Technology has advanced tremendously in the last few years and is only going to continue to compound. If you've ever heard of Moore's Law, this is the idea that technology's complexity doubles every year. Just imagine what that means for the next few years. But those who keep up to date with new technological advancements will be rewarded with new tools to experiment, play, and, of course, earn more clients and revenue. One industry which has experienced a major tech boom is engineering and construction.

But sometimes we take a look back. This week, we ran an excerpt from a new book that did just that, by examining how one psychologist's tireless investigation into WWII plane crashes inadvertently created an entire new field of study, and one that contributed directly to everyone's favorite 20th century tech product: the Macintosh computer. We also wondered what would happen if the makers of this year's (good!) car film Ford v Ferrari had included a bit more gearhead porn. It's been a week: Let's get you caught up. In an interview, Uber CEO Dara Khosrowshahi likened Saudi Arabia's assasnation of Jamal Kashoggi to the decisions that lead to the death of an Arizona woman who was struck and killed by a testing self-driving Uber vehicle.

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The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the $\mbox{Tornado}$ framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our $\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.