DeepSeek-R1, renowned for its exceptional reasoning capabilities and open-source strategy, is significantly influencing the global artificial intelligence landscape. However, it exhibits notable safety shortcomings. Recent research conducted by Robust Intelligence, a subsidiary of Cisco, in collaboration with the University of Pennsylvania, revealed that DeepSeek-R1 achieves a 100\% attack success rate when processing harmful prompts. Furthermore, multiple security firms and research institutions have identified critical security vulnerabilities within the model. Although China Unicom has uncovered safety vulnerabilities of R1 in Chinese contexts, the safety capabilities of the remaining distilled models in the R1 series have not yet been comprehensively evaluated. To address this gap, this study utilizes the comprehensive Chinese safety benchmark CHiSafetyBench to conduct an in-depth safety evaluation of the DeepSeek-R1 series distilled models. The objective is to assess the safety capabilities of these models in Chinese contexts both before and after distillation, and to further elucidate the adverse effects of distillation on model safety. Building on these findings, we implement targeted safety enhancements for six distilled models. Evaluation results indicate that the enhanced models achieve significant improvements in safety while maintaining reasoning capabilities without notable degradation. We open-source the safety-enhanced models at https://github.com/UnicomAI/DeepSeek-R1-Distill-Safe/tree/main to serve as a valuable resource for future research and optimization of DeepSeek models.

CryptoGPT: a 7B model competing with GPT-4 in a specific task -- The Impact of Automatic Annotation and Strategic Fine-Tuning via QLoRAIn this article, we present a method aimed at refining a dedicated LLM of reasonable quality with limited resources in an industrial setting via CryptoGPT. It is an LLM designed for financial news analysis for the cryptocurrency market in real-time. This project was launched in an industrial context. This model allows not only for the classification of financial information but also for providing comprehensive analysis. We refined different LLMs of the same size such as Mistral-7B and LLama-7B using semi-automatic annotation and compared them with various LLMs such as GPT-3.5 and GPT-4. Our goal is to find a balance among several needs: 1. Protecting data (by avoiding their transfer to external servers), 2. Limiting annotation cost and time, 3. Controlling the model's size (to manage deployment costs), and 4. Maintaining better analysis quality.

In the past years, the need for data entry services significantly grew because of its significance as one of the foundations of data and analytics infrastructure. But with the continuous evolution of technology which leads to the disruption in the said fields, are data entry services still necessary in today's generation? Many people, particularly businessmen, have already see how it can make their lives easier, simplify processes, and improve our way of life. Others, however, remain resistant to the change brought by this smart technology. Their pessimism often boils down to their fear that it might replace manual and generic human jobs -- those that require more routine skill set -- and steal away their source of income.

As we have explained on multiple occasions, AI has and will have an impact on many industries. Of course, with this development, the question that all the people working in those industries is the same: "what will happen to my job?" Have you ever ask yourself this question: How will AI affect the demand for human Labor. Do you think AI will decrease human labor? So if foreseeable technologies materialize, then then the need for human labor could decrease. Technology always puts existing jobs under strain.

Humanoid, facially expressive robots have been designed by researchers to help medical professionals improve their diagnosing skills. While robotic patient simulators (RPS's) are already used to train doctors, their faces don't move and don't express emotions. So researchers created a robot with rubber skin that can move its facial features to express real human emotions. From left to right pain, anger and disgust. The research team, led by Dr Laurel Riek, an associate professor of computer science and engineering at UC San Diego, designed the robot to be able to express pain, disgust and anger.

Do you think AI will decrease human labor? So if foreseeable technologies materialize, then then the need for human labor could decrease. Technology always puts existing jobs under strain. This doesn't immediately mean that human labor as a whole is under threat. Generally, other professions grow to fill the loss, often creating more jobs than the ones that are lost.

Do you think AI will decrease human labor? So if foreseeable technologies materialize, then then the need for human labor could decrease. Technology always puts existing jobs under strain. This doesn't immediately mean that human labor as a whole is under threat. Generally, other professions grow to fill the loss, often creating more jobs than the ones that are lost.