In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performance of the ensemble on the underlying task, and maximizes performance of the explainable model on the observations allocated to it, subject to the maximal ensemble performance condition. The proposed method is shown to produce such explainability optimal allocations on a benchmark suite of tabular datasets across a variety of explainable and black box model types. These learned allocations are found to consistently maintain ensemble performance at very high explainability levels (explaining $74\%$ of observations on average), and in some cases even outperforming both the component explainable and black box models while improving explainability.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. For some people, the term "black box" brings to mind the recording devices in airplanes that are valuable for postmortem analyses if the unthinkable happens. For others it evokes small, minimally outfitted theaters. But black box is also an important term in the world of artificial intelligence. AI black boxes refer to AI systems with internal workings that are invisible to the user.

Explainable AI typically involves tools & techniques to understand how a complex model behaves, in a simple, straightforward and intuitive way so humans can understand it. It answers why an automated decision making tool resulted in a specific output that impacts customers, but doesn't explain how. It's predicted the explainable AI market size is estimated to reach $21.8 billion by 2030, up from $4.1 billion in 2021. And Gartner's crystal ball paints a picture that "by 2025, 30% of government and large enterprise contracts for the purchase of AI products and services will require the use of explainable and ethical AI." So, what's fueling predicted market growth? The accelerant for the explainable AI market is due in part to EU advent of GPDR's Article 13-15 and 22, which establishes rights specific to algorithmic decision making, including a right of both notification and access to meaningful information about the logic involved and the right of the significance of and envisioned effects of solely automated decision making.

Machine learning doesn't always have to be an abstruse technology. The multi-parameter and hyper-parameter methodology of complex deep neural networks, for example, is only one type of this cognitive computing manifestation. There are other machine learning varieties (and even some involving deep neural networks) in which the results of models, how they were determined, and which intricacies influenced them, are much more transparent. It all depends on how well organizations understand their data provenance. Comprehending just about everything that happened to training data for models, as well as that for the production data models encounter, is integral to explaining, refining, and improving their results.

After impressive successes in deterministic and fully-observable board games to significantly outclass humans, game playing research shifts towards non-deterministic and imperfect information card games, where humans are still persistently better. In this paper we devise a player that challenges human supremacy in Skat. We provide a complete player for playing selected variants of the game, with effective solutions for bidding and Skat putting, extracting knowledge from several million games. For trick play we combine expert rules with engineered tree exploration for optimal open card play. For dealing with uncertainty especially in Ouvert games we search the belief space.

Nikki Baird is Vice President of Retail Innovation at Aptos, a retail enterprise solution provider, as well co-founder of Retail Systems Research and a former analyst at Forrester Research. She discusses advancements in Artificial Intelligence that will help retailers ensure that AI isn't making bad assumptions under the adage "garbage in, garbage out" as well as the trend toward in-store personalization in Part 7 of EcommerceBytes Online Selling Trends 2019. Retail Breaks Out of the AI Black Box First-generation AI solutions were simple – data in, answer out. Solutions were designed to protect the average end user from confusion and distraction. While black box solutions serve their purpose, they also limit the value organizations can extrapolate by hiding AI logic, which in theory could be used to teach humans what was learned that led to various recommendations.

I stepped inside a small, dark room in a large, airy loft space in New York's Soho district early Wednesday morning. Our host fitted me with an HTC Vive and told to explore the world around me. Within moments, I was trapped in a glass box, surrounded by other people, also wearing VR headsets, also trapped in glass boxes, one of whom continued to claw at the glass until both of our headsets were consumed by our own flesh. We were one with the machines. Over the next two hours I watched semi-autonomous robots run in circles, randomly scribbling on large sheets of butcher paper; pulled the virtual puppet strings of a CGI llama that lip synced to Mariah Carey; watched as Reeps One, a world-famous dubstep beatboxer, created unique digital sculptures with the incredibly nuanced tones of his voice; and floated through a VR dreamscape using my breathing and brain waves to propel me upward. And all of this before I'd finished my first cup of coffee.