Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

Social Capital and Digital Divide: Implications for Mobile Health Policy in Developing Countries

Digital divide has been a major obstacle for mobile health services for the elderly in developing countries; to assess the potential solution to narrow digital divide among the elderly, we use data from the China Health and Retirement Longitudinal Study (CHARLS) and test for a causal role of social capital in digital access among elderly individuals in China. To handle endogenous problems associated with social capital, we introduce instrumental variable (IV) estimates in our models. Our data analysis shows that social capital facilitates increased digital access. We distinguish between two digital access patterns, an infrastructure pattern and a personal device pattern, and find that the causal effect of social capital is determined by the personal device pattern. Therefore, since family members and relatives increase digital access among elderly people, we propose a family-centered mobile health policy in developing countries

Can Yield More Economic Returns: An Empirical Study from mHealth Services

Given the role of congeniality in interpersonal communication, congenial people can experience better relationships, including friend relationships, roommate relationships, marriage relationships, and business cooperation. However, in the medical field, it is worth further exploring whether congeniality can have similar benefits. From the perspective of similarity interpersonal attraction theory, this study explores the influence of doctor-patient personality similarity on doctors’ economic returns, as well as the moderating effects of doctors’ titles and patients’ disease types. This study takes mHealth services as the research context, extracts the personality traits of both doctors and patients from the doctor-patient interaction text. The results show that (1) doctor-patient personality similarity has a positive impact on the doctor’s economic return; (2) the doctor’s title and the patient’s disease type play a important role in moderating effect on the relationship between doctor-patient personality similarity and the doctor’s economic return. The results of this study verify the feasibility of extracting personality traits from doctor-patient interaction text and enrich the application of similarity interpersonal attraction theory in mHealth services