Unifying Consciousness and Time to Enhance Artificial Intelligence

Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving fast in many fields, including metaphysics and fundamental physics. Reality composes patterns in human Consciousness in response to the regularities in nature. These regularities could be physical (e.g., astronomical, environmental), biological, chemical, mental, social, etc. The patterns that emerged in Consciousness were correlated to the environment, life and social behaviours followed by constructed frameworks, systems and structures. The complex constructs evolved as cultures, customs, norms and values, which created a diverse society. In the evolution of responsible AI, it is important to be attuned to the evolved cultural, ethical and moral values through Consciousness. This requires the advocated design of self-learning AI aware of time perception and human ethics.Comment: This discussion paper has been submitted to Cognitive Neuroscience of Routledge, part of the Taylor & Francis publication

Acceleration Techniques for Sparse Recovery Based Plane-wave Decomposition of a Sound Field

Plane-wave decomposition by sparse recovery is a reliable and accurate technique for plane-wave decomposition which can be used for source localization, beamforming, etc. In this work, we introduce techniques to accelerate the plane-wave decomposition by sparse recovery. The method consists of two main algorithms which are spherical Fourier transformation (SFT) and sparse recovery. Comparing the two algorithms, the sparse recovery is the most computationally intensive. We implement the SFT on an FPGA and the sparse recovery on a multithreaded computing platform. Then the multithreaded computing platform could be fully utilized for the sparse recovery. On the other hand, implementing the SFT on an FPGA helps to flexibly integrate the microphones and improve the portability of the microphone array. For implementing the SFT on an FPGA, we develop a scalable FPGA design model that enables the quick design of the SFT architecture on FPGAs. The model considers the number of microphones, the number of SFT channels and the cost of the FPGA and provides the design of a resource optimized and cost-effective FPGA architecture as the output. Then we investigate the performance of the sparse recovery algorithm executed on various multithreaded computing platforms (i.e., chip-multiprocessor, multiprocessor, GPU, manycore). Finally, we investigate the influence of modifying the dictionary size on the computational performance and the accuracy of the sparse recovery algorithms. We introduce novel sparse-recovery techniques which use non-uniform dictionaries to improve the performance of the sparse recovery on a parallel architecture

Consciousness Studies

This paper was submitted to the Australian government consultation on the 2022 List of critical technologies in the national interest.  For more information, visit: https://consult.industry.gov.au/critical-technologies-2022</p

Keeping AI honest

Dr Mahendra Samarawickrama GAICD details a unified AI governance framework for AI ethics, ESG and sustainability.</p