How can you transform your productivity with the help of AI tools? On September 27th, the CCF CED 2024 China Computer Federation Engineer Culture Day "AI Tools for Productivity Innovation" sub-forum will take you to explore.
Date: 2024.09.27 ~ 09.28
Venue: Building 7, Area B, Xixi, Alibaba Digital Ecological Innovation Park, Yuhang District, Hangzhou City, Zhejiang Province
There are only two weeks left until the CCF CED 2024 China Computer Federation Engineer Culture Day, and the speakers are intensively optimizing the content of the speeches and rehearsing all aspects, hoping to provide participants with a wonderful event with an in-depth discussion of engineer culture in the AI era. In addition to the highly anticipated CED celebrity talk about industry, celebrity talk about growth, celebrity talk show and other classic links, this year also added five sub-forums, focusing on technology leadership in the AI era, engineer team management in the AI era, AI tools for productivity innovation, creating your own AI agent, product innovation and other hot directions, making the event more diverse and comprehensive. Today, let's take a sneak peek at the highlights of the "AI Tools for Productivity Innovation" sub-forum, and jointly foresee how technology will reshape the new ecosystem of productivity and creativity.
Introduction to the sub-forum
AI is reshaping industries, and in recent years, many AI tools have emerged that bring productivity updates.
The "AI Tools for Productivity Innovation" sub-forum brought together top experts in the industry, together with elites from many well-known enterprises such as Baidu, SenseTime, Weibo, Amazon Web Services, and Ant, to explore the application of AI in production tools and team collaboration. The emergence of large models is profoundly changing the workflow of software development, from office to documentation, from architecture to development, from testing to security, every link contains the power of innovation.
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Sub-forum agenda
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Chair of the Forum
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Zang Zhi
General Manager of Baidu Comate
The Chair of the Forum said:
Here, you'll see how AI is reshaping the R&D process, improving R&D efficiency, and bringing happiness to engineers. Experts will bring cutting-edge application cases, share valuable experience and forward-looking perspectives, and build a platform for engineers and business managers to embrace the AI-native era.
Specifically, Baidu's experts will share the paradigm change of software R&D driven by large models, demonstrate innovative thinking and practical results in intelligent R&D, and explore the new paradigm of human-machine collaboration. Experts from SenseTime focused on the efficiency of the large model to help the team, analyzed the origin of its ability characteristics on productivity innovation, and looked forward to the broad prospects of the implementation of large model products. Experts from Sina Weibo will use Wecode as an example to explain how to build an efficient code-assisted generation assistant with the help of open source models to improve R&D efficiency. Experts from Amazon Web Services will provide an in-depth explanation of how generative AI can empower all aspects of cloud development, providing developers with new ideas and solutions. Experts from Ant Digital Technology will share their experience in the application of large models in the field of smart contract code security to improve the performance of contract security tools.
It's not just a feast for technology, it's a historic moment to see an AI-driven leap in productivity. Let's join this event to explore the infinite possibilities of AI and usher in a new era of engineering productivity!
Share the guests
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Li Hui
Chief Architect of Baidu's Engineering Performance Department
Chief Architect and TC Chairman of Baidu Engineering Performance Department, Secretary General of Baidu CMC and DevOps TOC. Graduated from Beijing University of Posts and Telecommunications, majoring in information science, with 20 years of experience in engineering architecture and application, he is a senior expert in the field of DevOps, the overall technical leader of Baidu's DevOps R&D tool product line, and the core initiator of Baidu's engineering capability map, engineering efficiency evaluation system, and AI native R&D paradigm. He is now focusing on the design and construction of AI R&D tools, and deeply explores the changes and evolution trends of human-machine collaboration in software R&D in the AI era.
Application Practice of R&D Tools and Human-Machine Collaborative Exploration Driven by Large Models
With the rapid development of generative AI and large model applications, all walks of life are actively embracing AI technology. As an important field of large-scale model application, software research and development is also regarded as an epoch-making productivity tool. This sharing will introduce the changes experienced by the software R&D paradigm driven by large model technology, combined with Baidu's thinking and in-depth practice in intelligent R&D, fully excavate and release the productivity of large models, and improve the efficiency of R&D teams and engineers.
We will explain in simple terms how big model technology has revolutionized the field of software engineering, and share how AI technology is reshaping existing DevOps processes and tools. Combined with Baidu's in-depth practice and exploration, we will share Baidu's valuable experience in the construction and application of intelligent R&D tools.
The industry is still exploring the new paradigm of human-machine collaboration in the AI era. This sharing will also introduce Baidu's latest exploration and implementation results in this field, and illustrate how to use these productivity tools more effectively to stimulate creativity and accelerate innovation through real-life scenarios.
Zhang Tao
Head of Copilot Application Technology at SenseTime
Graduated from Beijing University of Posts and Telecommunications, majoring in communication engineering, he has 16 years of experience in advanced technology engineering application. He has led the design and development of large-scale software architectures in the fields of video communication, computer vision, CV heterogeneous inference framework, and large language model applications. At present, Zhang Tao focuses on the research and development of AI application products, and leads the team to develop and launch the SenseTime AI Native productivity series tools "Code Raccoon" and "Office Raccoon", providing programming assistance services for hundreds of thousands of developers.
"Cornerstone, Path, Far Mountain: Technical Exploration of Large Models to Help Teams Improve Efficiency"
Different from previous technological innovations, the emergence of large-scale intelligence has opened up the boundaries of human imagination, and many ideas that were previously unrealizable due to the limitations of machine intelligence have begun to falter.
The improvement of social productivity is the main driving force for the progress of human civilization. The rapid development of large models and generative AI technologies has led many early movers to focus on the potential of AI to improve productivity. The main contents of this sharing are:
- The cornerstone - the ability trait of the large model is the source of productivity innovation
- Path – the way in which large models play a role in R&D and data analysis scenarios
- Yuanshan - the current limitations in the landing of large-scale model products, as well as prospects
I will use the above content to disassemble the technical principles and product practice experience, and discuss with you how the current large model can help improve productivity; At the same time, we will imagine how strong AI will reshape our world in the future.
Li Qingfeng
Senior Director of Sina Weibo R&D Center
As the Senior Director of Weibo R&D Center, he is responsible for the research and development of Weibo infrastructure and related technology platforms such as streaming media. He has rich experience in R&D and management in the fields of high-availability architecture, cloud-native, hybrid cloud architecture and streaming media architecture, and actively promotes the practice and exploration of AI technology to improve efficiency. As an active advocate of the technology community, he has served as a lecturer and producer at several cutting-edge technology conferences in the industry.
"AI Helps R&D Improve Efficiency-Wecode Code Assistant Architecture Practice"
Large models and AIGC technology are profoundly changing various industries, and AI-assisted code generation to improve R&D efficiency has also become an important exploration direction in the industry.
This sharing will take Sina Weibo's code assistant Wecode as an example to introduce how to use open source large models to quickly and securely build a code-assisted generation assistant suitable for your team. Through appropriate architecture design, it can achieve rapid iterative optimization, improve the code assistance effect, and help the team improve R&D efficiency.
I hope that through this case sharing, everyone can better understand that in the era of large models, the way of research and development has gradually changed; AI will play an increasingly important role in R&D efficiency. The era of large models has brought new challenges and opportunities to R&D personnel, and we must embrace AI and be at the forefront of large model technology.
Zheng Yubin
Senior Developer Evangelist for Amazon Web Services
Master of Software Engineering, with more than 20 years of practical experience in the ICT industry and digital transformation. Currently, he is a senior developer evangelist at Amazon Web Services, focusing on cloud native, DevOps, and generative AI market content creation and promotion. As the first female technology evangelist focused on developers at Amazon Web Services, she is very active in the global Chinese developer community. With 18 years of experience as an architect, she focuses on consulting and technology implementation of solutions for data center construction and software-defined data centers for financial, educational, manufacturing, and Fortune 500 enterprise clients. She brings her extensive industry experience to provide technical coaching to developers seeking mutual success.
Generative AI Empowers the Whole Process of Cloud Development
We'll take a deep dive into how generative AI can be used to elevate every aspect of cloud development. With the popularity of cloud computing, developers are faced with the dual challenges of efficiency and innovation. Generative AI offers revolutionary solutions for code generation, test automation, performance optimization, and even troubleshooting.
The presentation will take Amazon Web Services as an example to show how generative AI-powered tools and services can be used on the cloud platform to achieve intelligent code completion and error detection, intelligent generation of documents and code comments, thereby helping developers reduce work time. At the same time, the presentation will also explore the role of AI in automated testing, showing how to improve the reliability of software by generating virtual test environments and automated test cases.
In addition, the presentation will cover how to use generative AI for performance optimization, by analyzing large amounts of log data, identifying system bottlenecks and providing optimization recommendations. Develop new apps and add new features with natural language. These technologies not only improve development efficiency, but also help developers focus on innovative and more strategic tasks.
Through practical cases and demonstrations, we hope to inspire developers to apply generative AI in their own projects to achieve a more intelligent and efficient cloud development process. Get ready for a new era of AI-driven development!
Zhou Wei
Head of Contract Security at Ant Digital Technology
The head of smart contract security and virtual machine of Ant Digital Technology has long been committed to the research of the underlying technology of smart contracts, and is an expert in the field of smart contract language compilers, virtual machines and contract security. Since joining AntChain, he has participated in and led the development of blockchain contract language compiler, just-in-time compilation (JIT) virtual machine and AI contract audit platform as a core member. He led the team to apply large models to a variety of smart contract audit tools, and developed an AI contract audit platform that not only serves internal users, but also supports dozens of external customers. He continues to explore the application of AI technology in improving the capability boundaries of security tools, assisting manual audits, and improving audit efficiency, and is committed to applying large model technology to the development and practice of professional tools.
Exploration of Large Models to Enhance the Capabilities of Contract Security Tools
In the field of smart contract security assessment and code review, techniques such as static analysis and formal verification are often used to assist manual audits. However, these methods have certain limitations in practical application, such as high threshold for use, overly generalized and lack of specificity in vulnerability explanation, and insufficient performance in analyzing complex logic vulnerabilities.
I will share how to incorporate the innovative technology of large models into the tools in the field of smart contract code security to improve their performance. I will introduce in detail the application and implementation of large models in traditional security tools, as well as the experience of training large models for contract auditing. It is hoped that relevant practitioners will be provided with practical experience and cases of the application of large models in highly complex technical tools.
The complete schedule of the conference can be accessed by scanning the QR code.