DeepSeek vs. ChatGPT: AI Knowledge Distillation Sparks Efficiency Breakthrough & Ethical Debate
Image Credit: Google DeepMind | Splash
On January 20, 2025, Chinese AI company DeepSeek released its latest model, DeepSeek-R1, which has garnered significant attention for its advancements in artificial intelligence, particularly through the use of knowledge distillation. DeepSeek-R1 has demonstrated performance comparable to leading AI models like OpenAI's o1, but with substantially lower computational requirements and costs. This achievement has sparked discussions about the methods employed in its development.
OpenAI has raised concerns that DeepSeek may have utilized its proprietary models during the training of R1. Specifically, OpenAI suggests that DeepSeek employed a technique known as "distillation", which involves training a smaller model to replicate the behaviour of a larger, more complex model. While distillation is a common practice in AI development, using it to create a competing model could potentially breach OpenAI's terms of service.
This situation underscores the broader implications of knowledge distillation in AI development. While the technique offers significant benefits in creating efficient models, it also raises questions about intellectual property rights and the ethical considerations of leveraging existing models to develop new ones.
Understanding Knowledge Distillation in AI Development
Imagine you have a highly knowledgeable professor who understands a subject in great depth. However, because they process vast amounts of information, they take a long time to explain things. To make learning more efficient, instead of having everyone study directly from this professor, you create a structured version of their knowledge and teach it to a student. While the student may not have the professor’s full expertise, they can still provide quick and accurate answers for most situations.
In AI, the "professor" represents a large, complex model known as the teacher model, while the "student" is a smaller, more efficient model called the student model. The process of transferring knowledge from the teacher to the student—allowing the student to mimic the teacher’s performance while being faster and more lightweight—is called knowledge distillation.
Instead of just copying the teacher’s final answer, the student learns from the probability distribution of predictions. For example, if the teacher model classifies an image as a dog, it might predict:
Dog: 85%
Wolf: 10%
Fox: 5%
Instead of only knowing "this is a dog", the student model learns why the teacher leans toward "dog" but still considers "wolf" or "fox" as possibilities. By learning these subtle patterns, the student can make better decisions and generalize more effectively, even with fewer resources.
[Read More: DeepSeek AI Faces Security and Privacy Backlash Amid OpenAI Data Theft Allegations]
Why Does the Student Model Learn Better from Probability Distributions?
If the teacher model only provides the final answer (a hard label), such as "Dog, 100%", the student model merely memorizes that a particular image is a dog. However, this does not help the student understand why it is a dog and not something else.
In contrast, if the teacher model provides probabilities (a soft label), it conveys more nuanced information. For example, the teacher might predict that the image is 85% likely to be a dog, but also 10% similar to a wolf and 5% similar to a fox. This additional information helps the student model learn how to differentiate between similar categories, leading to better generalization.
By looking at these probabilities, the student model doesn’t just memorize answers—it learns patterns and reasoning:
It understands that dogs and wolves look similar but that subtle features make "dog" more likely.
If it later sees a new, slightly different dog image, it won’t get confused as easily.
It learns to make better guesses when it encounters animals it has never seen before.
[Read More: DeepSeek’s 10x AI Efficiency: What’s the Real Story?]
Do Human Learn Using Probability Distributions?
Yes, to an extent! The brain doesn’t always make hard, binary decisions (e.g., 'this is an apple, not a pear'). Instead, it processes uncertainty and recognizes similarities based on experience, including pattern recognition. Here’s how:
1. Bayesian Learning in the Brain
Cognitive scientists believe that the brain often follows a Bayesian learning approach—which means it assigns different probabilities to different possibilities before making a final decision. For example, when a child sees a new fruit, their brain doesn’t just say, "This is definitely an apple!" Instead, it subconsciously assigns probabilities:
Apple: 80% (because it’s red and round)
Pear: 15% (because it has a similar shape but is slightly different)
Peach: 5% (because it has a similar color but fuzzier skin)
Over time, as the child gets more exposure, their brain updates these probabilities and gets better at classification.
2. Learning by Feedback and Uncertainty
If a child mistakenly calls a pear an apple and an adult corrects them by saying, 'No, this is a pear!', their brain adjusts the probability weights for future encounters through error correction and reinforcement learning. This process is very similar to how AI models refine their predictions using soft labels rather than strict classifications of hard labels.
3. Generalization in Learning
If a child only ever sees red apples, they might struggle when they first see a green apple. But because their brain has already learned a probabilistic model of what makes something an apple (e.g., round shape, smooth skin, stem, crunchiness), they can generalize and say, "This is probably an apple too, even though it’s green".
Similarly, knowledge distillation helps AI models learn broader patterns rather than memorizing specific cases.
[Read More: Italy Bans DeepSeek AI: First Nation to Block China’s AI Over Privacy Issues]
How This Relates to AI?
In knowledge distillation, the teacher model provides probability distributions (not just one "right" answer), which help the student model learn subtle variations and relationships between categories. This prevents the student model from becoming too rigid and helps it make smarter predictions in unfamiliar situations.
AI models like ChatGPT or Google’s Bard are very large and require a lot of computing power. If you try to run them on a mobile phone or a small device, they may be too slow. So, we need a way to compress or shrink these models while still keeping them smart and useful. That’s where knowledge distillation helps.
[Read More: Why Did China Ban Western AI Chatbots? The Rise of Its Own AI Models]
Historical Context and Evolution
The concept of transferring knowledge between models dates back to the early 1990s. In 1991, Jürgen Schmidhuber introduced the "neural sequence chunker", which utilized a deep hierarchy of recurrent neural networks (RNNs) to find compact internal representations of long data sequences, enhancing sequence prediction efficiency. The formalization of knowledge distillation as a model compression technique was significantly advanced by Geoffrey Hinton, Oriol Vinyals, and Jeff Dean in 2015. They demonstrated that a smaller "student" model could be trained to mimic the output probabilities of a larger "teacher" model, effectively capturing its knowledge and achieving similar performance with reduced complexity.
Real-Life Examples of Knowledge Distillation
Google's BERT (Bidirectional Encoder Representations from Transformers) is a large AI model designed for understanding human language. To make it more efficient, researchers developed TinyBERT, a compressed version created through knowledge distillation. TinyBERT is approximately 7.5 times smaller and 9.4 times faster than BERT-base, while retaining about 96.8% of its performance on natural language tasks.
AI assistants like Apple's Siri, Google Assistant, and Amazon Alexa likely employ techniques such as knowledge distillation, quantization, and pruning to run deep learning models efficiently on mobile devices. By compressing large AI models, these assistants can process language and respond quickly without solely relying on cloud-based supercomputers.
Autonomous vehicle AI models employ knowledge distillation, along with pruning, model compression, and sensor fusion, to make real-time decisions while driving. This approach enables self-driving systems to operate smaller, faster models while maintaining high accuracy, thereby improving safety and response times on the road.
[Read More: OpenAI Unveils o3: Pioneering Reasoning Models Edge Closer to AGI]
Pros and Cons of Knowledge Distillation
Knowledge distillation reduces the size and complexity of AI models, allowing them to run efficiently on resource-constrained devices such as smartphones and embedded systems. By creating smaller models that require less computational power, this technique lowers the operational costs associated with deploying AI applications. Additionally, distilled models consume less energy during inference, making AI more environmentally friendly by reducing its carbon footprint.
However, there are some challenges. During distillation, the student model may not capture all the nuances of the teacher model, which can lead to a slight drop in performance. The effectiveness of the student model also depends on the quality of the teacher model—if the teacher is poorly trained, the student inherits its weaknesses. Moreover, the distillation process involves additional training phases, such as tuning hyperparameters and aligning the student’s outputs with the teacher’s, which increases development time and complexity.
[Read More: Google Unveils "Learn About": Transforming Education with Interactive AI Tools]
Why Not Directly Train a Small AI Model?
A natural question in AI development is: if we want a small, efficient AI model, why not train it directly instead of using knowledge distillation from a larger model? The answer lies in the complexity of learning, efficiency, and generalization. Directly developing a small model comes with several challenges, making knowledge distillation a valuable technique.
Large Models Capture More Complex Knowledge: Larger AI models, known as teacher models, have more parameters and greater computational power, enabling them to recognize intricate patterns and relationships in data. In contrast, a smaller model trained from scratch may lack the capacity to capture these fine-grained details, leading to poorer performance. Think of it like a PhD professor who has spent years mastering a subject. If a high school student tries to learn the same topic independently from textbooks, they may struggle to grasp key concepts. However, if the professor provides structured explanations, the student can learn more efficiently and develop a deeper understanding.
Training a Small Model from Scratch is Inefficient: Training a small model from scratch requires large amounts of high-quality labeled data and significant training time. In contrast, large models are trained on massive datasets and have already extracted valuable knowledge. Knowledge distillation enables the student model to inherit insights from the teacher model, greatly reducing the need for exhaustive training. Instead of training a small AI model from the ground up with millions of images, we can transfer knowledge from a pre-trained large model, making the learning process faster and more efficient.
Distillation Improves Generalization: One major risk of training a small model from scratch is overfitting—where the model performs well on training data but struggles with unseen data. Knowledge distillation helps the student model learn decision-making patterns from the teacher model, making it more adaptable to new situations. Think of it like a child learning language. If they only study words from a dictionary, they may struggle in real-world conversations. However, if they learn from a fluent speaker (teacher model), they develop a deeper understanding of language, allowing them to generalize better and apply their knowledge in different contexts.
Soft Labels Offer More Context Than Hard Labels: When AI models are trained traditionally, they rely on hard labels—absolute classifications such as "This image is a dog" with 100% certainty. However, knowledge distillation introduces soft labels, which provide richer feedback. This allows the student model to grasp subtle relationships between categories rather than simply memorizing answers.
Computational Constraints Favour Smaller Models: Training large models requires massive computing power, which may not be available on mobile devices, embedded systems, or edge AI applications. Instead of building a small model from scratch, distilling knowledge from a powerful AI into a compact version allows it to run efficiently on limited hardware. For example, a self-driving car needs to process real-time data quickly, but it cannot use a massive AI model onboard due to hardware limitations. Knowledge distillation helps compress the AI, keeping it effective while ensuring fast decision-making.
[Read More: ChatGPT Enhances Search: Instant Access to Real-Time News, Sports, and More]
Is Knowledge Distillation a Form of Intellectual Property Theft?
While knowledge distillation is widely used in AI development, it raises important ethical and legal questions—particularly regarding intellectual property rights. The core concern is whether training a smaller model based on the outputs of a larger, proprietary model constitutes a form of theft or unauthorized replication.
The Case for Knowledge Distillation as a Legitimate Practice
Inspired Learning vs. Direct Copying: Knowledge distillation does not involve copying the exact internal structure or weights of the teacher model. Instead, it learns from the outputs (soft labels) provided by the teacher. This is similar to how humans learn—students don’t replicate a professor’s brain but absorb knowledge and apply it in their own way.
Fair Use and Model Generalization: AI models are trained using publicly available datasets. If a student model is only learning patterns and probabilities from an existing model, some argue that it is similar to an artist learning from another artist's style without directly copying their work. Many companies and researchers open-source their models, allowing others to improve upon them legally.
Established Precedent in AI Research: AI researchers have used distillation for years to compress large models and improve efficiency, and it is a standard practice in the field. If a model is trained using its own dataset but with guidance from another model’s outputs, it is arguably not direct theft.
The Case for Knowledge Distillation as an IP Violation
Training on Proprietary AI Outputs May Breach Terms of Service: If a company uses proprietary AI models like OpenAI’s ChatGPT to generate training data for a student model, it might violate terms of service agreements. OpenAI’s concerns with DeepSeek-R1 stem from the possibility that its model was trained using ChatGPT’s outputs without permission, which could be considered an unauthorized derivative work.
Replication Without Compensation: Large AI models require huge investments in research, data, and computation. If a company distills knowledge from a competitor’s model and sells a competing product, it could unfairly benefit from another company’s investment without paying for the underlying work.
Blurring the Line Between Learning and Stealing: If an AI model is trained solely based on another model’s responses, rather than on independent data, it closely resembles plagiarism. This is different from traditional distillation, where models are trained on large datasets rather than another model’s outputs.
[Read More: Is AI Indeed a Theft? A New Perspective on Learning and Creativity]
Is DeepSeek an AI Theft?
Knowledge distillation is a powerful technique that enables AI efficiency, but its ethical and legal implications depend on how it is applied. If used to improve open-source AI models or compress proprietary models within legal boundaries, it remains a valuable tool. But if it is used to create direct competitors without authorization, it could be viewed as a form of AI model theft—a debate that will continue as AI governance evolves.
[Read More: OpenAI's Data Leak: Unveiling the Cybersecurity Challenge]
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Source: IDSIA, arXiv, DataScienceDojo, IBM, Financial Times
