Explanatory inference is the creation and evaluation of hypotheses that provide explanations, and is sometimes known as abduction or abductive inference. Generative AI is a new set of artificial intelligence models based on novel algorithms for generating text, images, and sounds. This paper proposes a set of benchmarks for assessing the ability of AI programs to perform explanatory inference, and uses them to determine the extent to which ChatGPT, a leading generative AI model, is capable of making explanatory inferences. Tests on the benchmarks reveal that ChatGPT performs creative and evaluative inferences in many domains, although it is limited to verbal and visual modalities. Claims that ChatGPT and similar models are incapable of explanation, understanding, causal reasoning, meaning, and creativity are rebutted.

In the first phase of the analysis, I produced a conceptual reconstruc-Peter D. Karp In the next phase, I searched for patterns in the differences between successive My Ph.D. dissertation describes an A class knowledge base defines a tax-states of the biologists' knowledge. Patfocuses on a program of research in process knowledge base describes the terns in the differences indicate reamolecular biology that culminated in chemical reactions that can occur soning methods that were used to the discovery of a new mechanism of between the biological objects in this derive new theories from old ones. An experiment is described My analysis identified theory-modifiuation. In the first phase of my work, in a third knowledge base by creating cation operators that the biologists I performed a historical study of this the particular objects (instantiated used to modify their theories; these program of biological research in from the known classes of objects) operators form the core of the which I reconstructed the different that are present in the experiment. These patterns also supat different points in time and then called Gensim (genetics simulator) port the conjecture that scientists use analyzed the differences between predicts experimental outcomes by four different modes of scientific these successive theories.

Both tasks are concerned with the interpretation of large quantities of digitized signal data. The task of SU/X is to understand "continuous signal that is, signals which persist over time. The task of SU/P is to interpret protein x-ray crystallographic data. Some features of the design are: (1) incremental interpretation of data employing many different pattern-invoked sources of knowledge, (2) production rule representation of knowledge, includiR high level strategy knowledge, (3) "opportunistic" hypothesis formation using Dr,

Hypothesis Formation and Qualitative Reasoning in Molecular Biology

My Ph.D. dissertation describes an investigation of scientific reasoning from a computational perspective.

Research report RC 294, IBM, Yorktown Heights, New York