Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

Abstract

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

Thoughtful artificial intelligence: Forging a new partnership for data science and scientific discovery

“…This is not meant to be a comprehensive survey, but rather a sample of pioneering work on artificial intelligence for scientific discovery for readers unfamiliar with this literature. For more detailed overviews see [16,18,21,23,36]. The goal here is to highlight two important things.…”

Data Science and symbolic AI: Synergies, challenges and opportunities

“…Inspired by progress in Data Science and statistical methods in AI, Kitano [37] proposed a new Grand Challenge for AI "to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize". Before we can solve this challenge, we should be able to design an algorithm that can identify the principle of inertia, given unlimited data about moving objects and their trajectory over time and all the knowledge Galileo had about mathematics and physics in the 17th century.…”

Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems

“…Using the convolution as commonality (27), we define the maximum commonality order O : X → N as follows:…”

“…While inter-subjective objectivity is a conceptual framework that classifies the quality of observation, the buoy, anchor, and raft refer to actual constructs of databases implemented with ICT. The terms arose from the developmental process of management systems in open systems science [5], sharing the perspective with the transversal question of the grand challenge of AI research regarding the effective extraction of scientific knowledge out of heterogenous data of varying quality [27]. Without properly positioning the subjective background of the study, it is often the case that established knowledge with large-scale experiments and statistical analyses is revealed to be false in high-throughput discovery-oriented research, resulting in a null-field with statistically prevailing bias [28].…”