The online interactive magazine of the Association for the Advancement of Artificial Intelligence
Looking Back, Looking Ahead: Humans, Ethics, and AI

Looking Back, Looking Ahead: Humans, Ethics, and AI

By Ashok Goel

Concerns about ethics of AI are older than AI itself. The phrase “artificial intelligence” was first used by McCarthy and colleagues in 1955 (McCarthy et al. 1955). However, in 1920 Capek already had published his science fiction play in which robots suffering abuse rebelled against human tyranny (Capek 1920), and by 1942, Asimov had proposed his famous three “laws of robotics” about robots not harming humans, not harming other robots, and not harming themselves (Asimov 1942). During much of the last century, when AI was mostly confined to research laboratories, concerns about ethics of AI were mostly limited to futurist writers of fiction and fantasy. In this century, as AI has begun to penetrate almost all aspects of life, worries about AI ethics have started permeating mainstream media. In this column, I briefly examine three broad classes of ethical concerns about AI, and then highlight another concern that has not yet received as much attention.

Looking Back, Looking Ahead: Symbolic versus Connectionist AI

Looking Back, Looking Ahead: Symbolic versus Connectionist AI

By Ashok Goel; School of Interactive Computing, Georgia Institute of Technology

Like much of the AI community, I have watched the ongoing discussion between symbolic AI and connectionist AI with fascination. While symbolic AI posits the use of knowledge in reasoning and learning as critical to producing intelligent behavior, connectionist AI postulates that learning of associations from data (with little or no prior knowledge) is crucial for understanding behavior. The recent debate between the two AI paradigms has been prompted by advances in connectionist AI since the turn of the century that have significant applications.

The Role of Open-Source Software in Artificial Intelligence

The Role of Open-Source Software in Artificial Intelligence

By Jim Spohrer

With this publication, we launch a new column for AI Magazine on the role of open-source software in artificial intelligence. As the column editor, I would like to extend my welcome and invite AI Magazine readers to send short articles for future columns, which may appear in the traditional print version of AI Magazine, or on the AI Magazine interactive site currently under development. This introductory column serves to highlight my interests in open-source software and to propose a few topics for future columns.

The Case Against Registered Reports

The Case Against Registered Reports

By Odd Erik Gundersen, Norwegian University of Science and Technology

Registered reports have been proposed as a way to move from eye-catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top-twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence?

Betting on Bets

Betting on Bets

Chris Welty, Google Research, USA
Praveen Paritosh, Google Research
Kurt Bollacker, LongNow Foundation

The AI bookies have spent a lot of time and energy collecting scientific bets from AI researchers since the birth of this column three years ago. While we have met with universal approval of the idea of scientific betting we have likewise met with nearly universal silence in our acquisition of bets. We have collected only a very few in this column over the past two years. In our first column we published the “will voice interfaces become the standard” bet, as well as a set of 10 predictions from Eric Horvitz that we proposed as bets awaiting challengers. No challengers have emerged.

Engagement During Pandemic Teaching

Engagement During Pandemic Teaching

By Michael Wollowski, Rose-Hulman Institute of Technology, USA

In this panel, AI faculty with experience teaching online and blended classes were asked to share their experiences teaching online classes. The panel was composed of Ashok Goel, Georgia Institute of Technology, Ansaf Salleb-Aouissi, Columbia University and Mehran Sahami, Stanford University. The panelists were asked to describe which tools and methods work well to help instructors engage and bond with students online. They were furthermore asked to share their insights into which components of a course can be done best online and which ones are best accomplished in person. The panel took place as part of the 2021 Symposium on Educational Advances of AI, which was collocated with AAAI-21. The panel was attended by about 55 people and it included a vigorous Q/A portion.