Over the last few years DARPA has adopted a perspective on the AI field's development that is descriptive and useful. The agency’s “three waves of AI technologies” neatly tags the dominant developments, which correlate well with the availability of computation and communications. The waves are covered in depth in this issue.


 
 
 This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.
 
 

Artificial intelligence, whether embodied as robots or Internet of Things, or disembodied as intelligent agents or decision‐support systems, can enrich the human experience. It will also fail and cause harms, including physical injury and financial loss as well as more subtle harms such as instantiating human bias or undermining individual dignity. These failures could have a disproportionate impact because strange, new, and unpredictable dangers may lead to public discomfort and rejection of artificial intelligence. Two possible approaches to mitigating these risks are the hard power of regulating artificial intelligence, to ensure it is safe, and the soft power of risk communication, which engages the public and builds trust. These approaches are complementary and both should be implemented as artificial intelligence becomes increasingly prevalent in daily life.

This article discusses a set of principles for accountability and transparency in AI as well as a set of antipatterns or harmful trends too often seen in deployed systems. It provides concrete suggestions for what can be done to shift the balance away from these antipatterns and toward more positive ones.

This editorial introduces several of the deployed applications that were described at the 2018 Innovative Applications of Artificial Intelligence conference, held in New Orleans, Louisiana, in February 2018.

Conversion rate optimization (CRO) means designing an e‐commerce web interface so that as many users as possible take a desired action such as registering for an account, requesting a contact, or making a purchase. Such design is usually done by hand, evaluating one change at a time through A/B testing, evaluating all combinations of two or three variables through multivariate testing, or evaluating multiple variables independently. Traditional CRO is thus limited to a small fraction of the design space only, and often misses important interactions between the design variables. This article describes Ascend by Evolv,1 an automatic CRO system that uses evolutionary search to discover effective web interfaces given a human‐designed search space. Design candidates are evaluated in parallel online with real users, making it possible to discover and use interactions between the design elements that are difficult to identify otherwise. A commercial product since September 2016, Ascend has been applied to numerous web interfaces across industries and search space sizes, with up to fourfold improvements over human design. Ascend can therefore be seen as massively multivariate CRO made possible by artificial intelligence.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

A recent study implies that research presented at top artificial intelligence conferences is not documented well enough for the research to be reproduced. My objective was to investigate whether the quality of the documentation is the same for industry and academic research or if differences actually exist. My hypothesis is that industry and academic research presented at top artificial intelligence conferences is equally well documented. A total of 325 International Joint Conferences on Artificial Intelligence and Association for the Advancement of Artificial Intelligence research papers reporting empirical studies have been surveyed. Of these, 268 were conducted by academia, 47 were collaborations, and 10 were conducted by the industry. A set of 16 variables, which specifies how well the research is documented, was reviewed for each paper and each variable was analyzed individually. Three reproducibility metrics were used for assessing the documentation quality of each paper. The findings indicate that academic research does score higher than industry and collaborations on all three reproducibility metrics. Academic research also scores highest on 15 out of the 16 surveyed variables. The result is statistically significant for 3 out of the 16 variables, but none of the reproducibility metrics. The conclusion is that the results are not statistically significant, but still indicate that my hypothesis probably should be refuted. This is surprising, as the conferences use double‐blind peer review and all research is judged according to the same standards.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

The workshop program of the Thirty‐Third AAAI Conference on Artificial Intelligence (AAAI‐19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27 and 28, 2019. There were 16 workshops in the program: Affective Content Analysis: Modeling Affect‐in‐Action; Agile Robotics for Industrial Automation Competition; Artificial Intelligence for Cyber Security; Artificial Intelligence Safety; Dialog System Technology Challenge; Engineering Dependable and Secure Machine Learning Systems; Games and Simulations for Artificial Intelligence; Health Intelligence; Knowledge Extraction from Games; Network Interpretability for Deep Learning; Plan, Activity, and Intent Recognition; Reasoning and Learning for Human‐Machine Dialogues; Reasoning for Complex Question Answering; Recommender Systems Meet Natural Language Processing; Reinforcement Learning in Games; and Reproducible AI. This report contains brief summaries of all the workshops that were held.