Journal of Small Business & Entrepreneurship Special Issue on Socio-economic and Policy Impacts of AI

#artificialintelligence

With the recent progress in artificial intelligence (AI) algorithms, dramatic increase in computational capacities, and availability of big data necessary for training deep neural networks, a lot of AI applications became available at the market and automation tendencies started to penetrate all spheres of human activities and all industries. While the topic of AI has been getting a lot of media coverage and public attention, profound research on its socio-economic and policy effects, especially with regard to entrepreneurship, has yet to be developed. Moreover, methodological papers in artificial intelligence field have been mainly published in very technical venues and it is difficult for a broader publics to grasp the most recent developments in this area. Therefore, the purpose of this special issue is to address these shortcomings. This special issue is the first initiative to interact the technical and methodological papers in AI with papers exploring socio-economic, entrepreneurship and policy effects of AI.


Beyond the Turing Test

AI Magazine

The articles in this special issue of AI Magazine include those that propose specific tests, and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI.


Beyond the Turing Test

AI Magazine

The articles in this special issue of AI Magazine include those that propose specific tests, and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI.


An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques

AI Magazine

The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.


Like it or not, artificial intelligence is here to stay

#artificialintelligence

Artificial intelligence (AI) is changing the way we live our lives; it is everywhere and here to stay. The concepts of Artificial intelligence started on the pages of science fiction, which introduced us to the notion of smart robots. With the invention of electronic digital computers in the early 1940s the pursuit of AI was made possible. The term itself was coined at a conference at Dartmouth in the summer of 1956, where scientists gathered to discuss ways to program computers to solve problems with the skills of a human. AI flourished for the next two decades and optimism was high that we would soon have machines with the general intelligence of an average human.