Jasper Capital AI Insights | How AI is Driving an Efficiency Revolution in the Wake of DeepSeek’s Rise

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Recent Hype Around DeepSeek and AI's Role in Efficiency Gains

DeepSeek has recently gained significant attention, prompting me to revisit some key machine learning concepts, such as Mixture of Experts (MoE), distillation, and multi-head latent attention. At the same time, I’ve noticed some controversies—some people claim that distillation is just transferring knowledge from large models to smaller ones, making it sound like a form of "theft" or repackaging. But is that really the case?

Model distillation is a well-established AI technique that enables a smaller "student" model to learn from a larger "teacher" model’s outputs. The goal is to compress the model, reduce computational costs, and enhance inference speed. It’s not a simple copy-paste but rather an innovation in optimizing model performance and resource efficiency—far from theft or plagiarism.

How DeepSeek Leverages AI Efficiency: Key Concepts Explained

The concepts of Mixture of Experts (MoE) and distillation are crucial to DeepSeek’s ability to run efficiently at relatively low costs. If these technical terms sound intimidating, don’t worry—we’ll break them down in simple terms and explore how large AI models can enhance various aspects of quantitative hedge fund strategies.

1. Mixture of Experts (MoE)

A Mixture of Experts (MoE) model is an architecture that combines multiple specialized “expert” models, each proficient in handling specific tasks. A gating mechanism dynamically assigns tasks to the most suitable expert model based on the input, ensuring efficient and accurate processing.

Source：OLMoE: Open Mixture-of-Experts Language Models.

Simplified Explanation

Imagine MoE as a large factory with many skilled workers, each specializing in a different area. When a task arrives, the system quickly determines which worker is best suited for the job, ensuring fast and high-quality completion. Importantly, only a select few workers are engaged at a time, avoiding unnecessary resource consumption and significantly improving cost efficiency.

DeepSeek’s MoE model has 671 billion parameters, yet for each token’s inference, it activates only about 37 billion parameters, significantly reducing computational overhead while maintaining high performance.

2. Distillation Technology

Knowledge distillation is a method that transfers the knowledge of a large, complex AI model (the "teacher") into a smaller, more efficient model (the "student").

Although large models are powerful, they require extensive computing resources and have slow execution speeds, making deployment on resource-limited devices challenging. Knowledge distillation extracts the essential knowledge from large models and compresses it into smaller models, allowing them to perform tasks efficiently even with limited resources.

SOURCE:Knowledge Distillation: A Survey

Simplified Explanation

Think of the distillation process as a Michelin-star chef teaching an apprentice. The master chef distills complex cooking techniques into concise instructions, ensuring the apprentice not only learns the steps but also understands the underlying principles. With practice, the apprentice can cook dishes nearly as good as the master’s, enabling restaurant expansion.

Similarly, knowledge distillation condenses large models into smaller ones while preserving their core capabilities.

DeepSeek’s open-source approach further enhances the effectiveness of distillation. Open sourcing makes this technology accessible to the broader AI community, enabling researchers worldwide to optimize distillation algorithms and discover new applications. As Turing Award winner Yann LeCun said:
"Open-source is surpassing proprietary models—everyone benefits from it."

Beyond MoE & Distillation: Reinforcement Learning & Multi-Head Latent Attention

DeepSeek’s efficiency also relies on reinforcement learning and multi-head latent attention mechanisms:

  • Reinforcement learning allows the model to continuously improve based on feedback, like a self-taught expert refining their skills over time.

  • Multi-head latent attention compresses key-value caches using latent vectors, significantly improving model efficiency when processing vast amounts of data.

These concepts will be explored in future articles.

AI’s Role in Quantitative Hedge Funds: The “Efficiency Revolution”

AI is ushering in an efficiency revolution. At Jasper Capital, AI has deeply integrated into strategy research, from factor discovery to portfolio optimization. Currently, over half of our fundamental and a majority of our price and volume data is processed through end-to-end machine learning models.

Beyond investment strategies, AI has also become our "super digital employee," enhancing efficiency across multiple areas.

AI Applications in Quantitative Hedge Funds

Scenario 1: Code Generation & Optimization Assistant

Developing quantitative strategies requires efficient coding. DeepSeek can automatically generate template code for tasks like data preprocessing. It also optimizes inefficient loops by converting them into vectorized operations, improving execution speed.

Scenario 2: Information Extraction from Research Reports

Investment research involves processing large volumes of reports, papers, and market data. AI models like DeepSeek can summarize academic papers and transform them into structured knowledge graphs, allowing researchers to extract key insights quickly.

Scenario 3: Alternative Data Processing

Natural language processing (NLP) capabilities enable models to analyze news, social media, and financial reports, extracting sentiment and uncovering trading opportunities from unstructured data.

Scenario 4: AI-Powered Operations & Client Services

AI automates operational tasks, such as handling account opening data, generating customized performance reports, and tailoring client risk assessments. This enhances both pre-sales and post-sales services, offering a personalized experience efficiently.

The Future of AI in Quantitative Investment

We are witnessing a new era of human-machine collaboration, where every byte has the potential to reshape quantitative investing. At Jasper Capital, we believe that true technological empowerment is not about blindly chasing trends but about using AI to illuminate hidden opportunities in financial data.

AI is no longer just a tool—it is an indispensable partner in uncovering deeper investment value.