This paper elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, and discovering potential cooperators etc. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard working. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.

Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, its applicability to large-scale prediction tasks on complex biomedical knowledge bases is limited by long inference times and difficulties with aggregating predictions made by multiple rules. We improve upon AnyBURL by introducing the SAFRAN rule application framework which aggregates rules through a scalable clustering algorithm. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark OpenBioLink. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and narrows the gap between rule-based and embedding-based algorithms on OpenBioLink. We also show that SAFRAN increases inference speeds by up to two orders of magnitude.

From anywhere and with just a mobile phone, anyone can become an air traffic controller, or at least a virtual air traffic controller. One can follow the world traffic flow of airplanes live and find out where an aircraft is coming from and where it is headed. One just has to take advantage of the millions of pieces of data that fly across the Internet. This is the magic power of Big Data. Artificial intelligence then enters the picture to find patterns and give meaning to the massive and heterogeneous information stream.

We are victims of one common superstitionthe superstition that we understand the changes that are daily taking place in the world because we read about them and know what they are. The anthropological stories and the concept of memes were brought to my attention several years ago by Lynn Conway Much of the vision and some of the material was drawn from a paper that we worked on together but never published. The important distinction between process and product, was made crisp for me by John Seely Brown, who also has encouraged and made possible projects like Trillium, which I watched with interest, and like Colab, in which I participated. Joshua Lederberg kindled my interest in biological issues and a respect for knowledge processes and their partial automation that has not faded Dan Bobrow listened to my ramblings on several runs, agonized over my confusions, helped to get the kinks out of the arguments, and suggested the title for the article Sanjay Mittal and I have spent many hours speculating together on the issues in building community knowledge bases and knowledge servers and in understanding the principles of knowledge competitions Austin Henderson helped me to understand the Trillium story and to report it accurately. Austin and Sanjay hounded me to say, more precisely, what a knowledge medium is Agustin Araya and Mark Miller participated in a Colab session in which we tried to jointly lay out these ideas, and together asked me to make the prescriptions clearer Ed Feigenbaum persuaded me to be more precise in the discussion of the limits of today's expert systems technology Thanks to Agustin Araya, Dan Bobrow, John Seely Brown, Lynn Conway, Bob Engelmore, Ed Feigenbaum, Felix Frayman, Gregg Foster, Austin Henderson, Ken Kahn, Mark Miller, Sanjay Mittal, Julian Orr, Allen Sears, Lucy Suchman, and Paul Wallich for reading early drafts of this paper and for helping to clarify the ideas and improve the article's readability Stephen Cross triggered the writing of this article when he invited me to give the keynote address at the Aerospace Applications of Artificial Intelligence Conference in Dayton, Ohio, in September 1985.

Gene-expression microarrays, commonly called gene chips, make it possible to simultaneously measure the rate at which a cell or tissue is expressing--translating into a protein--each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells; identify novel targets for drug design; and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. This article describes microarray technology, the data it produces, and the types of machine learning tasks that naturally arise with these data.

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamic devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems that include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks.

Molecular biology is emerging as an important domain for artificial intelligence research. The advantages of biology for design and testing of AI systems include large amounts of available online data, significant (but incomplete) background knowledge, a wide variety of problems commensurate with AI technologies, clear standards of success, cooperative domain experts, nonmilitary basic research support and percieved potential for practical (and profitable) applications. These considerations have motivated a growing group of researchers to pursue both basic and applied AI work in the domain. More than seventy-five researchers working on these problems gathered at Stanford for a AAAI sponsored symposium on the topic. This article provides a description of much of the work presented at the meeting, and fills in the basic biology background necessary to place it in context.

One of the fastest advancing areas of modern science is functional genomics. This science seeks to understand how the complete complement of molecular components of living organisms (nucleic acid, protein, small molecules, and so on) interact together to form living organisms. Functional genomics is of interest to AI because the relationship between machines and living organisms is central to AI and because the field is an instructive and fun domain to apply and sharpen AI tools and ideas, requiring complex knowledge representation, reasoning, learning, and so on. This article describes two machine learning (inductive logic programming [ILP])-based approaches to the bioinformatic problem of predicting protein function from amino acid sequence. The first approach is based on using ILP as a way of bootstrapping from conventional sequence-based homology methods.

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high. I have therefore taken the liberty of dividing the last 30 years of medical AI research into three eras: the era of diagnosis, the era of managed care, and the era of molecular medicine. A description of these eras allows me to review for you some of the early and current work in AIM and then tell you about some of the exciting opportunities now emerging. Why is AI in medicine even worth considering? In the late 1950s, medicine was already drawing the attention of computer scientists principally because it contains so many stereotypical reasoning tasks. At the same time, these tasks are fairly structured and so are ...

The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to fix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically.