Beyond Deep Associative learning

超越深度联想学习

• 批准号: RGPIN-2019-04824
• 负责人: Ghodsi, Ali
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737885
• 关键词: Beyond; Deep; Associative; learning

Mathematicians and scientists have been pursuing Artificial Intelligence (AI) for over sixty years. A significant discipline within AI is machine learning, where a system is trained through exposure to enormous amounts of data to learn new situations. Applications of machine learning are now commonplace - think of personalized searches on Google or Amazon, voice recognition systems, or digital assistants such as Cortana and Siri. These applications are associational - they look for consistent relationships between variables. While they are impressive, they still lack a human's ability to use prior knowledge and intuition and the ability to examine a new situation and imagine alternative outcomes. Further development of AI could plateau without a breakthrough into more human-like learning. The goal of this proposal is to begin the process of training a machine to learn as a human child learns, thereby significantly advancing the field of AI. Humans learn through repeated exposure to similar but slightly different events. For a machine, we believe the same result can be accomplished through a new generation of deep neural networks - where a machine can be trained through repeated exposure to similar but somewhat different data sets to learn and anticipate outcomes. My team and I will work towards this goal and attempt to move AI beyond associational models and towards human-like reasoning. There are three specific ingredients which will be the focus of our research: first, we will work to train a machine with deep learning of basic physics and see if it can apply this training across a wide variety of scenarios, approaching what would be common sense in a human; second, we will attempt to train a machine in cause-and-effect relationships which should allow it to predict alternative outcomes, the way a human considers counterfactuality - the innate human ability to imagine an infinite number of `what if?' scenarios; third, we will explore the possibilities of conditioning the machine to use its training outside of specific scenarios into completely new fields, the way a human can use experience and imagination when encountering new situations. There are many areas of work associated with this project and we will use two applications to illustrate the motivation driving this research and to validate our algorithms. These two applications are of significant importance by themselves and address a challenging industrial problem (autonomous vehicles) and an important and very influential bioinformatic open question (peptide sequencing).

六十多年来，数学家和科学家一直在追求人工智能 (AI)。人工智能中的一个重要学科是机器学习，系统通过接触大量数据来训练以学习新情况。机器学习的应用现在很常见 - 想想谷歌或亚马逊上的个性化搜索、语音识别系统或 Cortana 和 Siri 等数字助理。这些应用程序是关联的——它们寻找变量之间一致的关系。虽然它们令人印象深刻，但它们仍然缺乏人类使用先验知识和直觉的能力，以及检查新情况和想象替代结果的能力。如果没有在更像人类的学习方面取得突破，人工智能的进一步发展可能会陷入停滞状态。该提案的目标是开始训练机器像人类孩子一样学习，从而显着推进人工智能领域的发展。人类通过反复接触相似但略有不同的事件来学习。对于机器来说，我们相信通过新一代深度神经网络可以实现相同的结果——可以通过重复接触相似但有些不同的数据集来训练机器来学习和预测结果。我和我的团队将努力实现这一目标，并尝试让人工智能超越关联模型，走向类人推理。我们研究的重点包括三个具体内容：首先，我们将努力训练一台具有基础物理深度学习能力的机器，看看它是否可以将这种训练应用于各种场景，接近人类的常识；其次，我们将尝试训练机器的因果关系，使其能够预测替代结果，就像人类考虑反事实的方式一样——人类与生俱来的想象无限数量“假设”的能力。场景；第三，我们将探索使机器在特定场景之外使用其训练进入全新领域的可能性，就像人类在遇到新情况时运用经验和想象力的方式一样。 该项目涉及许多工作领域，我们将使用两个应用程序来说明推动这项研究的动机并验证我们的算法。这两个应用本身就非常重要，它们解决了一个具有挑战性的工业问题（自动驾驶汽车）和一个重要且非常有影响力的生物信息学开放问题（肽测序）。