News from the AI & ML world
DeeperML - #deepseek-r1
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Ben Dickson@AI News | VentureBeat
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DeepSeek, a Chinese AI company, has achieved a breakthrough in AI reward modeling that promises to enhance the reasoning and responsiveness of AI systems. Collaborating with Tsinghua University researchers, DeepSeek developed a technique called "Inference-Time Scaling for Generalist Reward Modeling," demonstrating improved performance compared to existing methods and competitive results against established public reward models. This innovation aims to improve how AI systems learn from human preferences, a key factor in developing more useful and aligned artificial intelligence.
DeepSeek's new approach involves a dual method combining Generative Reward Modeling (GRM) and Self-Principled Critique Tuning (SPCT). GRM provides flexibility in handling various input types and enables scaling during inference time, offering a richer representation of rewards through language compared to previous scalar approaches. SPCT, a learning method, fosters scalable reward-generation behaviors in GRMs through online reinforcement learning. One of the paper's authors explained that this combination allows principles to be generated based on the input query and responses, adaptively aligning the reward generation process. The SPCT technique addresses challenges in creating generalist reward models capable of handling broader tasks. These challenges include input flexibility, accuracy, inference-time scalability, and learning scalable behaviors. By creating self-guiding critiques, SPCT promises more scalable intelligence for enterprise LLMs, particularly in open-ended tasks and domains where current models struggle. DeepSeek has also released models like DeepSeek-V3 and DeepSeek-R1, which have achieved performance close to, and sometimes exceeding, leading proprietary models while using fewer training resources. These advancements signal that cutting-edge AI is not solely the domain of closed labs and highlight the importance of efficient model architecture, training algorithms, and hardware integration. Recommended read:
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Matthew S.@IEEE Spectrum
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IEEE Spectrum
, Sebastian Raschka, PhD
Recent research indicates that AI models, particularly large language models (LLMs), can struggle with overthinking and analysis paralysis, impacting their efficiency and success rates. A study has found that reasoning LLMs sometimes overthink problems, which leads to increased computational costs and a reduction in their overall performance. This issue is being addressed through various optimization techniques, including scaling inference-time compute, reinforcement learning, and supervised fine-tuning, to ensure models use only the necessary amount of reasoning for tasks.
The size and training methods of these models play a crucial role in their reasoning abilities. For instance, Alibaba's Qwen team introduced QwQ-32B, a 32-billion-parameter model that outperforms much larger rivals in key problem-solving tasks. QwQ-32B achieves superior performance in math, coding, and scientific reasoning using multi-stage reinforcement learning, despite being significantly smaller than DeepSeek-R1. This advancement highlights the potential of reinforcement learning to unlock reasoning capabilities in smaller models, rivaling the performance of giant models while requiring less computational power. Recommended read:
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Harsh Mishra@Analytics Vidhya
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DeepSeek AI has been making significant contributions to the open-source community, particularly in the realm of AI model efficiency and accessibility. They recently launched the Fire-Flyer File System (3FS), a high-performance distributed file system tailored for AI training and inference workloads. This system is designed to address the challenges of managing large-scale, concurrent data access, a common bottleneck in traditional file systems. 3FS leverages modern SSDs and RDMA networks, offering a shared storage layer that facilitates the development of distributed applications by bypassing limitations seen in more traditional, locality-dependent file systems.
DeepSeek's commitment extends to data processing and model optimization. They have introduced the Smallpond framework for data processing and released quantized DeepSeek-R1 models, optimized for deployment-ready reasoning tasks. The quantized models, including Llama-8B, Llama-70B, Qwen-1.5B, Qwen-7B, Qwen-14B, and Qwen-32B, are available as a Hugging Face collection with evaluations, benchmarks, and setup instructions. These models maintain competitive reasoning accuracy while unlocking significant inference speedups. Recommended read:
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Matthias Bastian@THE DECODER
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Chinese AI company DeepSeek is making waves in the global AI market with its high profit margins and low pricing. The company makes $200 million per year at 85% or greater profit margins, even while charging $2.19 per million tokens on its R1 model, about 25 times less than OpenAI. DeepSeek's financial data suggests a theoretical peak revenue could exceed operating costs by six times when using optimal R1 model pricing.
The company's success has prompted Tencent to unveil its own AI platform, Hunyuan Turbo S, designed specifically to compete with DeepSeek. Although Hunyuan Turbo S is the clear winner in certain cases, it still falls behind DeepSeek-R1-Zero in several instances. DeepSeek uses smart resource management and a dynamic resource allocation system which keeps costs down. Recommended read:
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@the-decoder.com
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DeepSeek's R1 model has garnered significant attention in the AI landscape. Perplexity AI has created R1 1776, a modified version of DeepSeek-R1 designed to overcome Chinese censorship through specialized post-training techniques. This modification addresses the original model's limitation of responding to sensitive topics with pre-approved Communist Party messaging. Perplexity's post-training process involved extensive data collection on censored Chinese topics, developing a multilingual censorship detection system to identify and address censored responses.
This modification allows R1 1776 to handle previously censored topics comprehensively and without bias, while maintaining its mathematical and reasoning capabilities. Furthermore, IBM has confirmed its integration of distilled versions of DeepSeek's AI models into its WatsonX platform. This decision is validated by a commitment to open source innovation and an eye on the high costs of US-originated AI models. IBM aims to broaden WatsonX's ability to perform secure reasoning by incorporating the "best open source models" available, including those from DeepSeek. Recommended read:
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@www.analyticsvidhya.com
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References:
Analytics Vidhya
DeepSeek AI's release of DeepSeek-R1, a large language model boasting 671B parameters, has generated significant excitement and discussion within the AI community. The model demonstrates impressive performance across diverse tasks, solidifying DeepSeek's position in the competitive AI landscape. Its open-source approach has attracted considerable attention, furthering the debate around the potential of open-source models to drive innovation.
DeepSeek-R1's emergence has also sent shockwaves through the tech world, shaking up the market and impacting major players. Questions have arisen regarding its development and performance, but it has undeniably highlighted China's presence in the AI race. IBM has even confirmed its plans to integrate aspects of DeepSeek's AI models into its WatsonX platform, citing a commitment to open-source innovation. Recommended read:
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@the-decoder.com
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Perplexity AI has launched Deep Research, an AI-powered research tool aimed at competing with OpenAI and Google Gemini. Using DeepSeek-R1, Perplexity is offering comprehensive research reports at a much lower cost than OpenAI, with 500 queries per day for $20 per month compared to OpenAI's $200 per month for only 100 queries. The new service automatically conducts dozens of searches and analyzes hundreds of sources to produce detailed reports in one to two minutes.
Perplexity claims Deep Research performs 8 searches and consults 42 sources to generate a 1,300-word report in under 3 minutes. The company says that Deep Research tool works particularly well for finance, marketing, and technology research. The service is launching first on web browsers, with iOS, Android, and Mac versions planned for later release. Perplexity CEO Aravind Srinivas stated he wants to keep making it faster and cheaper for the interest of humanity. Recommended read:
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Emily Forlini@PCMag Middle East ai
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www.analyticsvidhya.com
, hackernoon.com
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DeepSeek is emerging as a notable contender in the AI landscape, challenging established players with its DeepSeek-R1 model. Recent analysis highlights DeepSeek-R1's reasoning capabilities, positioning it as a potential alternative to models like OpenAI's GPT. The company's focus on AI infrastructure and model development, combined with its competitive pricing strategy, is attracting attention and driving its expansion.
The affordability of DeepSeek's models, reportedly up to 9 times cheaper than competitors, is a significant factor in its growing popularity. However, some reports suggest that this lower cost may come with trade-offs in terms of latency and potential server resource constraints, impacting the speed of responses. While DeepSeek is expanding, the Center for Security and Emerging Technology has weighed in on the US and China's race to AI dominance. The DeepSeek-R1 model is built with a Mixture-of-Experts framework that only uses a subset of its parameters per input for high efficiency and scalability. Recommended read:
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@techhq.com
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techhq.com
, datafloq.com
DeepSeek is making waves in the AI industry with its open-source AI models, challenging the dominance of proprietary models from industry giants like OpenAI and Anthropic. DeepSeek-R1, a reasoning model built on top of DeepSeek-V3, is being recognized as a significant milestone, sparking excitement within the open-source community. Its accessible AI development approach could democratize the technology by allowing anyone to download, modify, and build upon the system at a lower cost. DeepSeek claims it built its system for approximately $5.6 million – roughly one-tenth the cost of Meta’s Llama model.
The company's open-source approach has also raised some concerns. While DeepSeek has released model weights and some technical documentation, it hasn’t fully disclosed its training data, leading to questions about complete transparency. In addition, a cybersecurity company found security and privacy issues of concern in the DeepSeek iOS mobile app. Data is initially sent to the DeepSeek servers with information such as the device language and User Agent data readable. This has prompted lawmakers in the US House of Representatives to consider a ban of DeepSeek's AI models on federal devices. Recommended read:
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