The crystal ball has long been a staple of mythology – knowing what the future holds has a compelling allure. However, while the crystal ball could predict the future, that future was simply one of many possible outcomes. What usually is missing from these hero's journeys is how the past (hindsight) informs us in the present (insight), how together they might be used to predict the future and, most importantly, what can be done to change the future for the better (foresight). The heroes are often left to figure this out on their own through trial and error, harrowing escapes, and help from unlikely sources. We don't want the same thing to happen with predictive analytics.
Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a...
Just as in the fictitious Lake Wobegon, where all the children are above average, in the DevOps software lifecycle, we shift every step in the lifecycle all the way'to the left.' The'shifting to the left' metaphor dates to the 1990s, as software development organizations realized that the industry-standard waterfall methodology led to poor quality software and expensive fixes. The problem: the testing step was far'to the right,' that is, late in the lifecycle. By moving it earlier, i.e., 'to the left,' software quality improved and any necessary bug fixes were far less expensive to address. Shifting testing to the left is now an established software best practice.
Before reading this, you should watch this video where Bryan Cantrill explains a value-conflict between Joyent and Node.js, I believe we have a similar problem. All these values are important - but they are in tension. In the end one has to choose between them. Perl's has traditionally prioritized certain values over these others, and in my experience these are: Expressiveness is probably the most obvious one.
Loihi is Intel's novel, manycore neuromorphic processor and is the first of its kind to feature a microcode-programmable learning engine that enables on-chip training of spiking neural networks (SNNs). The authors present the Loihi toolchain, which consists of an intuitive Python-based API for specifying SNNs, a compiler and runtime for building and executing SNNs on Loihi, and several target platforms (Loihi silicon, FPGA, and functional simulator). To showcase the toolchain, the authors describe how to build, train, and use a SNN to classify handwritten digits from the MNIST database.