Beyond Deep Associative learning

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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).**