【導(dǎo)讀】thefieldadvances.August1,20xx,

　　

【正文】 enge in Education, which has seen considerable progress in the same period. Though quality education will always require active engagement by human teachers, AI promises to enhance education at all levels, especially by providing personalization at scale. Interactive machine tutors are now being matched to students for teaching science, math, language, and other disciplines. Natural Language Processing, machine learning, and crowdsourcing have boosted online learning and enabled teachers in higher education to multiply the size of their classrooms while addressing individual students‘ learning needs and styles. Over the next fifteen years in a typical North American city, the use of these technologies in the classroom and in the home is likely to expand significantly, provided they can be meaningfully integrated with facetoface learning. Beyond education, many opportunities exist for AI methods to assist Lowresource Communities by providing mitigations and solutions to a variety of social problems. Traditionally, funders have underinvested in AI research lacking mercial application. With targeted incentives and funding priorities, Society is now at a crucial juncture in determining how to deploy AIbased technologies in ways that promote rather than hinder democratic values such as freedom, equality, and transparency. 7 Longer term, AI may be thought of as a radically different mechanism for wealth creation in which everyone should be entitled to a portion of the world’s AIproduced treasures. 8 AI technologies could help address the needs of lowresource munities, and budding efforts are promising. Using data mining and machine learning, for example, AI has been used to create predictive models to help government agencies address issues such as prevention of lead poisoning in atrisk children and distribution of food efficiently. These budding efforts suggest more could be done, particularly if agencies and anizations can engage and build trust with these munities. Gaining public trust is also a challenge for AI use by Public Safety and Security professionals. North American cities and federal agencies have already begun to deploy AI technologies in border administration and law enforcement. By 2030, they will rely heavily upon them, including improved cameras and drones for surveillance, algorithms to detect financial fraud, and predictive policing. The latter raises the specter of innocent people being unjustifiably monitored, and care must be taken to avoid systematizing human bias and to protect civil liberties. Welldeployed AI prediction tools have the potential to provide new kinds of transparency about data and inferences, and may be applied to detect, remove, or reduce human bias, rather than reinforcing it. Social and political decisions are likewise at play in AI‘s influences on Employment and Workplace trends, such as the safety s needed to protect people from structural changes in the economy. AI is poised to replace people in certain kinds of jobs, such as in the driving of taxis and trucks. However, in many realms, AI will likely replace tasks rather than jobs in the near term, and will also create new kinds of jobs. But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will likely be lost. AI will also lower the cost of many goods and services, effectively making everyone better off. Longer term, AI may be thought of as a radically different mechanism for wealth creation in which everyone should be entitled to a portion of the world‘s AIproduced treasures. It is not too soon for social debate on how the economic fruits of AI technologies should be shared. Entertainment has been transformed by social works and other platforms for sharing and browsing blogs, videos, and photos, which rely on techniques actively developed in NLP, information retrieval, image processing, crowdsourcing, and machine learning. Some traditional sources of entertainment have also embraced AI to pose music, create stage performances, and even to generate 3D scenes from natural language text. The enthusiasm with which people have already responded to AIdriven entertainment has been surprising. As with many aspects of AI, there is ongoing debate about the extent to which the technology replaces or enhances sociability. AI will increasingly enable entertainment that is more interactive, personalized, and engaging. Research should be directed toward understanding how to leverage these attributes for individuals‘ and society‘s benefit. What’s next for AI research? The research that fuels the AI revolution has also seen rapid changes. Foremost among them is the maturation of machine learning, stimulated in part by the rise of the digital economy, which both provides and leverages large amounts of data. Other factors include the rise of cloud puting resources and consumer demand for widespread access to services such as speech recognition and navigation support. Machine learning has been propelled dramatically forward by impressive empirical successes of artificial neural works, which can now be trained with huge data sets and largescale puting. This approach has been e to be known as ―deep learning.‖ The leap in the performance of information processing algorithms has been acpanied by significant progress in hardware technology for basic operations such as sensing, perception, and object recognition. New platforms and markets for datadriven products, and the economic incentives to find new p