人工智能算法对专利保护政策的挑战及应对

随着以算法为核心的人工智能技术的发展,算法的应用领域在深度和广度两个方向均拓展迅猛,已经悄然影响我们的生活的方方面面。当技术深入到生产生活的实践中后,如何获得市场的合法垄断、对创新技术的保护成为迫切的需要,创新主体关于算法保护的需求已经体现在已经公开的专利申请中。本文梳理了关于算法的国内外专利保护政策,结合具体案例探讨了算法对专利法第25条提出的挑战,并基于专利法立法本意,从算法的本质特点和促进技术进步的需要的角度,探讨对于通用算法可尝试加强专利保护,提出了结合实际技术发展和经济现状,采取逐步放开的方式,从而适应算法的发展需求。

Digital Terrain Modelling Using Corona and ALOS PRISM Data to Investigate the Distal Part of Imja Glacier,Khumbu Himal,Nepal

This study used Corona KH-4A and Advanced Land Observing Satellite (ALOS) PRISM images to generate digital terrain models (DTMs) of the distal part of Imja Glacier,where a few supraglacial ponds (～0.07 km 2) expanded into the large Imja Glacier Lake (Imja Tsho,～0.91 km 2) between 1964 and 2006.DTMs and subsequently derived topographical maps with contour intervals of 1 m were created from the high-resolution images (Corona in 1964 and ALOS in 2006) in the Leica Photogrammetric Suite (LPS) platform.The DTMs and topographic maps provided excellent representation of the elevation and micro-topography of the glacier surface,such as its supra-glacial ponds/lake,surface depressions,and moraine ridges,with an error of about +/-4 m (maximum).The DTMs produced from the Corona and ALOS PRISM images are suitable for use in studies of the surface change of glaciers.The topographical maps produced from the Corona data (1964) showed that part of the dead ice in the down-glacier area was even higher than the top of the lateral moraine ridges,while the glacier surface in the up-glacier area was noticeably lower than the moraine crests.This suggests more extensive melting of glacier ice in the up-glacier area before 1964.The average lowering of the glacier surface from 1964 to 2006 was 16.9 m for the dead-ice area west of the lake and 47.4 m for the glacier surface east of the lake;between 1964 and 2002,the lake surface lowered by 82.3 m.These figures represent average lowering rates of 0.4,1.1,and 2.2 m/year for the respective areas.

A framework for multi-session RGBD SLAM in low dynamic workspace environment

Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, which can maintain a map keeping track of the latest environment. The main model describing the environment is a multi-session pose graph, which evolves over the multiple visits of the robot. The poses in the graph will be pruned when the 3D point scans corresponding to those poses are out of date. When the robot explores the new areas, its poses will be added to the graph. Thus the scans kept in the current graph will always give a map of the latest environment. The changes of the environment are detected by out-of-dated scans identification module through analyzing scans collected at different sessions. Besides, a redundant scans identification module is employed to further reduce the poses with redundant scans in order to keep the total number of poses in the graph with respect to the size of environment. In the experiments, the framework is first tuned and tested on data acquired by a Kinect from laboratory environment. Then the framework is applied to external dataset acquired by a Kinect II from a workspace of an industrial robot in another country, which is blind to the development phase, for further validation of the performance. After this two-step evaluation, the proposed framework is considered to be able to manage the map in date in dynamic or static environment with a noncumulative complexity and acceptable error level.

Multi-LeapMotion sensor based demonstration for robotic refine tabletop object manipulation task

In some complicated tabletop object manipulation task for robotic system, demonstration based control is an efficient way to enhance the stability of execution. In this paper, we use a new optical hand tracking sensor, LeapMotion, to perform a non-contact demonstration for robotic systems. A Multi-LeapMotion hand tracking system is developed. The setup of the two sensors is analyzed to gain a optimal way for efficiently use the informations from the two sensors. Meanwhile, the coordinate systems of the Mult-LeapMotion hand tracking device and the robotic demonstration system are developed. With the recognition to the element actions and the delay calibration, the fusion principles are developed to get the improved and corrected gesture recognition. The gesture recognition and scenario experiments are carried out, and indicate the improvement of the proposed Multi-LeapMotion hand tracking system in tabletop object manipulation task for robotic demonstration.

Physiological responses of people in working faces of deep underground mines

The research studied the influences of high temperature, high pressure, high humidity, noise and other harmful factors in mining conditions on the people health and safety, and investigated the impacts of confined environmental on human physiology factors, including temperature, humidity, noise, pressure,toxic and harmful gases in terms of environmental characteristics in underground mines and an artificial intelligence system for simulation of the environment in a confined space of deep mines. Our results show that the systolic pressure, diastolic pressure, mean pressure, heart rate, respiratory rate, typing test speed and memory level percentage are negatively correlated with temperature value, and positively correlated with humidity value; the human temperature and weight are positively correlated with temperature value, and negatively correlated with humidity value. This research lays the foundation for the study of interaction between the deep confined space environment and safety behavior.

Study and application of reinforcement learning based on DAI in cooperative strategy of robot soccer

A dynamic cooperation model of multi-agent is established by combining reinforcement learning with distributed artificial intelligence(DAI),in which the concept of individual optimization loses its meaning because of the dependence of repayment on each agent itself and the choice of other agents.Utilizing the idea of DAI,the intellectual unit of each robot and the change of task and environment,each agent can make decisions independently and finish various complicated tasks by communication and reciprocation between each other.The method is superior to other reinforcement learning methods commonly used in the multi-agent system.It can improve the convergence velocity of reinforcement learning,decrease requirements of computer memory,and enhance the capability of computing and logical ratiocinating for agent.The result of a simulated robot soccer match proves that the proposed cooperative strategy is valid.

Research on the Perceptual Law of Artificial Ants

Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, artificial ants have the ability to identify and remember through similar measurement of pheromone. Based on the quantity of experiments, this paper analyzes some factors that influence the ability of artificial ants and draws some conclusions about the law of ant perception.

An International Journalism Model of Professionalism in News Production： The Concepts and the Measurements

This study aimed to contribute in establishing an international journalism model of professionalism in the production of the news. The main purpose is to explore the degree to which this model predicts the professional values in the media content. In particular, this model was tested on the content of a leading news organization in the Middle East, AI Jazeera, to identify whether or not AI Jazeera reflected professional values in news production or other non-professional values. A total of 592 news stories--234 from AJE and 358 from AJA--published from January I, 2014, to April 30, 2014, were analyzed. The findings of this study indicate that AI Jazeera reflects professional values to a substantial degree. The professional values were reflected highly and nearly two thirds of the stories had professional values in the content. The chi square tests shows there are frequency/percentage differences, but overall the patterns are similar, with no statistically significant differences in the AJA and AlE. Scholarly implications, future studies and limitations were presented in this study.

“人工智能+教育”的困境根源及应对

以云计算、大数据和深度学习为核心驱动力的人工智能正在如火如荼地开展,它与教育的深度融合(“人工智能+教育”)正在不断变革教育理念、更新教育手段、丰富教育内容。人工智能在教育领域不断推进应用之时,围绕它的批判声也络绎不绝。究其根源而言,人工智能试图驰骋于整个教育疆域但却力不从心,人工智能与教育深度融合过程中出现的新问题使它步履维艰。以此为根,人工智能应用于教育领域而招致的批判具体表现为三个方面:“人工智能+教育”系统、工具的安全性能无法保障,教育数据隐私泄露,教师主导地位沉沦。基于此,可以制定统一的“人工智能+教育”系统、工具开发与设计标准,利用区块链技术保护教育数据隐私,划定人工智能在教育领域的应用边界,从而拨开“人工智能+教育”前行中的重重迷雾。

Artificial intelligence and statistics

Artificial intelligence(AI) is intrinsically data-driven.It calls for the application of statistical concepts through human-machine collaboration during the generation of data,the development of algorithms,and the evaluation of results.This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population,question of interest,representativeness of training data,and scrutiny of results(PQRS).The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches.These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results.We discuss the use of these principles in the contexts of self-driving cars,automated medical diagnoses,and examples from the authors' collaborative research.