It has been three years since the establishment of Microsoft Research Asia "Innovation Exchange". From the very beginning, the concept of DTaaS (digital transformation as a service) was put forward, to the recent official release of Microsoft AI quantitative investment platform "micro-mine Qlib" and Microsoft multi-agent resource optimization platform "group strategy MARO", the platform road of DTaaS has achieved initial results.
The member companies of Innovation Exchange have now expanded to 27, including leading enterprises and innovative companies from finance, logistics, education, medical and health care, manufacturing, retail and other industries. The AI scientists of Microsoft Research Asia work closely with industry experts in various fields to stimulate their wisdom, promote enterprises to accelerate digital transformation, and help their business models keep pace with the times, and * * * have carried out many forward-looking AI cooperative research projects, which have landed in many industries.
Microsoft is a platform company. In the process of independent cooperation projects (which can be called "points"), our researchers are constantly abstracting the AI logic in the core business scenarios, excavating the internal essence of the problems, and gradually extending the innovative technological achievements to a wider range of industry fields (which can be called "areas"), and building these technologies into a universal platform to realize the closed loop of AI application in a certain industry field. Only by realizing the leap from "point" to "surface" can AI truly change all walks of life.
Huaxia Fund, a member of Innovation Exchange, and Microsoft Research Asia have cooperated in the field of quantitative investment-multi-factor stock selection since 217. Based on the strategy of "AI+ index enhancement", the two sides have excavated a portfolio with low correlation with traditional investment methods, thus realizing the differentiated competition of Huaxia Fund in the financial market.
In fact, in the whole process of stock investment, stock selection is only a small step. If you want to ensure the success of investment, you need to understand the relationship between the stocks that have opened positions, so as to control the risks, so as to avoid the problem of "putting eggs in one basket", just like buying stocks carefully and diversifying the investments of related enterprises. At the same time, constraints such as transaction cost and turnover rate need to be taken into account; When the optimal portfolio is formed, we should also consider the execution of orders and trading factors.
Based on this idea, Microsoft Research Asia has built a micro-mine Qlib 1, an AI quantitative investment platform, on the basis of previous research, hoping to realize the AI closed loop of quantitative investment process. As an open source toolkit, the platform can be used by financial institutions and individuals, so as to enhance investors' technical reserves and comprehensive level, improve the efficiency of the whole market, and thus form a larger virtuous circle in the investment field.
in the future, we will also consider expanding the open source platform in two directions-asset allocation and financial supervision. Large-scale asset allocation is an extension of stock investment. In addition to the secondary and primary stock markets, it can also help fund holders plan more investment portfolios from bonds, foreign exchange and even gold, so as to further share investment risks and ensure higher returns.
on the other hand, the business form of financial services industry is becoming more and more complex, and there are more and more institutions and individuals involved, and various operations are dazzling. For regulators, management is becoming more and more difficult. Looking for patterns, finding anomalies, monitoring risks and digging inside information in a complex environment is exactly what AI technology is good at. Therefore, in the process of communicating with partners, we also realize that AI can become a right-hand man in the field of financial supervision.
In the cooperation with OOCL, a member of Innovation Exchange, we have covered two main business scenarios of logistics industry, namely supply and demand forecasting and route optimization, and optimized the existing shipping network operation by using the latest artificial intelligence technologies such as deep learning and reinforcement learning. The cooperation with SF mainly focuses on intelligent claims early warning, link prediction and dynamic pricing, and explores the application value of AI in more links in the logistics field.
these two cases cover many basic scenarios of supply and demand matching in logistics chain, which are very representative, but they are still breakthroughs in "points". In fact, from the perspective of big logistics, besides container and truck scheduling, it also involves warehousing management, goods scheduling in warehouses, robot automatic sorting, and the relationship between warehousing and terminals, suppliers and retail terminals. All these sub-problems are integrated to form a complete logistics supply chain management platform.
among them, one of the most fundamental problems to be solved by the logistics industry is the matching of supply and demand. Therefore, aiming at the core engine of resource supply and demand matching, which can be applied to all walks of life, we have developed and opened the multi-agent resource optimization group policy MARO platform 2. Perhaps some enterprises have developed various IT systems to solve the sub-problems related to the matching of supply and demand of resources in the logistics chain, but our platform is the first in the industry that can be closely integrated with AI technology. Many business scenarios involving the matching of supply and demand of resources, such as the matching of bicycles and users in * * *, and the matching of tasks that need to be run in the data center with actual physical machines, can be solved by MARO platform.
It can be considered that MARO is a full-chain resource optimization AI solution for multi-industry cross-sections. Users only need to provide simple interfaces or data, and the platform will automatically generate an emulator, conduct intensive learning and training, and finally give industry solutions. The open-source MARO platform will not only be limited to the logistics industry, but also help more traditional enterprises to renovate resource matching tools, achieve resource optimization in a data-driven way, and greatly save costs.
similar to the construction and development of the general AI platform in the financial field, we hope to continuously enrich the general AI platform in the logistics field. In particular, for small and medium-sized logistics enterprises, they will be able to directly use the general AI platform in the logistics field, including the MARO platform, greatly shortening the process of building their AI intelligent business system and forming a late-comer advantage.
the universal AI platform in both financial and logistics fields is based on the application "point" that AI is best at. As an assistant of human intelligence, artificial intelligence can show the ability to surprise enterprise decision-makers when analyzing and solving complex problems only through short-term learning and debugging. When we find one core application "point" after another in different industries, we can gradually "open" every traditional industry from point to surface with AI.
at the same time, we also actively cooperate with Microsoft's product department to integrate more AI decisions into Microsoft's product system. In the future, AI will surely achieve closer integration with different industries and different scenarios, and lead every enterprise and industry to the AI era in an all-round way.
1 Microsoft AI quantitative investment platform-micro-mine QLIB:/Microsoft/QLIB
2 Microsoft multi-agent resource optimization platform-group strategy MARO:/Microsoft/MARO.