There is a significant amount of interest in the field of big data and machine learning right now. This has been driven by use of sophisticated learning algorithms along with large datasets and powerful computing hardware to achieve extraordinary success in narrow tasks. Such success has been classified as narrow artificial intelligence (AI), in order to distinguish it from general intelligence, which continues to be the holy grail of computing. If we are to progress from narrow to general AI, it is important to have a better understanding of what intelligence is and what it entails. As we seek to reboot computing across the stack, this is an important question to address, to help us identify the optimal devices, architectures and design techniques that will allow us to build the intelligent systems of the future. In this paper, I will review the fundamental ideas and assumptions that have allowed us to achieve computing in artificial systems over the years. Building off these ideas, I will discuss the important distinction between a good example of a system with general intelligence i.e. ourselves, and the intelligence achieved through our current computational approaches. Following this, I will use recent results to explore a new framework - a physically grounded theory of thermodynamic intelligence, and discuss the design paradigm that seeks to achieve such intelligence in systems.
展开▼
