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.
Current state of art (2017 to May 2019) AI learns using a set of users FL by Google [1]
A set of users use AI to Imagine together
Is it possible for a team to use AI to imagine ideas. This paper explore this new paradigm, and demonstrates it is feasible. But what if the goal of AI is help a team of users to jointly imagine together? Is this possible for AI to do?.
The main contribution of this paper is to add a new flavor to Federated Learning (FL) research, as illustrated in Table 1. By exploring a new paradigm of an AI that enables collective human imagination, a new wave of possibilities are let open. This paper combines Federated Learning with Generative Adversarial Networks (GAN) [3]. • Figure 3 illustrates how multiple city security cameras federate and imagine together to improve security of the city.
• Figure 8 explores how cause and effect relations across geo distributed events.
What is the secret formula behind this AI paradigm? How to realize the possibilities of Table 3. A novel Deep Learning architecture to accomplish this is contributed by this paper. The idea behind this architecture is to combine the power of FL and Generative Adversarial Networks (GAN) multimodal fusion. This paper designs the deep learning architecture for the above AI paradigms and develops a working model. This novel neural network architecture is presented in Figure 3 and Figure 7.
The concept of Federated AI Imagination is illustrated in Figure 3 with a use case. How to design a city scale AI architecture that allows a collective team to perform a team activity. The challenge here is AI for counter-terrorism
At the intersection of two promising active research areas, FL & GAN based Neural Network Fusion approaches, is the discovery of the promise of AI that powers Collective Thinking by a team. Architecture for combine GAN Fusion and FL is discussed in Figure 3. An implementation of GAN Fusion on is shown in Figure 5. As per Figure 5, key tricks are both Generators are sampled from the same noise vector, Generator #1 is frozen, so that Generator #2 learns during back propagation. Discriminator uses a new type of dataset, which has possible combinations of the 2 images. The working prototype is presented in Figure 4.
. This allows G2 to learn, given G1 is non-trainable now. 5. After learning, G2 will be able to imagine possible weapons that would have been carried in that bag. 6. This intelligent imagination patterns by this Federated AI is transmitted to drones for city surveillance.
The architecture showed how imagination power of humans is handed over internet into a Fusion GAN, allowing further imagination. This is the essence of the idea of using FL with GAN Fusion. Thus Federated AI Imagination by a team has been designed. How to develop AI that predicts the future from a set of geo distributed robots/cameras? Is it possible for a GAN Fusion to find patterns of cause and effect? Is it possible to create an visualization of the future given past events across many parts of the city?. The challenge presented in Figure 7 is approached by the formula of innovatively repeating a geo distributed computation graph. This algorithm is listed in Table 4. By repeating the FL based GAN Fusion distributed computation graph at periodic intervals, this AI achieves the feat of picturing the future based on patterns observed in the past across different parts of the city.
The potential for Federated AI Imagination was explored in this work.
Key results are 1. Contributed a new flavor to FL research as per Table 5 2. Expanded the potential of Federated AI as per Table 6 3. Contributed a novel Federated Deep Learning approach for Collaborative Imagination for a team as per Figure 7. 4. Demonstrated feasibility of Federated Imagination with a working prototype. Figure 5 shows screenshots.
V. CONCLUSION AND FUTURE SCOPE
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