你知道加拿大人工智能领域有哪些研究方向吗

1. ai: computational linguistics & nlp
research topics: natural language processing ﹙nlp﹚, speech processing, information retrieval, machine translation, language acquisition, formal perspectives on language, cognitive moelling of language acquisition an processing, semantic change, lexical evolution, lexical composition, cross-linguistic semantic typology, applications of nlp in health an meicine, applications of nlp in the social sciences an humanities
计算语言学与自然语言处理
研究主题：自然语言处理、语音处理、信息检索、机器翻译、语言习得、语言的形式视角、语言习得和处理的认知模型、语义变化、词汇演变、词汇构成、跨语言语义类型学、自然语言处理在健康和医学中的应用、自然语言处理在社会科学和人文学科中的应用
2. ai: computational social science
research topics: novel igital ata an computational analyses for aressing societal challenges, analysis of online social networks an social meia, intersection of ai an society, application of machine learning to social ata, analysis of large-scale online ata for social science applications, algorithmic fairness an bias
计算社会科学
研究主题:解决社会挑战的新型数字数据和计算分析、在线社交网络和社交媒体分析、人工智能和社会的交集、机器学习在社交数据中的应用、用于社会科学应用的大规模在线数据分析、算法的公平和偏见
3. ai: computer vision
research topics: tracking, object recognition, 3 reconstruction, physics-base moelling of shape an appearance, computational photography, content-base image retrieval an human motion analysis
计算机视觉
研究主题:跟踪、目标识别、三维重建、基于物理的形状和外观建模、计算摄影、基于内容的图像检索和人体运动分析
4. ai: knowlege representation an reasoning
research topics: knowlege representation, reasoning an inference, planning an ecision making, search, multi-agent systems, sequential ecision-making, cognitive robotics, reasoning about knowlege, belief, acting an sensing, constraint an satisfiability reasoning
知识表示与推理
研究课题：知识表示、推理与推理、规划与决策、搜索、多智能体系统、顺序决策、认知机器人、知识推理、信念、行为与感知、约束与满足性推理
5. ai: machine learning
research topics:
methos: eep learning, graphical moels, reinforcement learning, stochastic optimization, approximate inference, structure preiction, representation learning
theory: analysis of machine learning algorithms, convex an non-convex optimization methos, statistical learning theory
focus in health: eveloping an applying machine learning methos that leverage the structure of ata an problems in health incluing representation learning, reinforcement learning an inverse rl, preiction, risk stratification, an moel interpretability
focus in robotics: reinforcement learning, robot perception, learning an control, imitation learning, preictive moels, exploration, lifelong learning, learning for self-riving cars
focus in computer vision an graphics: image segmentation, etection
focus in systems: clou computing, operating systems, harware acceleration for machine learning
机器学习
方法:深度学习、图形模型、强化学习、随机优化、近似推理、结构化预测、表示学习
理论:机器学习算法分析，凸与非凸优化方法，统计学习理论
专注于健康:利用健康领域的数据结构和问题，包括表征学习、强化学习和逆rl、预测、风险分层和模型解释性，开发和应用机器学习方法，
专注于机器人:强化学习、机器人感知、学习与控制、模仿学习、预测模型、探索、终身学习、自动驾驶汽车学习
专注于计算机视觉和图形:图像分割，检测
专注于系统:云计算，操作系统，机器学习硬件加速
6. ai: robotics
research topics:
meical robotics, surgical robotics, continuum robotics, soft robots
robot manipulation, kino-ynamic moelling of robots, motion planning, optimal control
self-riving cars, mobile an fiel robotics, autonomous vehicles
human-robot interaction, multi-agent systems
机器人
医疗机器人，手术机器人，连续体机器人，软机器人
机器人操作，机器人kino-动力学建模，运动规划，最优控制
自动驾驶汽车，移动和野外机器人，自动驾驶汽车
人机交互，多智能体系统
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