It's time for another edition of "What TNW is reading." If you're not familiar with them, take a moment and enjoy the previous edition about the US election. After having first been colonized by the Spanish, the country – in its own weird way – is part of the Kingdom of the Netherlands and one of the busiest ports in the Northern Hemisphere. One of its four official languages is English and they managed to smush them all together to create Papiamentu, the local language. Speaking all four languages fluently, Cecil is currently adding French and Russian to the mix… he's such an?????????.

We start this four-part whitepaper series with Tableau. Tableau is business intelligence software that helps people see and understand their data. You can add scientific articles relevant to data science, IoT, machine learning, deep learning, big data, dataviz, stats, operations research, AI, machine-to-machine communications, scientific programming and related topics in the comment section below.

We started with the development of Leaf briefly before Google released Tensorflow. For two weeks Leaf's hypothesis seemed unique. Although Leaf has top-notch performance and an uniquely simple yet expressive API, it will lose against Tensorflow. Leaf's current theoretical benefits[1] are less significant, than the benefits that Tensorflow provides[2] over the early, scientific frameworks like Torch, Caffe, Theano. The next generation of tools, that help developers to build machine learning applications will build on Tensorflow, or more specifically on higher-level frameworks, like Keras, who abstract over multiple AI Engines[3].

Farms of 3D printers are a potential solution to batch production, with some companies relying on numerous machines to 3D print a large group of parts at once. While they may increase output, print farms may also be labor intensive to operate. To demonstrate the overall utility of its cloud robotics platform, an Oregon startup called Tend.ai has deployed a robotic arm, artificial intelligence and cloud computing to create an automated print farm. Tend.ai has built what it suggests is a universal machine automation solution that combines a number of components into a cloud robotics platform. While the specific make and model of each piece of hardware is left up to the user, all one needs to get started with Tend.ai is a robotic arm, a webcam and a robotic gripper.

Additionally, the fraud detection and prevention programs that keep your bank account safe are also utilizing predictive analytics. The most sophisticated of these programs analyze your spending habits and compare each purchase against them. The purchases themselves also have fraudulence probability scores (a 1,000 online purchase paid to a company based in Timbuktu is more likely to be fraudulent than a 200 purchase at your local grocery store). If anything seems fishy, the bank sends you an alert to ensure your funds aren't compromised if fraud is, in fact, taking place.

After years of touting itself as the company that didn't access its users data, Apple has finally acknowledged that it really does need at least some collective understanding. The company announced at last week's developer conference that, starting with iOS 10, it will collect a range of new information as it seeks to make Siri and the iPhone better at predicting the information its owner might want at a given time. Apple is looking to thread a fine needle, gaining access to the data it needs to make its servers smarter while also protecting user privacy. It's doing so by employing a concept known as differential privacy. However, the company was initially short on details on just what data it will be collecting and how.

I'm sure I'm not going be the first or last to make the association between the software name "Quixote" and the legendary story of The Ingenious Gentleman Don Quixote of La Mancha by Miguel de Cervantes Saavedra. The main character of the story is first driven by his wild fantasies that originates from all the romantic stories he read. Essentially, the character has no connection to reality and sets out on a journey where the final result is death, not just of the character, but also a metaphorical death of chivalry. In an ONR release, Marc Steinberg, the program manager says "For years, researchers have debated how to teach robots to act in ways that are appropriate, non-intrusive, and trustworthy," There-in lies the rub. "One important question is how to explain complex concepts such as policies, values, or ethics to robots. Humans are really good at using narrative stories to make sense of the world and communicate to other people. This could one day be an effective way to interact with robots."

Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.

The AI analyzes student answers, and groups them based on similarity.

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. The pioneers of artificial intelligence were surely off on the timing, but they weren't wrong; AI is coming.