From November 7th of 2019, Artificial intelligence and artistic creation exploration exhibition was held in Shanghai Ming Contemporary Art Museum (McaM). A lot of experts and the artistic pioneers came here to present an artificial intelligence art visual feast for the audience by the artistic works, exploring the infinite possibilities of “AI + Artistic”.
Unlike the usual exhibits, this art exhibition also has one special exhibit. The audiences not only can see it, but they also can play it. That is the visualization of AI games based on federated learning (Federated Learning Demo). Federated Learning Demo can enable the audiences to participate in the process of AI modeling, using the robot trained by ourselves to fight with the game robot. With interactivity and enjoyment, teaching in fun make the understanding of AI to be easy and interesting.
The federated learning demo is a semi-interactive racing game. The audiences can participant in the game so that they can understand the technical theory and advantage of federated learning AI visually. As we all know, in the racing game, the traditional gameplay is trying, again and again, to get more familiar with the map route in order to achieve the maximum speed. Due to the personal energy and the limited opportunities (perhaps limited to the talent), the players can’t make ensure to win after several times of training. Playing on solo, the traditional way is a kind of the AI model relying on the training by a single player. This AI model’s performance is general because the model just learns a little data from one player. However, in this process, federated learning technology can bypass the players’ data and directly combine the AI models trained by the data of every player, train and improve the AI model of federated learning constantly, and become the final winner. In other words, no matter how the single-player good is, he/she can only fight alone, while federated learning can win with multiple parties.
Above: Training process of AI
Below: Player interface
It was reported that the Shanghai art exhibition isn’t the first appearance for federated learning demo. As the first gamification semi-interactive demo of federated learning in China, in August it was also exhibited at the International Joint Conferences on Artificial Intelligence (IJCAI 2019) held in Macao. From the academic conference to the art exhibition, federated learning demo not only can be participated and visible but also it is gamification, which can enable audiences to join into the operation of federated learning technology. Providing a new path for AI to appear in public, federated learning demo also lower the threshold of AI perception for non-technical people.
The plenty of data used for model training during the game also reminds people of the data privacy and security problems behind the game. If we don’t share the data, that would be data silos. It is hard to train an effective AI model like practicing alone. Who can protect user privacy and security if we share data? Federated learning technology is a good demonstration – sharing the data, not the underlying data, which can enable data to never leave local storage and jointly modeling so as to improve the performance of machine learning. Thus, it also protects the data privacy and security to provide a new idea about solving the contradiction of data regulation, data soils and the development of AI.
As a pioneer and promoter in the development of Federated Learning technology in China, WeBank keeps promoting the application implementation of federated learning in different industries. For example, in the computer vision industry, WeBank and Extreme Vision jointly launch the “federated visual system”, helping the company expand the data application scope to share the success of data models. Meanwhile, you don’t need to concern the problem of data security. Except for the computer vision field, federated learning technology has also applied in finance, medicine, city management and other fields to help the intelligence upgrade of industries.
It can be predicted that in the future, the protection of data security and data privacy will be more concerned. Federated learning will apply in more industries and scenarios, accelerating the arrival of the intelligent future. In the path to the intelligent future, we not only need hard technology like federated learning, but we should also own an interesting tool, like the federated learning demo to help us understand the new technology easily. Only in this way can everyone participate and benefit from the wisdom of the future. As the demo creator Quan Li and Xiguang Wei said, “Whether it’s technology or the tools to explain it, it has to focus on human so that people can live a better life at a lower cost.”