What's Up with DeepSeek? 

With all the talk around DeepSeek, I thought it would be helpful to write this blog and consolidate some of the content that IBM has shared over the past few weeks. I have included the links to each of the sources for reference. Here's what I've captured..

All About DeepSeek

This article provides a comprehensive overview of DeepSeek, an AI research lab, and its various AI models, particularly DeepSeek-R1 and DeepSeek-V3. It clarifies the distinctions between these models and addresses common misconceptions surrounding them, such as the actual cost of their development and the nature of "DeepSeek-R1-Distill" models. The article explains DeepSeek's innovative Multi-head Latent Attention (MLA) technique and other engineering modifications that contribute to the efficiency and performance of their models.

The article also discusses the broader context of DeepSeek's emergence in the AI landscape, highlighting the importance of open-source models and the potential for smaller players to compete with larger tech companies. It emphasizes the need for accurate reporting and understanding of technical details to avoid misleading conclusions about DeepSeek's achievements and the overall direction of AI development.

Learn More About DeepSeek

DeepSeek's reasoning AI shows power of small models, efficiently trained

The following discusses DeepSeek-R1, an AI model developed by the Chinese startup DeepSeek, and its impact on the AI landscape. The model gained rapid popularity on the Hugging Face open-source platform due to its performance on certain AI benchmarks for math and coding, rivaling OpenAI's o1, while being significantly cheaper to use and trained with fewer resources. The article highlights the trend of Chinese AI companies, including ByteDance, adopting open-source strategies, contrasting it with the more closed approach of some US companies. DeepSeek's efficient training process, using a fraction of the GPUs typically required and employing a mixture of experts (MoE) architecture, is also emphasized. The article further explains DeepSeek-R1's use of reinforcement learning and chain-of-thought reasoning, contributing to its "meta cognition" abilities. While the model's cost-effectiveness is noted, the article also points out that the stated cost may not reflect all associated expenses. Experts interviewed in the article suggest that the long-term impact of DeepSeek-R1 will depend on developer adoption, use cases, and the ability to integrate the model safely and ethically. The article concludes by questioning whether DeepSeek-R1 and similar models will truly transform human interaction and enterprise applications, or if their impact will be more incremental.

Read the Full Article Here

Security Concerns?

This article highlights the importance of security protocols for companies integrating AI and mentions alternative enterprise AI solutions offering local deployment options, including IBM's watsonx.ai, which now supports one-click deployment of distilled DeepSeek models.

What do the experts think?

CEO Thoughts

This post, written by IBM's CEO Arvind Krishna, discusses the industry's current focus on DeepSeek and the broader trend of pursuing ever-larger AI models. He notes that model size isn't the only factor determining AI success, suggesting that cost and efficiency are equally important. He also draws a historical analogy to the decreasing costs of computing components, predicting a similar trend for AI. The post highlights IBM's focus on efficient, purpose-built AI models and open innovation, citing the company's own cost reductions as evidence. This post concludes by expressing optimism that more affordable AI will lead to broader adoption and a more transformative impact on businesses.

Check out IBM CEO Arvind Krishna's Post Here

Open source DeepSeek R1 Distilled Models now available on watsonx.ai

This article announces the availability of DeepSeek-R1 distilled models (both Llama and Qwen variants) on IBM's watsonx.ai platform. It describes DeepSeek-R1 as a powerful, open-source large language model (LLM) known for its reasoning abilities, comparable to OpenAI's o1. The article explains that DeepSeek used knowledge distillation to create smaller, more efficient versions of the model. It clarifies that IBM did not perform additional distillation and that the models are subject to their original open-source licenses.

The article outlines potential use cases for DeepSeek-R1, including planning, coding, and mathematical problem-solving, and how developers can utilize these models within watsonx.ai. It highlights the benefits of using DeepSeek models on watsonx.ai, emphasizing security, governance, and integration with other IBM tools. The article provides instructions for deploying DeepSeek models on watsonx.ai, both through the user interface and via API calls. It concludes by reiterating IBM’s commitment to open-source AI and fostering collaboration.

Read the Full Article Here

For more information on DeepSeek, check out these additional resources! 
DeepSeek Facts vs. Hype

What is DeepSeek? AI Model Basics Explained

Try out DeepSeek-R1 Distilled Models in watsonx.ai