Federated AI lets a team imagine together: Federated Learning of GANs
This addresses the challenge of collaborative creativity for teams separated by time and space, presenting a new AI paradigm rather than an incremental improvement.
The paper tackles the problem of enabling geographically distributed teams to collaboratively envision new ideas by proposing a Federated AI Imagination paradigm that combines Federated Learning with Generative Adversarial Networks (GANs), resulting in a system that allows teams to synergize interests and imagine together.
Envisioning a new imaginative idea together is a popular human need. Imagining together as a team can often lead to breakthrough ideas, but the collaboration effort can also be challenging, especially when the team members are separated by time and space. What if there is a AI that can assist the team to collaboratively envision new ideas?. Is it possible to develop a working model of such an AI? This paper aims to design such an intelligence. This paper proposes a approach to design a creative and collaborative intelligence by employing a form of distributed machine learning approach called Federated Learning along with fusion on Generative Adversarial Networks, GAN. This collaborative creative AI presents a new paradigm in AI, one that lets a team of two or more to come together to imagine and envision ideas that synergies well with interests of all members of the team. In short, this paper explores the design of a novel type of AI paradigm, called Federated AI Imagination, one that lets geographically distributed teams to collaboratively imagine.