Machine Learning for Tomorrow: Efficient, Flexible, Robust and Automated

人工智能系统最近在计算机视觉、语音和自然语言处理以及游戏等下游领域的最先进技术方面取得了重大进展。虽然这些进步令人印象深刻，但这些进步掩盖了底层机器学习技术的一系列根本局限性，需要解决这些局限性，以在与工业和社会相关的各种应用中释放收益。这些限制主要有四种形式。首先，当前的方法数据效率低下，需要极其庞大且精心策划的数据集。其次，他们不灵活地解决随着时间推移而固定的单一任务。第三，当前的方法很脆弱，因为在面对噪声、丢失数据或相反选择的数据点时，性能可能会灾难性地下降。第四，这些方法只是半自动的，需要专家来设计和调整。这些限制意味着许多重要的应用程序领域目前都遥不可及。例如，在医学中，我们通常只有小而有噪声的数据集，这需要数据高效和健壮的机器学习。将机器学习作为一种服务提供需要完全自动化的机器学习。这一繁荣伙伴关系将利用剑桥大学机器学习小组最近开发的技术和微软剑桥研究院的深厚专业知识，开发数据高效、健壮、灵活和自动化的机器学习。这一合作伙伴关系确定了具有影响力的应用领域的独特试验台：健康、企业工具和游戏开发。这一研究计划是实现微软愿景的核心，微软的愿景是让每个开发人员、组织和个人都能够用人工智能创新和改变世界。此外，这一领域具有直接和广泛的国家重要性，并提供了通过与世界上最大的技术公司之一合作产生影响的途径。

Artificial intelligence systems have recently led to significant advances in the state-of-the-art in downstream fields including computer vision, speech and natural language processing, and game playing. Although impressive, these advances mask a set of fundamental limitations of the underlying machine learning technology that need to be addressed to unlock gains in a wide variety of applications relevant to industry and society. These limitations come in four main forms. First current approaches are data-inefficient requiring extremely large and painstakingly curated datasets. Second, they are inflexible solving single tasks that are fixed through time. Third, the current approaches are brittle as performance can degrade catastrophically in the face of noise, missing data or adversarially selected data points. Fourth, the approaches are only semi-automated requiring an expert to design and tune them. These limitations mean that many important application domains are currently out of reach. For example, in medicine we typically have only small and noisy datasets which requires data-efficient and robust machine learning. Providing machine learning as a service requires fully-automated machine learning. This Prosperity Partnership will develop machine learning that is data-efficient, robust, flexible and automated by leveraging recently developed technology from the University of Cambridge's Machine Learning Group and deep expertise from Microsoft Research Cambridge. This partnership has identified a unique testbed of impactful application domains: health, enterprise tools and games development. This research programme is central to realising Microsoft's vision to empower every developer, organization and individual to innovate and transform the world with AI. Moreover, this area of immediate and wide-ranging national importance, and provides pathways to impact by partnering with one of the world's largest technology companies.