Building the next generation of intelligent machines with cooperative deep learning.

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The progressive and pervasive affirmation of modern machine learning techniques such as deep learning has contributed to achieving unprecedented performance in several fields, including computer vision, natural language processing, and speech processing, to name a few. Most of the existing deep learning systems, however, solve one or very few problems only. This "isolated" intelligence paradigm prevents current technology from deeply modeling the complex world around us. The human brain, instead, shows that cooperation across specialized modules (aka "lobes") is essential to solving high-level and abstract problems. This research program proposes to study how artificial neural networks solving different tasks can cooperate to address challenging problems. Rather than focusing on improving neural modules independently, this research will mainly investigate how neural networks interact and communicate. The goal is to expand the functionalities of current technology by introducing a novel cooperative deep learning paradigm, where neural modules are not anymore implementing "isolated" intelligence but are arranged in a network where they progressively learn how to cooperate, communicate, and interact. A natural application scenario for this research is conversational AI. The development of machines able to effectively converse with humans is indeed a challenging problem that requires combining complex technologies, such as keyword spotting, speech enhancement, speech recognition, spoken language understanding, and dialog systems, to name a few. The proposed plan will accomplish this goal gradually by adding complexity progressively. The research will start with a small number of modules (e.g., 2 or 3 neural networks). The team will later scale the paradigm up to more complex solutions. This research will be conducted in an open, transparent, and reproducible way to maximize the benefits for the community. A convenient open-source toolkit for this research is SpeechBrain, whose development is conducted by a large international network led by the principal applicant. The potential outcome of this discovery grant program is the definition of a novel cooperative deep learning paradigm. This approach can eventually enable the development of networks of artificial intelligence agents where each member of the neural ecosystems contributes to solving challenging problems. Humanity experienced a revolution when computers were connected. We can achieve another breakthrough by interconnecting more intelligent machines such as neural networks. This program offers the exciting opportunity to train 4 Highly Qualified Personnel (HQP) (2 PhDs and 2 Master students) and 4 undergrad trainees. The HQPs will develop expertise in deep learning and conversational AI, which are competencies highly demanded in the job markets. Training HQP in this field is of paramount importance for the ambitious AI development plan of Canada.