Related papers: Introduction to discoverology
Recently, there has been increased attention to improving laboratory instruction at all levels. At the introductory level, research results have shown differing levels of success based on the nature of the desired learning outcomes. In…
The combination of diverse, pre-existing knowledge is a common explanation for scientific breakthroughs. However, a paradox exists: while scientific output and the potential for such recombination have grown exponentially, the rate of…
Abductive reasoning starts from some observations and aims at finding the most plausible explanation for these observations. To perform abduction, humans often make use of temporal and causal inferences, and knowledge about how some…
Recent years have seen the dramatic rise of the usage of AI algorithms in pure mathematics and fundamental sciences such as theoretical physics. This is perhaps counter-intuitive since mathematical sciences require the rigorous definitions,…
Research investigating the origins of life usually focuses on exploring possible life-bearing chemistries in the pre-biotic Earth, or else on synthetic approaches. Little work has been done exploring fundamental issues concerning the…
Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor…
It has been proposed that, since the origin of life and the ensuing evolution of biological species, a second evolutionary process has appeared on our planet. It is the evolution of culture-e.g., ideas, beliefs, and artifacts. Does culture…
Explorers have made breakthroughs in many fields of astrophysics. The science from both these missions contributed to three Nobel Prizes - Giacconi (2002), Mather, and Smoot (2006). Explorers have: marked the definitive beginning of…
This paper examines some methods and ideas underlying the author's successful probabilistic learning systems(PLS), which have proven uniquely effective and efficient in generalization learning or induction. While the emerging principles are…
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards. To address this, intrinsic rewards can be generated using forward…
Prompting is the primary method by which we study and control large language models. It is also one of the most powerful: nearly every major capability attributed to LLMs-few-shot learning, chain-of-thought, constitutional AI-was first…
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own…
Over the last few decades, the nature of scientific research has changed in response to external influences. Firstly, powerful networked computers have become a standard tool. Secondly, society presses ever harder for research to deliver…
Design has the potential to cultivate hope in the face of complex societal challenges. These challenges are often addressed through efforts aimed at harm reduction and prevention -- essential but sometimes limiting approaches that can…
Large language models have the potential to generate explanations for their own predictions in a variety of styles based on user instructions. Recent research has examined whether these self-explanations faithfully reflect the models'…
Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge…
Experimental activities are an essential part of physics education. In addition to conveying scientific knowledge, they play a significant role in developing scientific literacy, inquiry skills, and critical thinking. In today's world,…
Turing patterns are a cornerstone of biological self-organization, yet their emergence typically requires finely tuned parameters occupying narrow regions of high-dimensional space. This poses a fundamental challenge: how can evolving…
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective…
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI…