Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.

Towards an unanimous international regulatory body for responsible use of Artificial Intelligence [UIRB-AI]

Artificial Intelligence (AI), is once again in the phase of drastic advancements. Unarguably, the technology itself can revolutionize the way we live our everyday life. But the exponential growth of technology poses a daunting task for policy researchers and law makers in making amendments to the existing norms. In addition, not everyone in the society is studying the potential socio-economic intricacies and cultural drifts that AI can bring about. It is prudence to reflect from our historical past to propel the development of technology in the right direction. To benefit the society of the present and future, I scientifically explore the societal impact of AI. While there are many public and private partnerships working on similar aspects, here I describe the necessity for an Unanimous International Regulatory Body for all applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such an organization. To combat any drawbacks in the formation of an UIRB-AI, both idealistic and pragmatic perspectives are discussed alternatively. The paper further advances the discussion by proposing novel policies on how such organization should be structured and how it can bring about a win-win situation for everyone in the society.