Deep Seek is a Game Changer for AI - Computerphile

The video discusses the launch of Deep Seek and its variant DeepSeeker R1, which represent significant advancements in AI by demonstrating that high-performance models can be developed with limited resources, challenging the dominance of major tech companies. With innovative techniques like the “mixture of experts” and “Chain of Thought,” these models offer efficient training and problem-solving capabilities, potentially democratizing access to advanced AI technology for smaller organizations and researchers.

The video discusses the recent release of a new AI model called Deep Seek and its variant, DeepSeeker R1, which are seen as significant advancements in the field of artificial intelligence. Unlike many other AI models that have been released recently, Deep Seek is noteworthy because it challenges the dominance of major tech companies by demonstrating that high-performance AI can be developed with more limited resources. The video explains the concept of large language models (LLMs), which are transformer-based neural networks designed for next-word prediction, and highlights the ongoing arms race among tech companies to create larger and more powerful models.

The video elaborates on the traditional approach to training large language models, which typically requires vast amounts of computational power and data. Companies like OpenAI often keep their models proprietary, while others, like Meta, adopt a more open approach by releasing their models for public use. However, the training of these models remains out of reach for most individuals and smaller organizations due to the immense resources required. The introduction of Deep Seek signifies a shift, as it shows that efficient training can be achieved with less expensive hardware and data.

Deep Seek’s flagship model, V3, is compared to existing models like ChatGPT and LLaMA, with claims that it can achieve similar performance at a fraction of the cost—around $5 million compared to potentially hundreds of millions for larger models. The video highlights innovative techniques employed by Deep Seek, such as the “mixture of experts” approach, which allows the model to activate only the relevant parts of its network for specific tasks, thereby reducing computational costs. This efficiency not only lowers training expenses but also makes the model more accessible for use.

The video also introduces DeepSeeker R1, which incorporates a technique called “Chain of Thought.” This method enhances the model’s problem-solving capabilities by encouraging it to work through problems step-by-step, similar to how humans approach complex tasks. Unlike other models that require extensive datasets with detailed internal reasoning, R1 can learn effectively from simpler datasets by rewarding it for correct answers and internal monologues. This approach makes it easier for researchers and smaller organizations to train their own models, democratizing access to advanced AI technology.

Overall, the emergence of Deep Seek and its innovative training methods could disrupt the current landscape of AI development. The video suggests that this shift may lead to a more open-source approach in the industry, as smaller companies and researchers can now compete with larger organizations using less expensive hardware. This democratization of AI technology could foster further advancements and innovations, ultimately leveling the playing field in the AI space and challenging the traditional dominance of major tech firms.