Latest news with #DeepSeekR1
India Today
4 days ago
- India Today
DeepSeek says its R1 update rivals ChatGPT o3 and Gemini 2.5 Pro in performing math, coding and logic
Earlier this year, DeepSeek surprised the whole world with the launch of its R1 model which was capable of rivaling – or at least coming close in performance to – much larger AI models that were developed in the US. The DeepSeek R1, on the other hand, was developed by a Chinese startup at a fraction of the cost of models like ChatGPT and Gemini. R1 has now been upgraded and DeepSeek says that it is much better at reasoning, math and logic. 'In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimisation mechanisms during post-training,' DeepSeek wrote in a post on Hugging Face. advertisementDeepSeek says that it showed 'outstanding performance' in doing 'mathematics, programming, and general logic'. The AI company claims that after the update the general performance of the R1 model is 'approaching that of leading models, such as O3 and Gemini 2.5 Pro.' 'Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks,' DeepSeek adds in its says that besides being good at problem solving and reasoning, the upgraded R1 or R1-0528 also hallucinates less. The model now also apparently offers a 'better experience for vibe coding'. However, a developer on X alleges that the latest DeepSeek model is significantly more restricted when it comes to sensitive free speech issues, calling it the most heavily censored version so far, particularly when it comes to criticism of the Chinese government. '...the model is also the most censored Deepseek model yet for criticism of the Chinese government', the developer wrote in a post. This was first reported by TechCrunch. The developer says that the new DeepSeek R1 model avoids giving direct answers to questions about sensitive subjects such as the internment camps in China's Xinjiang region, where over a million Uyghur Muslims have reportedly been detained. Although the model occasionally references Xinjiang as a human rights concern, the developer notes that it frequently echoes the Chinese government's official position when responding to related queries. 'Deepseek deserves criticism for this release: this model is a big step backwards for free speech,' he writes in a post on X. The developer reportedly conducted a test on a website called SpeechMap (which he has developed), where one can compare how different models treat sensitive and controversial subjects.
Geeky Gadgets
4 days ago
- Business
- Geeky Gadgets
New DeepSeek R1 Coding Performance Tested : Pros, Cons and Real-World Applications
What if artificial intelligence could not only write code but also think through problems like a seasoned developer? Enter DeepSeek R1, the latest breakthrough in AI-driven coding and creativity. Built on the innovative V3 architecture, this model promises to transform how we approach complex programming tasks, offering unparalleled accuracy and adaptability. Yet, even the most advanced technologies come with trade-offs. While DeepSeek R1 excels in generating intricate web applications and dynamic animations, its tendency to overanalyze simple problems raises questions about its efficiency in high-pressure scenarios. Is this the future of coding, or does its brilliance come at a cost? In this in-depth breakdown, Prompt Engineering explore how DeepSeek R1 is redefining the boundaries of AI in coding and beyond. From its remarkable chain of thought reasoning to its ability to craft visually stunning outputs, this model is a fantastic option for developers and creative professionals alike. However, we'll also uncover its limitations, such as its struggles with logical deduction and occasional inefficiencies. Whether you're curious about its competitive edge against models like Gemini 2.5 or eager to understand its potential for creative problem-solving, this analysis will provide a balanced look at what makes DeepSeek R1 both impressive and imperfect. How does it stack up against the challenges of real-world applications? Let's find out. DeepSeek R1 AI Overview Transforming Coding: DeepSeek R1's Unparalleled Performance DeepSeek R1 sets a new standard in coding, showcasing exceptional performance that distinguishes it from earlier models. Whether you're developing interactive web applications, crafting animations, or designing complex algorithms, the model demonstrates outstanding accuracy and efficiency. Its performance in live coding benchmarks rivals leading competitors like Gemini 2.5 and Claude 3.7, cementing its status as a formidable player in the AI landscape. Generates interactive web applications with minimal input, streamlining development workflows. with minimal input, streamlining development workflows. Excels in creative coding , such as futuristic interface design and dynamic animations. , such as futuristic interface design and dynamic animations. Adapts seamlessly to real-time coding scenarios, enhancing productivity. Despite these strengths, the model occasionally takes excessive processing time for straightforward tasks. This inefficiency could pose challenges in time-sensitive applications, highlighting an area for potential refinement. Enhanced Reasoning: Transparency with Room for Growth One of DeepSeek R1's standout features is its advanced chain of thought reasoning. The model provides detailed, step-by-step explanations of its processes, allowing users to follow its logic with ease. This transparency is particularly valuable for debugging and understanding complex outputs, making it a useful tool for developers and analysts alike. Delivers structured reasoning paths that enhance clarity and comprehension. that enhance clarity and comprehension. Maintains raw chain of thought visibility , making sure transparency in decision-making. , making sure transparency in decision-making. Occasionally overanalyzes simple queries, leading to inefficiencies in certain scenarios. While this capability is a major strength, the model's tendency to overthink can slow performance in situations requiring quick, straightforward solutions. Addressing this issue could further optimize its utility in diverse applications. DeepSeek R1 Coding Performance Evaluation Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on Deepseek Ai models. Creative Potential: Unlocking New Possibilities Creativity is another domain where DeepSeek R1 excels. The model is capable of generating visually compelling outputs, ranging from animations to themed designs and interactive constellations. These features make it an invaluable asset for creative professionals seeking innovative solutions to complex challenges. Produces intricate, aesthetically pleasing visual outputs that meet professional standards. that meet professional standards. Demonstrates creativity in designing unique applications, interfaces, and artistic projects. in designing unique applications, interfaces, and artistic projects. Supports imaginative problem-solving, making it a versatile tool across industries. This creative versatility positions DeepSeek R1 as a valuable resource in fields such as entertainment, education, and digital design. However, making sure consistency in its creative outputs remains an area for ongoing development. Logical Deduction: Strengths and Challenges DeepSeek R1 showcases robust reasoning capabilities but occasionally struggles with logical deduction. In some cases, it defaults to patterns derived from its training data rather than applying strict logical constraints to solve problems. This limitation underscores an area for improvement in future iterations. Demonstrates inconsistent performance in tasks requiring rigorous logical reasoning. in tasks requiring rigorous logical reasoning. Relies on training data patterns in certain scenarios, which can limit its adaptability. in certain scenarios, which can limit its adaptability. Opportunities for refinement exist to enhance its logical deduction capabilities. Addressing these challenges will be critical for improving the model's reliability and effectiveness, particularly in applications requiring precise logical reasoning. Processing Efficiency and User Interface Advancements Built on the V3 architecture, DeepSeek R1 introduces significant advancements in processing efficiency and user interface (UI) generation. The model supports both reasoning and non-reasoning modes, allowing users to tailor its behavior to their specific needs. However, its tendency to overthink can sometimes offset these efficiency gains. Improved processing efficiency compared to earlier versions, allowing faster task completion. compared to earlier versions, allowing faster task completion. Enhanced UI generation capabilities for seamless and intuitive user experiences. capabilities for seamless and intuitive user experiences. Customizable modes that cater to diverse applications and user preferences. These improvements make DeepSeek R1 a versatile tool for a wide range of users. However, further optimization is necessary to fully address its overthinking tendencies and maximize its potential. Competitive Edge: Benchmarks and Comparisons In coding benchmarks, DeepSeek R1 consistently delivers strong performance, often surpassing models like Gemini 2.5 in specific tasks. Its capabilities are comparable to Claude 3.7 in many scenarios, solidifying its position as a competitive option in the AI landscape. Excels in coding and creative benchmarks , demonstrating superior performance in targeted tasks. , demonstrating superior performance in targeted tasks. Outperforms some competitors in areas such as real-time coding and creative output generation. in areas such as real-time coding and creative output generation. Comparable to leading models in reasoning and problem-solving capabilities. While official metrics from DeepSeek are still pending, early results suggest that R1 is a formidable player in the field. Its ability to compete with and, in some cases, outperform established models highlights its potential as a leading AI solution. Future Prospects: Evolving the DeepSeek Series The future of the DeepSeek series holds significant promise, with speculation suggesting that the upcoming R2 model may introduce a new architecture. This evolution could build on the strengths of V3 while addressing its current limitations. Anticipated updates and features are expected to further enhance the model's capabilities. Potential for a new architecture that improves reasoning and efficiency. that improves reasoning and efficiency. Focus on addressing current challenges , such as overthinking and logical inconsistencies. , such as overthinking and logical inconsistencies. Opportunities for enhanced customization and user control in future iterations. These developments underscore the ongoing innovation within the DeepSeek series and its commitment to advancing the boundaries of artificial intelligence. As the series evolves, it is poised to become an even more powerful tool for professionals across various industries. Media Credit: Prompt Engineering Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Forbes
5 days ago
- Business
- Forbes
3 Breakthrough Ways Data Is Powering The AI Reasoning Revolution
Olga Megorskaya is Founder & CEO of Toloka AI, a high quality data partner for all stages of AI development. The buzz around reasoning models like DeepSeek R1, OpenAI o1 and Grok 3 signals a turning point in AI development that pivots on reasoning. When we talk about reasoning, we mean that models can do more than repeat patterns—they think through problems step by step, consider multiple perspectives before giving a final answer and double-check their work. As reasoning skills improve, modern LLMs are pushing us closer to a future where AI agents can autonomously handle all sorts of tasks. AI agents will become useful enough for widespread use when they learn to truly reason, meaning they adapt to new challenges, generalize skills from one area to apply them in a new domain, navigate multiple environments and reliably produce correct answers and outputs. Behind these emerging skills, you'll find sophisticated datasets used for training and evaluating the models. The better the data, the stronger the reasoning skills. How is data shaping the next generation of reasoning models and agents? As a data partner to frontier labs, we've identified three ways that data drives AI reasoning right now: domain diversity and complexity, refined reasoning and robust evaluations. By building stronger reasoning skills in AI systems, these new approaches to data for training and testing will open a door to the widespread adoption of AI agents. Current models often train well in structured environments like math and coding, where answer verification is straightforward, fitting nicely into classical reinforcement learning frameworks. But the next leap requires pushing into more complex data across a wider knowledge spectrum. This is to achieve better generalization and performance as models transfer learning across areas. Beyond math and coding, here's the kind of data becoming essential for training the next wave of AI: These data points cover multi-step scenarios like web research trajectories with verification checkpoints. This includes open-ended domains such as law or business consulting that have multifaceted answers, which makes them difficult to verify but important for advanced reasoning. Think of complex legal issues with multiple valid approaches or comprehensive market assessments with validation criteria. Agent datasets are based on taxonomies of use cases, domains and categories as well as real-world tasks. For instance, a task for a corporate assistant agent would be to respond to a support request using simulated knowledge bases and company policies. Agents also need contexts and environments that simulate how they interact with specific software, data in a CRM or knowledge base or other infrastructure. These contexts are created manually for agent training and testing. The path a model takes to an answer is becoming as critical as the answer itself. As classical model training approaches are revisited, techniques like reward shaping (providing intermediate guidance) are vital. Current methods focus on guiding the process with feedback from human experts for better coherence, efficiency and safety: This focuses on a model's "thinking" rather than the outcome by guiding it through logical reasoning steps or guiding an agent through interactions with the environment. Think of it like checking step-by-step proofs in math, where human experts review each step and identify where a model makes a mistake instead of evaluating the final answer. Preference-based learning trains models to prioritize better reasoning paths. Experts review alternative paths and choose the best ones for models to learn from. This data can compare entire trajectories or individual steps in a process. These include data crafted from scratch to show high-quality reasoning sequences, much like teaching by example. Another approach is to edit LLM reasoning steps to improve them and let the model learn from the corrections. Current LLM evaluations have two main limitations: They struggle to provide meaningful signals of substantial improvements, and they are slow to adapt. The challenges mirror those in training data, including limited coverage of niche domains and specialized skills. To drive real progress, benchmarks need to specifically address the quality and safety of reasoning models and agents. Based on our own efforts, here's how to collaborate with clients on evaluations: Include a wider range of domains, specialized skill sets and more complex, real-world tasks. Move beyond single-metric evaluations to assess interdisciplinary and long-term challenges like forecasting. Use fine-grained, use-case-specific metrics. Co-develop these with subject-matter experts to add depth and capture nuances that standard benchmarks miss. As models develop advanced reasoning, safety evaluations must track the full chain of thought. For agents interacting with external tools or APIs, red teaming becomes critical. We recommend developing structured testing environments for red teamers and using the outcomes to generate new datasets focused on identified vulnerabilities. Even as model architectures advance, data remains the bedrock. In the era of reasoning models and agents, the emphasis has shifted decisively toward data quality, diversity and complexity. New approaches to data production are having a tremendous impact on the pace of AI development, urging reasoning models forward faster. With data providers upping their game to support the reasoning paradigm, we expect the near future to bring a wave of domain-specific, task-optimized reasoning agents—a new era of agentic AI. 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CNBC
5 days ago
- Business
- CNBC
China's DeepSeek quietly releases upgraded R1 AI model, ramping up competition with OpenAI
Chinese startup DeepSeek, which caused shockwaves across markets this year, quietly released an upgraded version of its artificial intelligence reasoning model. The company did not make an official announcement, but the upgrade of DeepSeek R1 was released on AI model repository Hugging Face. DeepSeek rose to prominence this year after its free, open-source R1 reasoning model outperformed offerings from rivals including Meta and OpenAI. The low-cost and short time of development shocked global markets, sparking concerns that U.S. tech giants were overspending on infrastructure and wiping billions of dollars of value of major U.S. tech stocks like AI stalwart Nvidia. These companies have since broadly recovered. Just as was the case with DeepSeek R1's debut, the upgraded model was also released with little fanfare. It is a reasoning model, which means the AI can execute more complicated tasks through a step-by-step logical thought process. The upgraded DeepSeek R1 model is just behind OpenAI's o4-mini and o3 reasoning models on LiveCodeBench, a site that benchmarks models against different metrics. DeepSeek has become the poster child of how Chinese artificial intelligence is still developing despite U.S. attempts to restrict the country's access to chips and other technology. This month, Chinese technology giants Baidu and Tencent revealed how they were making their AI models more efficient to deal with U.S. semiconductor export curbs. Jensen Huang, CEO of Nvidia, which designs the graphics processing units required to train huge AI models, slammed U.S. export controls on Wednesday. "The U.S. has based its policy on the assumption that China cannot make AI chips," Huang said. "That assumption was always questionable, and now it's clearly wrong." "The question is not whether China will have AI," Huang added. "It already does."
Yahoo
5 days ago
- Business
- Yahoo
DeepSeek Unveils Update to R1 Model
(Bloomberg) -- DeepSeek said it has upgraded the R1 artificial intelligence model that helped propel the Chinese startup to global prominence at the start of this year. NYC Congestion Toll Brings In $216 Million in First Four Months NY Wins Order Against US Funding Freeze in Congestion Fight NY Congestion Pricing Is Likely to Stay Until Year End During Court Case The company completed what it described as a 'minor trial upgrade' and is allowing users to start testing it, it said in an official WeChat group on Wednesday. Details of the upgrade weren't provided and the company didn't respond to an email seeking further comment. The Hangzhou-based startup stunned the global tech industry in January when it unveiled the original R1, a reasoning AI model that outperformed Western players on several standardized metrics, purportedly at a cost of just several million dollars. It triggered a reconsideration of heavy investments in acquiring AI computational resources and a flurry of new model introductions from Chinese players from Alibaba Group Holding Ltd. to Zhipu AI. 'The fast pace of model releases and updates since the release of DeepSeek R1 has resulted in some 'model fatigue' among investors,' said Gary Tan, portfolio manager at Allspring Global Investments. 'Until there is a breakthrough in the model, investors are turning their focus on which internet companies can integrate AI into their operations and create a killer application.' The debut of R1 turned DeepSeek founder Liang Wenfeng into a tech celebrity and a symbol of China's ability to compete with the best of Silicon Valley. In February, President Xi Jinping invited Liang to a high-profile gathering with some of the country's most prominent entrepreneurs. The young founder was seated among the likes of Alibaba co-founder Jack Ma and Tencent Holdings Ltd.'s Pony Ma. DeepSeek's upgrade was announced hours before the latest financial report from Nvidia Corp., the leading maker of AI chips whose shares were pummeled in the immediate wake of R1's release. Nvidia's fortunes have recovered since, as AI data center investment has continued at a strong pace, and the US company offered a solid forecast for the current quarter. --With assistance from Jessica Sui, Winnie Hsu and Saritha Rai. (Updates with table compiling most recent Chinese AI model releases) Mark Zuckerberg Loves MAGA Now. Will MAGA Ever Love Him Back? Millions of Americans Are Obsessed With This Japanese Barbecue Sauce YouTube Is Swallowing TV Whole, and It's Coming for the Sitcom Inside the First Stargate AI Data Center How Coach Handbags Became a Gen Z Status Symbol ©2025 Bloomberg L.P.
