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.

Because this paper is about computer programs thal generate explanations, my debt to Prof. Hempel will be obvious. However, insofar as I wish to use the term'discovery' to cover the activity of finding explanations, I know that Prof. Hempel will not entirely agree with these ideas about mechanizing the activity. The purpose of this paper is to elaborate a very simple idea: that discovery in science and medicine can be profitably viewed as systematic exclusion of hypotheses. That is, hypotheses that explain empirical data can be found systematically by methods that can be implemented in computer programs. The conditions under which this view makes sense are an important part of the elaboration. Two necessary conditions are that the space of relevant hypotheses is definable and that there exist criteria of rejection and acceptability. Because the space of hypotheses is immense for most interesting problems, it is also desirable that there exist criteria for guiding a systematic search.

Human scientists actively seek out information that bears on questions they have decided to pursue. They design experiments, explore the implications of the knowledge they have, refine their questions and test alternative ideas. Although many discoveries are the result of unexpected observations, these surprises take place in the context of an explicit pursuit of knowledge. Viewing scientific discovery as a kind of motivated action raises some basic issues common to goal-directed behavior generally: Where do desires (to know) come from? What are the actions that can be taken (to discover)? What are the resources those actions consume, and how are they allocated? How are decisions about selecting and combining actions made?