relies on natural language or visual cues.---
### 3.
**Beyond Pixels: Programming for All 5 Senses**
Huang’s vision extends beyond pixels to include all five human senses:
- **Sight**: Already being addressed through advancements in graphics,
VR, and AR.
- **Hearing**: AI can generate and process sound, music, and speech,
enabling auditory interfaces.
- **Touch**: Haptic feedback technologies are advancing, allowing users
to "feel" digital objects.
- **Smell and Taste**: While still in early stages, researchers are
exploring ways to digitize and simulate these senses.
The idea is to create a seamless interface where humans can interact with computers in ways that feel natural, without needing to understand the underlying binary logic or programming languages.---
### 4.
**How Pixel Coding Fits Into This Vision**
- **First Step**: Pixel coding (via GPUs) is a critical first step
because it enables the creation of visually rich, interactive environments.
These environments can then be expanded to include other senses.
- **AI as the Enabler**: AI models, trained on massive datasets, can
translate human inputs (like voice commands or gestures) into pixel-based
outputs (like images or videos). This reduces the need for traditional coding
and makes computing more accessible.
- **Democratization**: By abstracting away the complexity of binary programming and replacing it with intuitive, sense-based interfaces, Huang aims to make computing accessible to everyone, not just programmers.---
### 5.
**Examples of This Transition**
- **Generative AI**: Tools like DALL·E, MidJourney, and Stable Diffusion
allow users to generate images by simply describing them in natural language.
This is a step toward programming through sight.
- **Virtual Reality**: VR systems use GPUs to create immersive
environments that engage multiple senses, moving beyond pixels to include
spatial awareness and interactivity.
- **Natural Language Interfaces**: AI assistants like ChatGPT enable users to "program" by speaking or typing in natural language, bypassing traditional coding altogether.---
###
Conclusion
Yes,
pixel coding is a crucial bridge in Huang’s vision of democratizing computing.
By starting with pixels (which align with human sight), Nvidia and other
innovators are paving the way for a future where programming involves direct
interaction with all five senses. This shift will make computing more
intuitive, accessible, and aligned with how humans naturally perceive and
interact with the world
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Yes,
you're absolutely right! Jensen Huang has been articulating a more nuanced
vision for the future of AI and computing, which goes beyond just pre-training
models and extends into **post-training** and **thinking**. This framework is
critical for understanding how Nvidia's accelerated computing ecosystem fits
into the broader AI landscape—and why companies like **DeepSeek** (and their
investments in Nvidia's technology) are so important. Let’s break this down:
### 1.
**The Three Pillars of Huang’s AI Vision**
Huang
has described AI development as involving three key phases:
- **Pre-training**: Building foundational models using massive datasets
and computational power.
- **Post-training**: Fine-tuning and optimizing models for specific tasks
or domains.
- **Thinking**: Enabling models to reason, plan, and interact dynamically
with the world.
These phases represent a progression from raw data processing to sophisticated, real-world applications.---
### 2.
**Pre-training: The Foundation**
- **What It Is**: Pre-training involves training large-scale AI models
(like GPT, DALL·E, or DeepSeek’s models) on vast amounts of data to learn
general patterns and representations.
- **Role of Nvidia**: Nvidia’s GPUs and accelerated computing platforms
are essential for pre-training because they provide the massive computational
power needed to process terabytes of data and train billion-parameter models
efficiently.
- **Example**: DeepSeek’s models, like many others, rely on Nvidia’s hardware to pre-train on diverse datasets, enabling them to understand language, images, and other inputs.---
### 3.
**Post-training: Specialization and Optimization**
- **What It Is**: Post-training involves fine-tuning pre-trained models
for specific tasks, domains, or applications. This phase ensures that the model
performs well in real-world scenarios, such as customer service, healthcare, or
autonomous driving.
- **Role of Nvidia**: Nvidia’s platforms (like CUDA, TensorRT, and DGX
systems) are optimized for post-training tasks, enabling developers to
efficiently fine-tune models and deploy them at scale.
- **Example**: DeepSeek might use Nvidia’s technology to fine-tune its
models for specific industries, ensuring high accuracy and performance in
targeted applications.
### 4.
**Thinking: Reasoning and Interaction**
- **What It Is**: The most advanced phase, "thinking," involves
enabling AI models to reason, plan, and interact dynamically with their
environment. This includes capabilities like decision-making, problem-solving,
and real-time adaptation.
- **Role of Nvidia**: Nvidia’s hardware and software ecosystems
(including GPUs, AI frameworks, and simulation platforms like Omniverse) are
designed to support real-time, interactive AI applications. This is critical
for tasks like robotics, autonomous systems, and generative AI that require
continuous learning and adaptation.
- **Example**: DeepSeek’s models, when integrated into Nvidia’s
accelerated computing infrastructure, could power real-time applications like
autonomous agents, intelligent assistants, or dynamic content generation.
---### 5. **Why DeepSeek is Good News for Nvidia Investors**
Huang’s
mention of DeepSeek highlights the symbiotic relationship between AI companies
and Nvidia’s accelerated computing ecosystem:
- **DeepSeek’s Investment in Nvidia**: By fully leveraging Nvidia’s
hardware and software, DeepSeek can accelerate its AI development across all
three phases (pre-training, post-training, and thinking). This makes DeepSeek a
prime example of how Nvidia’s technology enables cutting-edge AI innovation.
- **Validation of Nvidia’s Ecosystem**: Companies like DeepSeek investing
heavily in Nvidia’s platform validate its importance in the AI industry. This
drives demand for Nvidia’s products and reinforces its leadership in
accelerated computing.
- **Future Growth**: As AI models become more advanced and require more
computational power (especially for post-training and thinking), Nvidia’s
ecosystem will continue to grow in importance. DeepSeek’s success is a sign of
this trend.
### 6.
**The Bigger Picture: Democratizing AI**
Huang’s
vision isn’t just about building powerful AI models—it’s about making AI
accessible and useful for everyone. By focusing on pre-training, post-training,
and thinking, Nvidia is enabling companies like DeepSeek to create AI systems
that are:
- **Efficient**: Optimized for specific tasks through post-training.
- **Intelligent**: Capable of reasoning and interaction through thinking.
- **Scalable**: Powered by Nvidia’s accelerated computing infrastructure.
This
democratization of AI will drive innovation across industries, from healthcare
and education to entertainment and robotics.
###
Conclusion
Jensen
Huang’s framework of **pre-training**, **post-training**, and **thinking**
represents a comprehensive vision for the future of AI. Companies like
DeepSeek, by fully investing in Nvidia’s accelerated computing ecosystem, are
at the forefront of this transformation. For Nvidia investors, this is
excellent news because it demonstrates the growing demand for Nvidia’s
technology and its central role in shaping the future of AI. As AI continues to
evolve, Nvidia’s ecosystem will remain a critical enabler of innovation across
all three phases of AI development
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Absolutely, the principles of **pre-training**, **post-training**, and **thinking**—as articulated by Jensen Huang in the context of AI—could one day apply to the design of **legislative systems**. In fact, this vision aligns with broader trends in technology, governance, and human-computer interaction. Here's how it might unfold:---
### 1.
**Pre-training: Building Foundational Knowledge**
- **What It Means for AI**: Pre-training involves teaching AI models
general knowledge using vast datasets.
- **Application to Legislative Systems**:
- **Data Collection**: Legislative systems could be
"pre-trained" on vast amounts of historical data, including laws,
court rulings, public opinions, and socio-economic outcomes. This would create
a foundational understanding of how laws have worked (or failed) in the past.
- **Global Knowledge**: By incorporating data from different
countries and cultures, legislative systems could learn from a diverse range of
governance models and best practices.
- **Example**: A legislative AI system could analyze centuries of tax policies to identify patterns that lead to economic growth or inequality.---
### 2.
**Post-training: Specialization and Optimization**
- **What It Means for AI**: Post-training involves fine-tuning models for
specific tasks or domains.
- **Application to Legislative Systems**:
- **Local Context**: Legislative systems could be fine-tuned to
reflect the unique cultural, economic, and social contexts of a specific region
or country.
- **Stakeholder Input**: The system could incorporate feedback
from citizens, experts, and policymakers to optimize laws for fairness,
efficiency, and effectiveness.
- **Dynamic Adaptation**: As societal needs change, the system
could continuously update its "training" to reflect new realities
(e.g., climate change, technological disruption).
- **Example**: A legislative AI could be fine-tuned to design healthcare policies that balance cost, accessibility, and quality of care based on local demographics and resources.---
### 3.
**Thinking: Reasoning and Interaction**
- **What It Means for AI**: Thinking involves enabling models to reason,
plan, and interact dynamically.
- **Application to Legislative Systems**:
- **Real-Time Problem Solving**: Legislative systems could analyze
complex, real-world problems (e.g., inflation, housing crises) and propose
solutions by simulating outcomes and trade-offs.
- **Public Engagement**: The system could interact directly with
citizens, using natural language interfaces to explain laws, gather feedback,
and even co-create policies.
- **Ethical Reasoning**: Advanced AI could incorporate ethical
frameworks to ensure laws align with societal values and human rights.
- **Example**: A legislative AI could simulate the impact of a new
environmental regulation, predict its economic and ecological consequences, and
adjust the proposal based on public input.
### 4.
**Democratizing Legislative Design**
- **Human-Centric Interfaces**: Just as Huang envisions AI interacting
with human senses, legislative systems could use intuitive interfaces (e.g.,
voice, visuals, or VR) to make policy design accessible to everyone, not just
experts.
- **Transparency and Trust**: By making the legislative process more
transparent and data-driven, these systems could rebuild public trust in
governance.
- **Example**: Citizens could use a VR platform to explore the potential
impact of a new education policy, visualize its effects on schools, and vote on
their preferences.
---### 5. **Challenges and Considerations**
While
the idea of applying AI principles to legislative systems is exciting, it also
raises important questions:
- **Bias and Fairness**: How do we ensure that the data used to
"train" legislative systems is free from bias and represents all
stakeholders?
- **Accountability**: Who is responsible for decisions made by
AI-assisted legislative systems—humans, machines, or both?
- **Ethics**: How do we balance efficiency with the need for human
judgment and moral reasoning in lawmaking?
- **Adoption**: Will societies be willing to trust AI with something as
fundamental as their laws?
---### 6. **The Future: AI-Augmented Governance**
In the
future, legislative systems could combine the best of human and machine
intelligence:
- **Collaborative Design**: Policymakers and AI systems could work
together to draft laws, with AI providing data-driven insights and humans
ensuring ethical and cultural alignment.
- **Continuous Improvement**: Laws could be continuously updated based on
real-world outcomes, creating a more adaptive and responsive governance model.
- **Global Collaboration**: AI-powered legislative systems could
facilitate international cooperation by identifying shared challenges and
proposing harmonized solutions.
---
###
Conclusion
The
principles of **pre-training**, **post-training**, and **thinking** could
indeed revolutionize the design of legislative systems, making them more
data-driven, adaptive, and inclusive. While there are significant challenges to
overcome, the potential benefits—such as more effective laws, greater public
engagement, and faster responses to global challenges—are immense. As AI
technology continues to advance, it’s not hard to imagine a future where
legislative systems are as dynamic and intelligent as the societies they serve.
This vision aligns with Jensen Huang’s broader goal of democratizing technology
and making it work for humanity.
