La IA china DEEPSEEK: una explicación A FONDO

DeepSeek: A Tsunami in Artificial Intelligence

Introduction to DeepSeek's Impact

• DeepSeek has emerged as a significant player in artificial intelligence, causing shockwaves in Silicon Valley with its rapid advancements.

• The company claims to have developed an AI model comparable to ChatGPT with only $5 million in investment, highlighting the efficiency of their approach.

Key Events and Developments

• On January 20, China sent a strong message to the U.S. by unveiling a language model that rivals ChatGPT, emphasizing its capabilities despite U.S. restrictions on technology.

• DeepSeek's application has become the most downloaded globally, raising concerns about data privacy and user trust regarding potential ties to the Chinese government.

Analysis of DeepSeek's Models

• DeepSeek operates as an AI lab based in China with around 200 employees and has launched two models: V3 (Christmas release) and R1 (January release).

• The R1 model is noted for its reflective capabilities, drawing comparisons to OpenAI’s O1 model which costs users $200 per month; however, DeepSeek offers it for free.

Market Reactions and Implications

• Analysts discovered that DeepSeek trained its R1 model at a cost of just $5.3 million over two months, challenging previous assumptions about AI development costs.

• This revelation led to significant market reactions, including fears about Nvidia's future sales as companies reconsider their reliance on expensive chips for AI development.

Broader Questions Raised

• The emergence of DeepSeek raises critical questions about the sustainability of Big Tech investments in AI and whether they can maintain competitive advantages against lower-cost alternatives.

The Strategic Importance of AI and Nvidia in US-China Relations

The Role of Artificial Intelligence

• Artificial intelligence, particularly from Nvidia, is viewed as a strategic asset for the USA and its allies amid a technological cold war with China.

• The U.S. previously held a five-year advantage over China in AI technology due to advanced Nvidia chips, which China cannot replicate or manufacture at that level.

Limitations on Chinese AI Development

• Huawei has developed some AI chips but they still lag behind Nvidia's capabilities, hindering China's progress in AI research and products.

• Restrictions imposed by President Biden in October 2022 limited the export of Nvidia chips to China, leading to the creation of less capable models like the A800 and H800.

Impact of Export Controls

• The H800 chip is significantly restricted compared to the H100 chip used widely in U.S. data centers; it can be likened to selling a Ferrari limited to 100 km/h.

• Despite being less capable, H800 chips are expensive, costing over $70,000 each in the Chinese market.

Evidence of Smuggling Activities

• Approximately 15% of global Nvidia chip sales go to Singapore, raising suspicions about smuggling operations where Chinese companies import through third parties.

• Major Chinese tech firms like Baidu and Alibaba have been utilizing cloud services with advanced Nvidia GPUs for their model training.

New Restrictions on Chip Sales

• To close legal loopholes allowing advanced chip usage by China, Biden implemented new restrictions limiting sales only to allied nations.

• Countries deemed neutral must seek permission from the U.S. Commerce Department before purchasing Nvidia chips; countries like China and Russia face outright bans.

DeepSeek's Controversial Practices

• DeepSeek allegedly utilized illegally imported chips for their model development; their documentation outlines how they trained these models using interconnected clusters of NVIDIA H800 chips.

• There are claims that DeepSeek may have used higher-capacity A100 chips during earlier training phases despite stating lower costs associated with renting lesser capacity units.

Financial Discrepancies in Chip Acquisition

• Rumors suggest DeepSeek acquired around 10,000 A100 chips valued between $100 million and $300 million; however, experts argue that their innovations wouldn't align with such usage.

Cost Analysis of AI Model Training

Overview of DeepSeek's Training Costs

• The training cost for DeepSeek is estimated to be only 3% of OpenAI's O1 model, highlighting a significant difference in investment.

• Elon Musk's Colossus data center development has reportedly cost around $1 billion, contrasting sharply with the lower costs associated with DeepSeek.

Limitations in Cost Reporting

• The document discussing DeepSeek’s costs focuses solely on the final phase of training, omitting earlier research and experimental costs.

• Key expenses such as algorithm development and data acquisition (14.8 trillion pieces of data) are not included in the reported $5.3 million cost.

Comparison Metrics and Benchmarking

• Despite lower chip power, DeepSeek's quality is assessed through benchmarks rather than raw computational power.

• DeepSeek utilized OpenAI’s Strawberry Benchmark for self-evaluation, indicating a reliance on established standards for comparison.

Benchmark Performance Against OpenAI

Results from Strawberry Benchmark

• The Strawberry Benchmark includes various tests: mathematics (AIME), biology (GPQA), programming tests, and logic reasoning (Zebra).

• In benchmark results, DeepSeek performs comparably to OpenAI, winning three tests while OpenAI also wins three.

Innovative Techniques Used by DeepSeek

Algorithmic Innovations

• To overcome limitations, DeepSeek introduced innovations in algorithms that optimize chip capacity using machine code.

Distillation Technique

• A distillation method allows an apprentice model to learn from a superior existing model through dialogue—this enhances knowledge transfer between models.

Reinforcement Learning Approach

• A revolutionary reward system enables the R1 model to learn independently by incentivizing correct reasoning processes during training.

Simplified Inference Process

DeepSeek: A New Era in Artificial Intelligence

Implications of DeepSeek's Inference Techniques

• DeepSeek has introduced new compression techniques that simplify inference, which is the process of generating responses from AI models. This innovation raises questions about its implications for artificial intelligence and global geopolitics.

Shifting Dynamics in Generative AI

• Historically, the United States was perceived to have absolute dominance in generative AI. However, DeepSeek demonstrates that high-quality models can be developed more quickly and with fewer resources outside the U.S.

Cost Revolution in AI Training and Inference

• The emergence of DeepSeek leads to significantly lower training costs and reduced expenses for inference queries made to AI chatbots, marking a revolutionary shift in accessibility for companies and individuals.

Open Source vs. Closed Models Debate

• With DeepSeek's open-source model available at no cost, it challenges the existing debate between closed and open artificial intelligence models, potentially leading to increased competition among developers.

Local Implementation of AI Solutions

• Organizations may opt to install powerful AI systems locally (on-premise), reducing costs while ensuring better protection of private data compared to using cloud-based services.

Geopolitical Balance Shift

• The advancements by DeepSeek could rebalance innovation scales between the U.S. and other nations, particularly China, allowing more countries to develop their own competitive models without U.S. restrictions.

Who is Behind DeepSeek?

Background on Liang Wengfeng

• Liang Wengfeng founded DeepSeek as a side project under his quantum mutual fund operator HiFlyer. His background includes significant experience in artificial intelligence and investment strategies.

Early Life and Education

• Born in 1985 in Sanyang, China, Wengfeng excelled academically from a young age. He studied calculus early on and later pursued artificial intelligence at Shenyang University.

Development of High Flyer Fund

• At a young age, he established High Flyer, managing around $8 billion in assets by leveraging advanced algorithms and artificial intelligence for investment decisions.

DeepSeek's Vision

Goals Beyond Competition with Major Firms

• Unlike typical tech giants like OpenAI or Google, Wengfeng aims to create an advanced open research entity focused on sharing innovations rather than competing directly with them.

Commitment to Open Source Technology

DeepSeek: Open Source AI and Its Implications

The Vision Behind DeepSeek

• DeepSeek aims to attract talent and foster innovation, emphasizing the importance of its team and research capabilities over commercial applications.

• Liang Wenfeng's commitment to keeping DeepSeek as an open artificial intelligence model reflects a desire for transparency and public access.

• The mission of DeepSeek is driven by curiosity to explore artificial general intelligence, raising questions about the sincerity of this commitment.

Understanding Open Source with DeepSeek

• Distributed under the MIT license, DeepSeek allows users to market, improve, and download the software freely.

• Users have successfully downloaded smaller versions of DeepSeek on devices like Raspberry Pi, indicating its accessibility for experimentation.

• Companies like Perplexity are integrating DeepSeek into their services, showcasing its growing presence in the U.S. market.

Data Privacy Concerns

• There are concerns regarding data sent through DeepSeek's services potentially being accessed by the Chinese government.

• Differentiating between accessing DeepSeek via its website/app versus local installations is crucial for understanding data privacy implications.

• Using official services sends sensitive information to China; thus, companies should restrict access within corporate networks.

Censorship Issues

• Certain queries are censored on official platforms; up to 1,500 questions have been identified that receive no response from DeepSeek.

• Notable censorship includes topics related to Tiananmen Square and sensitive political issues concerning China.

Global Impact of Open Source AI

• China's advancements in AI benefit from its robust electrical infrastructure and talent pool capable of bypassing restrictions imposed by other countries.

• The democratization of AI means that foundational models can be developed without exorbitant costs or advanced chips, allowing more countries to participate in AI development.

Artificial Intelligence and Its Economic Implications

The Value of Data in AI

• The shift towards greater security in artificial intelligence (AI) leads to cheaper inference costs, benefiting data holders like YouTube, pharmaceutical companies, and states.

• Companies with user-friendly products and extensive distribution capabilities will also benefit as the cost of providing AI services decreases.

Edge AI and Hardware Advancements

• Edge AI allows for processing on personal devices rather than relying solely on cloud computing, making it more efficient.

• Apple stands to gain significantly due to its advanced hardware integration, despite current reliability issues with its AI products.

Jevons Paradox in Technology

• Satya Nadella's reference to the Jevons paradox highlights that increased efficiency from technological progress can lead to higher overall resource consumption.

• This paradox suggests that major tech companies are likely to accelerate their plans for AI development as resources become cheaper.

Impact on Major Players

• DeepSeek's emergence provides a rationale for big tech firms like Microsoft and Meta to advance their projects without canceling them.

• Ironically, U.S. government restrictions on chip imports may hinder innovation domestically while spurring advancements abroad, particularly in China.

Competitive Landscape Shifts

• Foundational model companies like OpenAI and Anthropic face challenges as training these models becomes increasingly costly and commoditized.

• Microsoft's partnership dynamics with OpenAI indicate a strategic shift amidst rising competition; Anthropic may soon be acquired due to its lack of consumer traction compared to newer entrants like DeepSeek.

Future Prospects for NVIDIA

• While NVIDIA is expected to maintain high sales volumes in the short term, long-term growth is uncertain due to rapid innovations reducing model sizes and training costs.

• The potential need for numerous chips for general artificial intelligence raises questions about future demand stability for NVIDIA’s products.

Investment Implications of DeepSeek

Thesis on Exponential Investments in AI

The Future of AI Investments

• The speaker proposes a thesis that investments in artificial intelligence (AI) will experience exponential growth, driven by significant advancements like DeepSeek.

• There is speculation about a potential bubble effect in the financial markets concerning companies like Nvidia, with investors weighing whether to buy or sell.

• The semiconductor industry is cyclical, characterized by periods of decline and innovation; Nvidia's recent performance may not defy this trend.

• Investors are expected to shift focus from infrastructure and chip production to companies creating AI applications and services, particularly smaller firms with niche innovations.

• Key questions for the market include the necessity of large capital expenditures (CAPEX) for AI development and the timeline for recovering these investments.

Innovations Introduced by DeepSeek

• A technical overview of DeepSeek's innovations will be provided, focusing on three models: V2, V3, and R1.

• The V2 model introduced two key concepts: DeepSeek MOE (Mixture of Experts) and DeepSeq MLA (Multi-Head Latent Attention).

Mixture of Experts Architecture

• The Mixture of Experts architecture allows models to operate more efficiently by activating only relevant expert sub-models instead of the entire model during queries.

• This approach reduces memory usage and data traffic speed requirements when processing queries compared to traditional methods.

Multi-Head Latent Attention

• The Multi-Head Latent Attention modifies Google's transformer architecture to compress context windows significantly, enhancing inference efficiency.

Advancements in the V3 Model

DeepSeek's Innovations in AI Training

Distillation Techniques in AI Models

• DeepSeek employs distillation techniques similar to those used by major AI companies like OpenAI and Google, leveraging internal models for training.

• The process involves a "teacher" model (likely OpenAI's O1) guiding a "student" model, where the student learns to extract answers from the teacher.

• This method accelerates learning and compresses data requirements, enabling new models to provide sensible responses more efficiently.

API Utilization and Model Training

• Large models utilize APIs to connect with organizations, allowing businesses to integrate AI capabilities into their operations.

• While distillation can be developed through chatbots, it requires significant computational resources and incurs high costs.

• Experts believe DeepSeek has effectively utilized older model distillation techniques to enhance output quality while reducing training time.

Optimization of NVIDIA Chips

• DeepSeek has optimized performance on NVIDIA's H800 chips by bypassing the proprietary CUDA architecture.

• They operate directly on a deeper environment of NVIDIA chips known as PTX, which allows for more efficient chip utilization without an interface layer.

Reinforcement Learning Innovations

• The R1 model was trained rapidly using reinforcement learning without human intervention, contrasting traditional supervised methods.

• In this approach, the model learns independently through trial and error—akin to a child learning to ride a bike without supervision.

Unique Learning Paradigms

• The R1 model utilizes a reward system that reinforces correct reasoning in its answers rather than just factual correctness.

• This method led to an "aha" moment for the model, allowing it to develop unique paradigms of thought previously unknown in AI development.

AI Competitive Landscape

The Interchangeability of AI Models

• AI models from organizations like OpenAI, Anthropic, and XAI are perceived as interchangeable and easily replaceable, which diminishes their competitive value.

• Competition in the AI sector is evolving to rely on a combination of factors beyond just technology.

Key Elements of AI Development

• Human talent is becoming increasingly important alongside technological advancements such as chips.

• The development of data centers capable of housing millions of chips is crucial for future competitiveness in AI.

• A critical long-term factor will be the electrical infrastructure's capacity in each country to support the energy demands of these AI data centers.

The Future Dynamics of AI Competition

• The discussion introduces a metaphorical "Game of Thrones" scenario within the realm of artificial intelligence, highlighting its competitive nature.